Category: Augmented Analytics

At a time when businesses are looking at adopting Artificial Intelligence (AI) not just for competitive advantage but even for mere survival, it is increasingly challenging to build a successful AI practice with acute skills shortage for data scientists. On the other hand, Machine Learning (ML), which is built for its application involving laborious tasks such as cleaning data, preparing data and training ML algorithms, validation etc. However, there is continuous effort to automate these tasks by built more intelligent ML procedures and algorithms. AutoML , as we call it, can democratize ML by allowing even business users to develop and execute their own data models with little to no training on data science. Other than bridging the skills gap, automation in ML processes can also eliminate data biases, a major concern today, and reduce human errors while improving overall efficiency. Moreover, AutoML would allow domain experts and technical experts like data scientists, ensuring continued focus on business value.

The need for AutoML – Challenges with traditional ML processes

The growing interest in AI and ML means that there is a crippling shortage of data scientists. There were over 2.7 million open positions for data science and analytics jobs, according to a report by the Business-Higher Education Forum.As per the US Bureau of Labor Statistics, the number of jobs in the data science field will grow by 26 percent through 2026, adding nearly 11.5 million new jobs.

However, demand vastly outpaces supply for data scientists given how challenging it had been for several decades to work in this domain. It is impossible to generate hundreds of thousands of new data scientists in an instant, making it tough for organizations to implement their data science plans.Lack of these skillsets is one of the biggest reasons holding back thousands of companies from starting their AI journey. That said, automation is rapidly trying to solve this problem by making data science more accessible to even those without years of data science experience or even a degree in the subject.

Even so, lack of required skills is not the only challenge that organizations looking at machine learning face today. Even if an organization has the right skills, it may still be highly under-utilized because of the sheer amount of time that it takes just to clean the data. Data scientists spend as much as two-thirds of their time just cleaning the data. Just imagine if this is automated, what kind of fillip it will provide to the domain.

Further, data scientists often don’t come with domain and business expertise. However, even if bring domain and business understanding they end up focusing most of their time ingesting and processing data in order to make the models relevant. As a result specific business context often go amiss, leading to unsuccessful adoption of AI/ML.

Traditional ML processes are also highly dependent on human expertise, given the amount of customization that each ML model requires for the specific problem on hand. This makes the entire process inherently time-consuming. To build a new ML model, you still have to through the rigours of data preparation, feature engineering, training the model, evaluation and selection.

Biases in AI and ML models are also a major subject of debate today. Biases often creep in because of manual interventions and the inability of humans to analyze massive data sets for possible biases. The complexity of ML models currently has turned them into black boxes with very little visibility into what goes inside and what is impacting the final results.It is therefore vital to automate the process of machine learning to get better visibility into the models, eliminate all biases, and improve the overall efficiencies.

What is AutoML?

While machine learning continues to evolve, Automated Machine Learning (AutoML) goes beyond automation to accelerate the process of building ML and deep learning models. It automates several aspects of the ML processes, including the identification of the best performing algorithm from the available universe of features, algorithms and hyperparameters.

How Does AutoML Help?

By eliminating repetitive tasks, such as data cleaning, AutoML frees up the highly valued human resources to move towards value-adding analysis and more in-depth evaluation of the best-performing models. This allows enterprises to significantly cut down the time-to-market for the products and solutions built on these ML models.It:

• Eliminates repetitive tasks
• Allows enterprises to bring down time-to-market
• Guided analytics capabilities allow to eradicates biases
• Enables organizations to leverage their existing components
• Inspires trust by providing transparency on how the model functions
• Eliminates human error

However, complete automation also has its own set of challenges. Tesla founder Elon Musk famously said “AI is far more dangerous than nukes.” Apart from Musk, technology leaders like Bill Gates and Steve Wozniak have expressed concern about the dangerous aspect of AI. For instance, anyone with malicious intent can program AI systems to carry out mass destruction. Any powerful technology can be misused and AI is no different. The truth is that as long as AI systems continue to be Black Boxes, it will continue to remain a threat.

Some new age solutions are changing that equation by bringing in transparency and making it easier for users to interact better with AI systems. HyperSense AI Studio , for example, is built with guided analytics capabilities, which is a combination of automated ML and interactive ML. This allows usersto develop applications with a combination of automation and human interaction at any stage of the data science cycle based on task and business user requirements. The solution also generates alerts and gives recommendations to users as they are creating a pipeline.

The process eliminates biases that might have crept in and ensures that the system is not seen as a Black Box by providing details of how it functions and arrives at the results.

Through AutoML, the user can easily automate tasks like data pre-processing, feature engineering and hyper-parameter tuning. Moreover, it allows reusing features instead of rebuilding again from scratch for different models driving AI at scale.

What’s trending?

Several Machine Learning processes do not require any human intervention, allowing domain experts to work on building AI models instead of depending solely on the data scientists.

Data scientists, however, do not have to be a rare commodity anymore. Just how the power of a mobile phone camera made citizen journalism possible, the power of AutoML is now creating citizen data scientists . This new breed of professionals will now be able to build their own AI models without any formal education in Machine Learning or AI. Anyone familiar with the usage of Excel and interest in data analysis can potentially become a citizen data scientist.

The role of citizen data scientists will be critical in the growth of AI. In order to scale AI, one needs a massive number of data scientists. Moreover, citizen data scientists don’t just fill the skills gap. The biggest mismatch in ML initiatives is that ML projects are often associated with a lack of domain expertise. Data scientists are great at working on data, but they don’t necessarily come with a good understanding of your business or industry. Connecting the roles of domain expertise and data expertise has been a massive challenge for several firms.

However, by putting the ability to build a data model into the hands of a business user, AI projects can move towards newer dimensions that can only be perceived by a business domain expert.

What are the benefits of AutoML?

Other than democratizing machine learning, AutoML also has several other advantages. Automating the machine learning processes, for example, can tremendously accelerate the speed of training multiple models while also improving accuracy. In addition, AutoML eliminates biases in datasets by limiting human intervention and automating most of the processes in the ML pipeline. The reduced human intervention also cuts down on human errors in the process.

Automation also makes ML more scalable by enabling multiple ML models to be trained simultaneously, and in doing so, it also optimizes the overall ML processes to a great extent.

HyperSense AI Studio is an excellent example of AutoML platform . The platform enables enterprises to build and operationalize AI successfully using automated machine learning. It increases the efficiency of data scientists allowing them to focus on higher-value tasks. It automates every step of the data science lifecycle including, feature engineering, algorithm selection, and hyper-parameter tuning.

By leveraging HyperSense AI Studio , data scientists and domain experts can easily build ML models with higher scale, productivity, and efficiency while sustaining the model quality. By automating large part of the ML processes, the platform accelerates the time to get production-ready models with greater ease and efficiency. It also reduces human errors mainly because of manual measures in ML models.

It also makes data science accessible to all, enabling both trained and non-trained resources to rapidly build accurate and robust models, thus fostering a decentralized process. Further, it enhances collaboration between domain and technical experts which encourages the focus to remain on business value and not on technical part of the implementation. This helps in bringing down silos and promotes collaboration in other areas as well.

The quality of the machine learning model is not only based on code but also on the features used for running the model. Around 80% of data scientists’ time goes into creating, training, and testing data. HyperSense AI Studio comes built-in with a feature store that allows features to be registered, discovered, and used as a part of an ML pipeline. It allows reusing features instead of rebuilding again from scratch for different models driving AI at scale.

Key Takeaway

AI projects for long have been stuck at pilot stages due to several challenges that include lack of data scientists, slow progress in ML processes and even lack of coordination between business and data teams.According to a Gartner study, about 75 percent of organizations will shift from piloting to operationalizing AI by the end of 2024. Also, 50 percent of enterprises will devise AI orchestration platforms to operationalize AI. This, however, wouldn’t be possible without leveraging AutoML .

AutoML has the potential of democratizing AI and Machine Learning and finally take AI projects from mere pilots to scaled deployments. AutoML platforms like HyperSense AI Studio increases the efficiency of data scientists by allowing them to focus on higher-value tasks. The platform automates every step of the data science lifecycle including, feature engineering, algorithm selection, and hyper-parameter tuning, ensuring enhanced operational efficiency. In addition, it comes built-in with a feature store that allows features to be registered, discovered, and used as a part of an ML pipeline and even allows reusing features instead of rebuilding again from scratch for different models driving AI at scale.

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Introduction:
According to Gartner, 85 percent of all Artificial Intelligence (AI) projects tend to fail and the trend is expected to run well through 2022. What are the key reasons for this high failure rate in AI projects? There are three key ones:

1. Model deployment is not an easy tasks; it requires diverse expertise from software engineering, to machine learnings engineering along with data scientists
2. Model performances or effectiveness deteriorate on real-world applications
3. Models designed without collaboration between domain experts and engineers are unlikely to deliver the desired results

However, organizations can flip the equation by adopting Machine Learning Operations or MLOps, which allows organizations to redefine a process of putting model into productions, helps break from the shackles of siloes and allows different teams to collaborate in real-time with a goal to serve model for business and help achieve ROI. MLOps also ensures that the ML models created through the process are scalable and can be redeployed to solving other problems.

What is MLOps?

Before DevOps, developers were spending hours and hours working on code that may never go into production. As a result, DevOps got its footing in the tech industry over a decade ago as a means to bring the development teams and the IT teams together and make these somewhat different communities collaborate in a frictionless manner. Before DevOps, developers were spending hours and hours working on code that may never go into production.However, by being able to collaborate with the IT teams, nearly all DevOps teams today are convinced about their code even before it goes into production.

As AI and ML started to grow, they faced similar challenges that developers faced in the pre-DevOps era—getting stuck trying to take an AI project from ideation to production stage. And so, came Machine Learning Operations (MLOps). Modelled on the principles of DevOps, MLOps brings together people, processes, and practices by allowing collaboration between data, development and production teams.

Underpinning the idea of MLOps are technologies that automate the deployment, monitoring, and management of machine learning models. MLOps, in fact, goes a step ahead and ensures that the code that goes into production is scalable and provides a measurable business while having a strong governance framework at the same time.
What are the key components of MLOps?
MLOps acts as a guiding principles for data scientists, engineers and operations professionals to collaborate and help manage the production ML lifecycle. MLOps leverages automation to improve the quality of production ML with a constant eye on business goals.

Broadly, there are three key phases of any MLOps process—Designing the ML-powered application, ML Experimentation and Development, and finally, ML Operations.The design phase for the ML-powered application begins with understanding the business and the available data. Next, potential users need to be identified in this stage, and then an ML solution is designed to solve their problems while also looking at the possibilities of scaling the application to other areas. Typically, this phase looks at either enhancing user productivity or increasing the interactivity of the ML application.

The design phase also clearly defines the ML use-cases and prioritizes them. The available data is inspected and used to train the ML model. The requirements gathered from this exercise are then used to design the architecture of the ML application, establish the serving strategy, and create a test suite for the future ML model.

In the next phase of MLOps, it is vital to verify the applicability of ML for the identified problems through the deployment of an ML Model Proof-of-Concept. This phase is run iteratively to identify or polish the suitable ML algorithm for the given situation, data engineering, and model engineering. The idea is to build a stable quality ML model thatcan be runin production.

The last and final phase of operations aims to deliver the previously developed ML model in production by using established DevOps practices such as testing, versioning, continuous delivery, and monitoring.

The three phases are highly interconnected while also influencing each other. Each of these phases contributes key elements that work to close the ML lifecycle loop within an organization.
What are the benefits of MLOps?
MLOps can be highly beneficial for CXOs, data scientists, and data engineers alike. Let’s take for example on how MLOps can benefit CXOs. C-suite leaders require fast, accurate, and unbiased predictions. They are also looking for an AI solution that can provide them with a clear return on investment. That has been challenging for years but MLOps changes that forever by making it simple to highlight ROI on AI investments. By putting MLOps in place, CXOs can therefore utilize their energies into scaling AI capabilities throughout the organization while focusing on tracking KPIs that matter to each team and department.

Data scientists can similarly gain immense benefits from MLOps as it automates several parts of their daily lives while also allowing them to effectively collaborate with their operations counterparts. MLOps also eases out data scientists and ML engineers’ efforts by offloading much of the burden of day to day model management. This allows them to focus on the larger problems such as identifying new use cases, managing feature discovery, and developing more in-depth business expertise. A large part of a data scientist’s time goes into maintaining models or reviewing their performance manually. All of that gets automated with MLOps and frees up valuable resources.

For DevOps and data engineers, MLOps offers a way to manage their actual machine learning models in a single pane—right from testing and validation to updates and performance metrics. This enables the organization to scale ML deployment over a period of time to meet latency, throughput, and reliability SLAs, thereby generating more value from it.

How to implement MLOps?

Even before one thinks of implementing MLOps, it is important to start with a clear business goal or objective. These objectives need to be fleshed out with target performance measures, technical requirements, budget for the project, and KPIs that drive the process of monitoring the deployed models.

Once that’s in place, MLOps can be implemented in three different ways, depending on the organization’s maturity level in terms of the understanding of MLOps. The three types of implementation include manual process, ML pipeline automation, and CI/CD pipeline automation. These are also commonly referred to as the three levels of MLOps—MLOps level 0 (manual process), MLOps level 1 (pipeline automation), and MLOps level 2 (CI/CD pipeline automation).

Typically while starting their journey with ML, organizations begin with the manual ML workflow. In this type of deployment, every step of the journey is manual, including data analysis, data preparation, model training and even validation. In this type of implementation, data scientists work on the ML model and hand it over after training it to the engineering team to deploy on their API infrastructure.

This type of deployment is suitable when the assumption is that your data science team manages a few models that don’t change frequently. And since there are no frequent changes, there is no need for Continuous Integration and Continuous Deployment.

The second type of implementation is ML pipeline automation. This type of implementation goes a step ahead of the manual process and automates the ML pipeline to perform continuous training of the ML model. This type of implementation is suitable for solutions that operate in a constantly changing environment and need to proactively address shifts in indicators such as customer sentiment, market prices etc. While in MLOps level 0, the trained model is deployed as a prediction service to production, in level 1, an entire training pipeline is deployed that automatically and iteratively runs to serve the trained model as the prediction service.

However, this model is still not suitable for new ML idea, rather only new models based on new data. Moreover, it is not ideal for environments where you need to manage multiple ML pipelines in production.

To overcome the limitations of MLOps level 1, MLOps level 2 takes things up a notch and fits well with tech-driven companies that continuously retrain their ML models on a daily basis and redeploy the code on thousands of servers simultaneously.

The automated CI/CD pipeline, data scientists can spend more time on high-value items such as feature engineering, model architecture and hyperparameters. The output of MLOps level 2 is a deployed model prediction service.

The challenges to implementing MLOps

In 2013, IBM partnered with The University of Texas MD Anderson Cancer Center to build Watson for Oncology with an aim to eradicate cancer. Five years down the line, the project was shelved as it started giving erroneous treatment advice. Later it is found that the ML model was trained not on real patient data but rather on a small number of hypothetic patients.

Mistakes like these are pretty common in the ML domain. A typical ML lifecycle involves the identification of a business problem, establishing the success criteria, and then delivering an ML model to production. The delivery part happens in multiple steps, and each of these steps can either be performed manually or through an automatic pipeline.

While it may sound prudent to focus on solving the business problem, it is easy to lose focus on the complexities in managing the entire ML process. ML is a highly iterative process, and data scientists end up spending a lot of time in these iterations. Forcing models into production after the first or the second iteration can quickly turn into a failed deployment.

Data scientists need to not only deal with short response times but also support a large number of users. Moreover, working with thousands of code lines bring along their own set of difficulties to manage. Therefore, while data scientists were previously only required to produce an ML model, today, the first step is bringing ML models to production.

Lack of synergies between data science and operations teams sometimes also becomes a big challenge for organizations. Often the data science teams don’t have enough process understanding, and operations teams end up overestimating their understanding of ML processes, leading to disastrous outcomes.

MLOps requires dedicated people and resources to succeed. CXOs need to understand that MLOps is an iterative process and requires significant advance planning. The process cannot be taken casually, and companies need to be prepared for various contingencies.

Lately, there are plathero of tools, frameworks and platforms available in the market to bring together highly disparate space of “model production management” into the center of AI ecosystem. These tools and frameworks are primarily focused technical engineers to centralize the orchestration of model production using principles of MLops.

At the same time, while AI is going no-code and enabling business users or citizen data scientist to handle data science projects. It is equally important to domain users, analytic experts to enable their ML models build into production. Hence, there are no code MLOps platforms such as HyperSense AI studio. It is designed exclusively for domain and analytic experts to take chart of machine learning models and deploy and manage complete life-cycle of ML models.
Tips to implement MLOps
New age MLOps platforms have significantly reduced the management challenges faced by data scientists, allowing them to be more confident about their code going into production. HyperSense AI Studio is a great example of new-age MLOps. The platform enables any enterprise user to build and operationalize AI successfully using automated machine learning. It increases the efficiency of data scientists allowing them to focus on higher-value tasks. It automates every step of the data science lifecycle including, feature engineering, algorithm selection, and hyper-parameter tuning.

By leveraging HyperSense AI Studio, data scientists and experts can easily and quickly build ML models with larger scale, productivity, and efficiency while sustaining the model quality. By automating a large part of the ML processes, the platform accelerates the time to get production-ready models with greater ease and efficiency. It also reduces human errors mainly because of manual measures in ML models. Further, HyperSense also makes data science accessible to all, enabling both trained and non-trained resources to rapidly build accurate and robust models, thus fostering a decentralized process.

The quality of the machine learning model is not only based on code but also on the features used for running the model. Around 80% of data scientists’ time goes into creating, training, and testing data.

HyperSense AI Studio comes built-in with a feature store that allows features to be registered, discovered, and used as a part of an ML pipeline. In addition, it enables reusing components instead of rebuilding again from scratch for different models driving AI at scale.

HyperSense AI Studio increases the efficiency of data scientists by allowing them to focus on higher-value tasks. The platform also automates every step of the data science lifecycle including, feature engineering, algorithm selection, and hyper-parameter tuning.
Key Takeaways
Data scientists are a highly coveted lot. Yet, 80 percent of their time ends up being wasted doing repetitive tasks that can easily be automated. At the same time, the lack of synergies between data science and operations teams has led to a majority of AI projects to fail. This can be easily avoided.

MLOps allows all the stakeholders in the ML process to work collaboratively and ensure the models they work on gets into production. New tools such as HyperSense AI that brings in automation and low code capabilities also bridge the data science skills gap to a large extent by freeing up nearly 70-80% of the time spent by data scientists on model testing and validation.

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Traditional Black Box AI systems automate decision making and offer limited visibility into how the algorithms work. In a time, when transparency is everything, can we really trust artificial intelligence systems? In this article, we explore the concept of AI Bias and the role of Explainable AI in eliminating AI Bias and increasing model transparency

What is AI Bias?

AI bias is defined as “a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.” This happens when AI models ingest societal biases leading to flawed outcomes. The examples are many: Microsoft’s bot Tay learning racial slurs and Twitter’s photo cropping algorithm blotting out African people.

Why it is important to eliminate AI Bias?

Without a way to check these biases, AI models grapple with inefficiencies. Model accuracy comes under scrutiny, leaving users feeling distrustful about model recommendations. The effectiveness of model predictions also suffers because of results that reflect a skewed reality. For instance, biases in automated loan underwriting can unknowingly isolate an entire demographic of customers that are eligible for affordable loans, leading to negative brand image and lower profitability.

Biased model outcomes also inadvertently encourage discrimination. Seeking to mechanize recruiting, Amazon designed a machine learning program, AMZN.O. It was later found that the algorithm was rating candidates in a non-gender-neutral manner, heavily preferring men over women. On deeper investigation, the fault lay with one of the datasets that used resumes submitted to the company over a period of time, most of which were from male candidates. AI biases can breed a lack of accountability in decision-making within the organization, compromising an open and transparent culture. To gain user trust, AI systems need to be responsible and free of bias. Explainable AI plays a vital role in eliminating model bias and improving AI Adoption.

What is Explainable AI and why it matters?

Explainable AI deals with the concept of building transparent AI systems. According to Google, Explainable AI is “a set of tools and frameworks to help enterprises understand and interpret predictions made by machine learning models.” It is used to describe an AI model, the expected impact, and potential biases. It debugs the model and gives users insights into model behaviour to improve performance.

But perhaps one of the most pioneering features of Explainable AI is that it can resolve biases and gaps within AI models. Simply put, Explainable AI allows users to understand the path that an IT system or algorithm takes to make a decision. Being a new technology with unprecedented potential to transform business and human experiences, explainable AI is critical to gain user trust and enhance AI adoption.

How does Explainable AI work?

At a fundamental level, Explainable AI involves exposing the logic within black box models – and thereby any fallacies – used to drive AI outcomes. A black box model is a catch-all term used to describe a computer program designed to transform various data into useful strategies. In machine learning, these black box models are created directly from data by an algorithm, meaning that humans, even those who design them, cannot understand how variables are being combined to make predictions. The differentiator, therefore, is transparency. When AI models are made transparent, it instantly provides scope to correct human biases.

Best practices to leverage Explainable AI and eliminate AI bias

As the evidence suggests, AI models can embed societal biases and deploy them at scale. Feeding such biases is typically unintentional. Nevertheless, to stay ahead of risk, enterprises need ways to purposefully make AI accountable. The goal is to convert the art of making AI responsible into the science of making AI explainable.

1) Remove bias in the underlying data

Datasets used by AI systems are often the root source of bias. Biases in datasets occur due to two broad reasons. One is the lack of sufficient variety and distribution of data. For instance, non-representative methods of collecting, sampling, and selecting data for the model. Two, decision biases may sprout from past recorded human decisions based on flawed assumptions or societal/historical inequalities. To avoid such biases becoming part of the underlying data, one must proactively ensure that datasets with adequate representation are being used. Platforms that offer a range of granular visualizations assist data scientists in decrypting patterns, ensuring representative samples, and assessing whether the input data is skewed or not.

2) Weed out socially or legally unacceptable correlations

Sensitive variables such as gender, ethnicity, and race are often intentionally avoided as inputs in algorithms. However, these can be picked up from other correlated variables. For example, AI systems may derive ethnicity from geographical locations, and age from the number of times a service has been used. Data scientists must be primed, therefore, and take extra effort to identify such possibilities beforehand. Dashboards that provide visual representations of data and its correlations can greatly help data analysts understand algorithmic logic. These also allow data scientists to apply their own understanding and domain skills to debias the model.

3) Integrate user feedback to ensure model improvements

An AI model should mandatorily include feedback from end users about how the model functions in the real world. This requires steady, continuous, and persistent model testing to refine the model for greater levels of accuracy.

How HyperSense Explainable AI helps eliminate AI Bias?

Companies are wary of the implicit risk within AI models, and want solutions that help them mitigate potential negative impact. HyperSense AI Studio is one such solution. HyperSense AI Studio comes with explainable AI capabilities. It eliminates bias due to costly errors and ensures transparency in prediction, thereby improving model performance and building user trust through AI trust and governance framework. It enables organizations to build trust and confidence when putting AI models into production. It also improves accuracy and fairness when interpreting the models and algorithms.

Key takeaway

Building a system of trust within the AI landscape calls for well-formed ethics, governance, and frameworks. The definition of ‘AI trust’ must be deconstructed and every element transformed into a metric that is measurable and transparent. To ensure unbiased, transparent, and trustworthy results, enterprises need to interpret AI models and their predictions with Explainable AI capabilities with solutions such as HyperSense AI Studio.

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Gone are the days when the most experienced person in the room made the big decisions. Today, it’s data, and not instinct, that drives most business decisions. And the growing volume of data and its complexity mandates harnessing it for quick and sound decisions. According to an IDC survey, the total amount of global data creation will grow by over 163 zettabytes by 2025. So, leveraging data by organizations and individuals to make strategic business decisions has now become more critical than ever!

According to BARC survey, the organizations that are reaping the benefits of Big Data see:

• 69% chance of better strategic decisions
• 52% better understanding of consumers
• 8% average increase in revenue
• 10% reduction in costs

It requires immediate attention from all the organizations across all industries. But although Big Data is critical to an organization’s ability to make efficient business choices, most businesses fail to gain compelling insights. Data usually comes in different types, shapes, sizes, and formats from multiple sources. Many companies use multiple point solutions to handle the various steps in the data to insights journey, creating silos across the organization.

Traditionally, point solutions are built to solve a single problem. Whether providing automation, data-related activities, etc., these tools put their complete focus on offering an effective and efficient solution within their area of expertise. Each point solution requires the development and management of an integration that lets it work with other solutions and the core line of business applications. Flipside, each solution will result in higher maintenance and training costs. Also, point solutions aren’t easily adjusted to scale up and can struggle to adapt to new factors that add additional layers to processes.

According to IDC, data workers are using four to seven different tools to perform data activities. Variety of data sources, diversity of data types, data volumes, multiple targets for analytics, and data science outputs results in multiple, complex point solutions. Proliferating points solutions can make data analytics even more complex than it already is. There are three challenges with this approach:

• To extract value from the data, proficiency in using each of these individuals’ tools is an absolute necessity.
• Integrating various points solutions costs a lot of money!
• Acquiring these points solutions demand individual ROI for each solution.

The need for a unified platform consolidating various tools in a single solution help eliminate the number of point solution across the data value chain. Current spectrum consists of various solutions across the entire data value chain.

This is where an augmented analytics platform comes to the rescue! Consolidating tools into a unified end-to-end platform gives end-users a frictionless experience, reduces the total cost of ownership, and provides consistent answers to what-next and what-if questions across the data value chain. Gartner states that Augmented Analytics is the future of data and analytics, and by 2025, it will facilitate the most widespread way of consuming analytics. Augmented Analytics platforms (such as Subex HyperSense) can step in with machine learning and AI capabilities to assist with data preparation, insight discovery, and insight sharing to augment how enterprises explore and analyze the data. It also automates data science and ML model development, management, and deployment. Augmented Analytics platform couples together different capabilities of data processing, data management, artificial intelligence, and data visualization into one unified platform. This eliminates the need for multiple point solutions for different data needs across an organization.

Augmented Analytics plays a critical role in addressing the first challenge. Augmented Analytics platform (like Subex’s HyperSense) provides automated data engineering and ML modelling capabilities, which goes a long way in alleviating the need to be proficient in multiple point solutions. It democratizes data analytics for the less business-savvy users, i.e., Citizen Data Scientists. It empowers them to build complex AI models without specialized training in data science or programming. Platforms like HyperSense, i.e., no-code AI, provide advanced automated data management, data visualization, ML modelling, and multi-data source integration capabilities in a single unified platform.

With two more challenges of a point solution, let’s see how they can be addressed. The cost of integrating multiple point solutions is quite high. As individual solutions have their own specific IT requirements, release policies, and support windows, these can add to a lot of costs and demand individual ROI for each solution. With accumulating point solutions, you ultimately pay for the same thing multiple times. For example, hard costs to the software vendor (accounts, server space, cost of sales, support, overheads). These costs are passed on to the company and the user. That’s why many companies prefer the concept of one unified solution as it reduces the number of existing point solutions and eases the maintenance of systems lowering the total cost of ownership. Augmented Analytics platform like HyperSense is a cohesive end-to-end platform that contains data management, business modelling, data science, business intelligence, and case management capabilities in a single unified data analytics solution.  Augmented Analytics platform provides a single source of truth for all the data that easily aggregate data from disparate sources, turns data into insights by building, interpreting, and tuning AI models, and sharing their findings across the organization. Having one unified data analytics platform eases data maintenance, ensures consistency and data quality, and supports better reporting and more extensive analyses.

Augmented Analytics platforms (like Subex HyperSense) with their explainable AI capabilities help remove biases and ensure transparency in decision-making. Compared to a point solution, the augmented analytics platform is easy-to-use, delivers more transparency, control, and reliable information. It will change the way analytics is consumed across the organization. The platform will help in reducing data to insights journey and improve decision-making through enterprise-wide democratization of analytics.

Is your organization still using multiple point solutions for different data needs? What are the challenges associated with it? Is there any plan to adopt a single unified data analytics solution across the organization? Please share your thoughts in the comments section below.

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A 2019 story by VentureBeat claims that only 13% of AI projects make it into business production. IDC reports that a quarter of organizations report up to 50% of AI project failure. In recent times, there has been several worrying news about AI applications reported that has put AI under scrutiny again. Uber and Tesla incidents of autonomous systems have raised a question on AI trust. Amazon’s AI-powered recruiting system was gender-biased again (Source: Quartz, Oct, 10′ 2018). Several studies have found that various AI systems built are biased towards gender, race, and skin type in the business space. These business risks make AI systems vulnerable to mistrust. It is a mandate of various data privacy and regulatory authorities to ensure AI systems are sufficiently explainable to target users.

When AI is under scrutiny, the first thing we try to understand is how the AI system is built – what data is the AI trained on, what algorithms are used and how is it tested and validated. Most of the AI models are black box. The black box AI/ML model are those that essentially cannot answer questions such as:

1. Why it made certain predictions that led to a certain type of decision?
2. Why it avoided making other predictions for a given case?
3. How to correct an error?
4. When to trust AI and when not to?

In short,

1. Current black box AI creates business risk.
2. Black box AI creates confusion and doubts.
3. AI systems are mistrusted and are not used to their full potential.

The word Explainable prefixed to AI seems to be a solution to the problem at hand. The explainable AI is a whole new concept of building transparent AI systems. Any AI system when built with a goal to achieve desired transparency and explainability, will eventually choose the right algorithms which are not black box in nature. In other words, these “transparent” algorithms shall provide required explanation to the questions above. The only trade-off one must make with this approach is model accuracy these models are able to achieve and their performance especially on large volume of data is not on par with expectations.

So, we require a system of explainable AI models that can superimpose the black box, super performing AI models to provide explainability, and transparency. It helps users evaluate the model fairness and guardrails AI models with security and controls.

How can the explainable AI model help enterprises?

1. They help verify the ML models and isolate direct and indirect impacts.
2. They improve ML models by leveraging user decision and actions and feedback to improve model outcomes
3. They will help discover newer insights.
4. They debug ML model predictions.
5. They help discover reasons for specific model decisions.

At Subex AI Labs, we apply the AI trust framework in design, develop and deploy ML models from inception to production. This AI trust framework guides us to follow our Digital Trust principles.

We look at AI Trust as a function of 4 key constructs which include Reliability, Safety, Transparency, Responsibility, and Accountability. These core constructs are pillars of driving AI trust in our products and solutions. Let me explain how to enable each core construct.

1. Reliability:AI products generally drives critical business decisions for our clients and underlying ML models which learn the “rules” from the data to drive various decisions comes with a certain level of uncertainty. For example, there will always be a small percentage of misclassification or prediction error driving incorrect decisions; unless it is clearly understood and guarded with human rules or conditional decisions. Having a clear understanding of this uncertainty requires one to first understand the scenarios and conditions in which the model response is accurately certain. This understanding provides a certain level of confidence in the model along with conditions in which model response may not stand its ground. When we train our AI models with our ‘User-centric approach’, it gives all necessary tools to ensure that the AI model built provides the required performance, explainability, and fairness, thereby making the models reliable.

2. Safety: It is a three-dimensional construct influenced by model fairness, explainability, and model security from malicious attacks and/or uncertain data behavior. When we design our AI products, we again apply our User-centric approach which enables us to ensure that the models built are fair for all target users whose experience is AI-optimized. When we develop these models on the data, we ensure that the models are transparent and performing well in various test scenarios. We use various XAI tools and frameworks which help us, and our client users to get the transparency we need to ensure that the models and business decisions based on the models are safe. At deployment of such models, we regularly perform ‘model de-biasing’ using our de-biasing tools to ensure that model stays safe during the entire lifecycle.

3. Transparency: Itenables toensure that the AI products are transparent throughout the lifecycle. It is important to involve the Human perspective in the design loop, the development, and deployment stage of AI products. Our 3D approach for Design, Develop and Deploy AI models enables us to keep Human decisions, situations, and context in mind while designing, developing, and deploying AI models for enhancing user experience.

 

Here is an overview of our frameworks based on which we have designed various tools  with a human in-loop.

 

4. Responsibility and Accountability: We, at Subex, take full responsibility for how our AI products are built and work on user data. We provide full transparency in our AI products, clearly highlighting the short-term and the long-term benefits and impacts of AI within a defined boundary. We work with our clients to build an ethical framework for the business to ensure data privacy, model security and maximize user experience which improves the overall Digital Trust of our client’s products and services. We help our customers build ethical AI products that inspire customer trust.

At Subex, we take utmost care in the way our AI products are built and the way we work on user data. We provide full transparency in our AI products, clearly highlighting the benefits and the impact of AI within a defined boundary. We work with our clients to build an ethical framework for the business to ensure data privacy, model security, and maximize user experience, enabling our clients to build AI trust and digital trust.

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Consumer behavior is constantly evolving. The COVID-19 pandemic has changed the way people live, work, and interact with brands. The challenges brought on by lockdowns and restrictions on movement in public have increased our dependence on online services and accelerated the shift to remote work. With this increased digitization, the need for data, speed, security, and connectivity has risen exponentially. These trends are reshaping the telecom business model for both B2B and B2C clients. In the new digital and 5G race, telcos now stand to gain more than just high-speed, high-performance connectivity. Communication Service Providers can strike gold with billion-dollar monetization avenues in the form of new-age digital services. Consumer trust will play a crucial role in making this a reality.

User Data and Consumer Trust in Telecom

Telecom operators are sitting on an untapped data goldmine. While the industry generates and collects large volumes of consumer data, it has not capitalized this data fully for monetization especially in exploring new digital avenues. As they are governed by various regulatory bodies, Telcos must adhere to stringent user data privacy policies which further limits the use of this data. Without a focused digital objective, most of this data is siloed and unused for enabling or providing digital services.

The challenges brought on by the pandemic have changed the way businesses across sectors operate. From healthcare, education, financial services to entertainment, there has been a dramatic shift to digital channels since the onset of the pandemic, and telecom operators have played a critical role in enabling these digital services. This has increased the overall consumer trust in telcos and consumers today are more willing to share their data with telecom operators.

As per an Analysys Mason Survey, telecom operators today garner more consumer confidence than tech companies. Consumer trust is a valuable asset for building loyalty and driving growth in the digital marketplace. Telcos can capitalize on this trust to unlock new monetization avenues and transition to become providers of new-age digital services. The telecom digital transformation presents a $2 trillion opportunity for the industry.

Making That One Giant Leap Towards Digital Services

With 5G, the access to data on user behavior, and greater trust premium, there is a real potential now for telcos to transform into new-age digital service enablers and providers. Telcos already have all the ingredients necessary to deliver these new-age services – the technology, the speed, the connectivity, the security, and the user data.

In addition to diversifying the business portfolio and opening up new revenue streams, these services allow CSPs to carve out a new digital future for themselves – one that’s conducive to sustainable growth in a hyper-connected world. By diversifying their portfolio and reaching out to all types of online customers, telcos can gear up to become the CSPs of the future.

The Need for Investing in the Right Opportunities and Partners

As consumers continue to switch to online mediums and new digital services, telcos must go beyond traditional services and identify new digital avenues that have the highest potential in terms of ROI. These include consumer services such as entertainment, healthcare, mobile finance, and more. Not all opportunities are worth pursuing, the key is to identify the right digital areas for diversifying the telco business. By identifying the most profitable consumer services, telecom operators can make the right investments and capture the right data to launch successful digital services.

Based on the high-ROI avenues, CSPs can make long-term, growth-focused strategies and invest in the right technologies and partners to deliver digital service in line with customer expectations. A technology-driven approach will enable telcos to make the most of their data, drive automation, and enable a faster transition to new business models. The right technology partnerships can speed up the innovation required to quickly launch new products and services.

Meeting customer experience is vital to sustain consumer confidence in the brand and beat competition. The customer engagement benchmarks must be Ft par with those of other sectors such as OTT.

Change Is Inevitable

On April 3, 1973, Motorola executive Martin Cooper made the first Wi-Fi call from Sixth Avenue, New York to the headquarters of Bell Labs in New Jersey. It was one of the major milestones in cellphone technology that revolutionized the way telecom works. We are now at the cusp of a new revolution – spearheaded by 5G and the rapid digitization. Change is inevitable. Change is also a good opportunity for growth.

Change involves going beyond one’s traditional approach, wading through uncharted waters, and adapting to new realities – no matter what they are. The best strategy for enterprises in today’s dynamic digital landscape, is to be proactive, take a customer-centric approach, evaluate strengths, explore new opportunities and be willing to embrace change to emerge stronger than ever before.

Want to know how telecom operators can leverage emerging technologies like AI to accelerate growth? Get in touch with us!

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In today’s interconnected world, data is a powerful asset for growth. Data can help businesses make profitable decisions to improve multiple functions such as sales, marketing, finance, and revenue. Artificial intelligence is empowering businesses, both big and small, to harness more value from their data and get ahead of competitors. However, accelerated use of data is not possible with just manual efforts.

We are generating more data today than ever before. Internet users today produce about 2.5 quintillion bytes of data every day. Given the large volume of data being generated every day, automation is critical. Artificial intelligence is transforming data analytics as we know it and unlocking new growth opportunities. In combination, AI, data analytics, and automation is enabling enterprises across the globe to achieve unmatched speed, efficiency, and results.

What is data analytics automation?

Data analytics automation involves the automation of the entire data analytics life cycle by using AI/ML-based computer techniques. By enabling users to autonomously monitor and analyze large data sets, data analytics automation allows for fast insight discovery and decision making.

Artificial intelligence can help enterprises automate, simplify, and speed up the data preparation, and insight generation process. AI/ML algorithms can automatically analyze large volumes of streaming data, quickly identify patterns, and generate insights for meaningful action.

With data analytics automation, businesses can quickly turn raw data into reliable insights and drive business transformation projects. AI-enabled data analytics automation offers several high-value use cases from customer engagement, predictive analytics, to product optimization. The potential contribution to the global economy from AI is estimated at around $15.7 trillion by 2030.

Why do you need to automate data analytics?

Augmented analytics or AI-enabled analytics speeds up the process of data preparation, automates insight and report generation, and empowers everyone in the organization to make data-driven decisions. Analytics automation provides enterprises numerous benefits and also makes it easy to share the findings across the organization. Here are some of the key business benefits of automated analytics:

1) Faster insights for profitable decisions

In a competitive market, speed is vital. To successfully launch new services or improve existing products, real-time data insights are essential. Making sense of data metrics and variables from multiple sources is a challenging task. By automating the entire data value chain, users can get real-time insights from raw data to take meaningful, profitable actions. It can help you to successfully upgrade products or manage marketing campaigns.

2) Improve productivity

Automation saves considerable amounts of time and effort in managing the data life cycle process right from data preparation to visualization, allowing data science teams to focus on core business areas and key problems. It removes the complexities of monitoring rapidly changing variables and makes it easy for users to make sense of their data, detect minute anomalies, find hidden patterns, and discover complex insights that are not found through traditional manual approaches.

3) Reduce costs

By saving employee time in data preparation, modeling, and analysis, data analytics automation contributes to overall savings for the enterprise. With SaaS-based AI- platforms, enterprises can quickly scale their AI and data analytics efforts without a large investment in building and maintaining in-house AI capabilities.

By leveraging artificial intelligence, enterprises can automate the entire data life cycle value chain from data ingestion, data preparation, data validation, data analysis, model building to reporting.

No-code AI: The future of AI-enabled analytics

One of the major roadblocks to implementing enterprise AI and data analytics is the limited access to data science and coding skills in the organization. While businesses understand its value, AI implementation becomes a hurdle due to skill shortage.

The emergence of new-age AI-driven analytics solutions is making it easier for enterprises to implement data analytics and automation to get greater business value. New-age flexible, modular, SaaS solutions are allowing businesses to automate the whole data science life cycle including data collection, data preparation, AI modeling, data visualization, and automation of workflows.

In addition to the reduced cost of AI implementation, No-code AI platforms also offer the benefit of speed in turning raw data into insights. Enterprises can run experiments faster and reduce time-to-market.

Subex’s HyperSense is one such no-code AI platform that allows business users without coding skills to easily aggregate data from disparate sources, gain insights by building, interpreting, and tuning AI models. With HyperSense, enterprises can augment the ROI from data analytics, drive automation, and increase efficiency across the entire data value chain.

With a myriad of applications and benefits, AI-enabled data analytics and automation are transforming the future of business as we know it. No-code AI solutions can greatly accelerate the adoption and democratization of Artificial Intelligence (AI) and data analytics in enterprises. As the markets get more competitive, businesses that leverage No-Code AI, stand to benefit in multiple ways and get better results faster.

Want to learn more about No-code AI and HyperSense? Email us at hypersense@subex.com or visit our website, www. hypersense.subex.com

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Artificial intelligence is transforming life as we know it. The COVID-19 pandemic has further accelerated automation and increased enterprise investment in AI across the globe. The global AI market size is expected to grow to USD 309.6 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 39.7%. While there is greater adoption of AI worldwide, scaling AI projects is not easy. According to Gartner, the staff skills, the fear of the unknown, and finding a clear starting point for AI projects are some of the most prominent challenges faced by enterprises in their AI journey. To overcome these challenges and launch new AI initiatives, executives are setting up dedicated AI knowledge platforms or AI Center of Excellence (AI CoE) within their organization. As per a Harvard Business Review article, 37% of U.S. executives from large firms that use AI have already established a COE in AI.

What is an AI Center of Excellence (AI CoE)? 

An AI Center of Excellence (AI CoE) is a centralized knowledge group or team that guides and oversees the implementation of organization-wide AI projects. An AI CoE brings together the AI talent, knowledge, and resources required to enable AI-based transformation projects. It brings together all the AI capabilities needed to address the challenges of AI adoption and prioritize AI investments. AI COE essentially serves as an internal centralized counsel to identify new opportunities for leveraging AI to solve various business problems such as controlling costs, improve efficiency, and optimize revenue. The key objective of setting up an AI CoE is to build and support the AI vision of the company and serve as an internal counsel to manage all AI projects.

Why should you build an AI CoE?

An AI CoE plays a vital role in developing AI talent and driving innovation within the company. The team acts as an internal counsel for guiding the company on all AI initiatives from prioritizing AI investments to identifying high-value use cases for implementation. By providing a robust framework for AI implementation, the CoE helps in building future-ready engineering capabilities to manage high volumes of data, improve efficiency, and drive innovation. Here are the key benefits of creating an AI CoE:

• To consolidate AI resources, learnings, and talent in one place.
• To create and implement a unified AI vision and strategy for the business.
• To standardize the platforms, processes, and approach to AI within the organization.
• To speed up AI-led innovation and identify new revenue opportunities.
• To scale data science efforts and make AI accessible to every function within the company.
• To drive AI-enabled initiatives such as cost reduction, churn prevention, and revenue maximization to stay ahead of competitors.

How to build an AI CoE? 

Technology is constantly evolving. Enterprises must continuously adapt their AI roadmap to deliver the highest business value. With scattered data science teams, resources, and legacy systems, it becomes hard to know where to start. To set up an AI innovation center, business leaders must take a holistic approach encompassing all the factors that contribute to its success.

The key pillars of an AI Center of Excellence 

A CoE or innovation center is built on the following four primary pillars:

1. Strategy: Strategy helps in clearly defining the business goals, identifying the high-impact use cases, and prioritizing the AI investments. When you are starting with AI, while it is okay to think big, it is wise to start with small and smart, achievable, realistic AI goals. This will allow you to progress quickly and gauge the ROI from the AI initiatives.

2. People: The people-oriented pillar helps define the data-driven culture and the strategies for managing the teams that use AI and data within the organization. It is important to clearly define the roles, data owners, and structures for driving AI-led innovation. The people strategy also helps in hiring the right AI talent and fostering a collaborating, innovation-driven culture.

3. Processes: The process-oriented pillar helps define the methods to sustain continual AI innovation within the company. The more iterative and agile the process is, the easier it is to learn fast and adapt to fast-changing business needs.

4. Technology: The right technology stack is crucial for building robust AI capabilities. The tech strategy should clearly define the process for evaluation, and adoption of new tools that match the organizational needs and work well with the existing IT infrastructure.

Best practices and tips to build an AI CoE 

If executed with leadership commitment and a very well-planned strategy, AI has the power to reward organizations with non-linear rewards, however, if not operationalized with the right fundamentals and critical mass scale, it could become a bottomless pit of investments with no significant returns. Here are some best practices and tips to set up an AI CoE:

1. Set AI vision and measurable goals 

Leaders need to identify the key business objectives they want to achieve with AI such as improving conversion, reducing churn, etc. These core goals help in prioritizing the AI investments to be made and in identifying the most high-impact use cases to be implemented first. You also need to develop a transparent and compressive system to track the progress and measure the benefits of their AI initiatives. By capturing the benchmarks and the KPIs for the AI experiments, enterprises can gauge the value generated and do course corrections early on if required.

2. Assemble the right team and set up governance

People are the core strength of an AI CoE. Once you have identified the business problems to solve, you need to onboard the right talent to the core innovation team. Roles and responsibilities will have to be clearly defined. Leaders will also have to set up governance to oversee the development of the CoE.

3. Get your data ready for AI 

AI is only as smart as the data used to train it. Enterprises must invest in robust data collection, cleaning, storage, management, and validation mechanisms to ensure that the data used is reliable and ready for AI.

4. Standardize and create reusable AI assets

Based on the existing systems, the business objectives, and high-value AI use cases, companies must invest in the necessary tools and infrastructure required to apply AI. By creating scalable, flexible, and reusable AI assets, enterprises can apply their AI solutions to multiple scenarios and derive more value.

5. Democratize AI and collaborate with no-code platforms

Good ideas can come from anywhere. To drive AI-led innovation, you need a collaborative, data-driven work culture and the right AI tools. A no-code AI platform can enable anyone in the organization to use AI and apply their perspective to solve complex business problems. In addition to democratizing AI, these platforms help in standardizing AI operations within the company. Leaders must also initiate AI and data science education across functions to nurture an AI-first culture.

The final verdict

In today’s competitive market, AI has become a necessity and a key enabler for growth. No-code AI platforms such as HyperSense can accelerate the adoption and democratization of AI across the organization. It puts the power of AI in the hands of the non-technical business user, eliminating the traditional challenges of misaligned objectives, skill shortage, and siloed operations.

Flexible and modular AI platforms play a vital role in broadening organization-wide AI adoption. A dedicated AI CoE and the right AI platform can facilitate the launch of AI initiatives, accelerate AI implementation, improve efficiencies, and ultimately help enterprises to achieve their long-term AI goals.

For more details on HyperSense AI platform, please email us at hypersense@subex.com or visit our website, www. hypersense.subex.com

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Artificial Intelligence has been one of the prime technological themes of this century. Many businesses are increasingly becoming aware of its impact in today’s world. Thus, they are seriously considering AI adoption now more than ever.

While the COVID-19 pandemic may have affected many aspects of our lives and the way we do business, the research and development in the AI space remain unperturbed. Over the last five years, AI companies attracted nearly $40 billion in investment globally. The United States and China are currently leading the race with massive investments in AI research.

In this blog post, we will look at some of the top emerging trends in enterprise AI.

1. No code AI Automation Platforms.

Leveraging AI capabilities to expand and grow our businesses may sound exciting and help us stay relevant in the competitive landscape but could just as easily become tedious and time-consuming. This is where “No Code” AI platforms come in. It not only helps non-technical individuals experiment building AI solutions, but it also boosts the productivity of the technical team to create reliable and scalable systems at a faster pace. According to a report by Gartner, in a couple of years, over two-thirds of application development will be done by no code or low code platforms.

2. Predictive Analytics for Small Data

In general, the performance of a machine learning model improves with the quantity of data available. In today’s era of big data, not all organizations enjoy the luxury of having large data sets. This is not just a problem concerning only some organizations, but also the inherent nature of some issues that makes the data collection and preparation arduous and expensive. This may be one of the significant roadblocks which hinder taking full advantage of AI.

Machine learning models trained on small datasets are notorious for their tendency to overfit, meaning that they perform very well on the data used for training while yielding poor results when deployed for use cases in the real world. Thus, continuous research becomes critical for exploring and discovering methodologies that produce high accuracy despite limited data.

3. Explainable AI

The introduction of deep neural networks was a game-changer and truly revolutionary. They drastically improved the accuracy of predictive analytics and model performance in general. But one of the challenges was the black-box nature of such models. It was not possible to explain the reason behind the models’ predictions. For more organizations to employ AI for their businesses, it becomes extremely important to trust its predictions.

The models may inadvertently assimilate some biases in the dataset, and therefore an explanation with its prediction will prove useful. Let us take the instance of a model that decides if a credit card should be approved based on the customer’s information. While it is reasonable to deny approval from the model that bases its prediction on age and salary, it is not acceptable if the credit card is denied based on an individual’s gender, race, or country of origin.

Along with the predictions, Explainable AI would give us additional information such as:

What were the key features or variables considered while making the prediction?

What specific values or range of values resulted in the prediction?

How could the prediction change with different feature values?

Such explanations help us be more responsible and accountable while reducing the model bias and the cost associated with erroneous predictions. Explainable AI is still in its juvenile stage, but research in this area is rapidly gaining traction.

4. Quantum AI

Two important reasons why AI has gained popularity over the last couple of decades are the increase in the availability of data and computational power. While it is true that current computing capabilities have improved a thousand times over the last three decades, it is still not sufficient to process big data by executing some heavy algorithms. Employing the classical supercomputers available would take a lot of time to solve the problems at hand. This is where Quantum computing comes to the rescue. The use of quantum computing for executing machine learning algorithms speeds up the process drastically, enabling us to tackle more problems in a short time and paves the way to build Artificial General Intelligence (AGI) systems. Many tech giants have already started investing in this technology and carrying out research to achieve quantum supremacy.

5. AIOps

AIOps is an abbreviation for Artificial Intelligence for IT Operations. The amount of IT operations data getting generated every year is humungous. Managing such large volumes of data by the IT staff to understand the problems and analyze its root cause becomes difficult. To address this issue, AIOps was born.

AIOps uses machine learning capabilities to handle and process enormous amounts of data that arise from many IT components and applications. It would then intelligently detect notable events and anticipate potential problems related to system performance and availability. This will alert the IT team and enable them to address and provide a swift response reducing the mean time to resolution (MTTR).

6. Graph Neural Networks

Many real-world datasets are challenging for ordinary neural networks to handle. Some datasets include social network data, geographical map network data, chemical, and biomolecular structures, etc. But these datasets can be easily expressed as a graph, a mathematical way to represent and model relational information in the data. It led to the development of Graph Neural Networks (GNNs). They are specially designed to handle graph data to produce insights on the relational information present in the data. GNNs are new but have promising applications in various domains like social media, recommender systems, pharmacy, and pure sciences, etc.

7. Ethical AI

Although AI is changing the world for the better, we are faced with ethical and moral dilemmas in many scenarios. Consider the use of AI in the art industry. Let us say an AI model is trained on all the music composed by Beethoven. Now, when this model composes new music like Beethoven, who should be recognized as the original author? Should it be the company that organized the project, the engineers who created the algorithm, or Beethoven himself? AI can also be misused in many ways, potentially threatening human dignity, privacy and disrupting the way we operate in society. Thus, maintaining transparency, accountability, and developing a legal document with a global scope regarding the ethics of AI is of paramount importance. Indeed, AI is a double-edged sword, and it is up to us how responsibly we use it.

As many organizations are reaping the benefits of AI, companies have started ramping up their AI investments. Considering these trends, it is evident that AI is becoming a critical function for all businesses.

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Challenges addressed by No Code AI platforms

An AI model building is challenging on three fundamental counts:

1. Availability of relevant data in good quantity and quality: The less I rant about it, the better.
2. Need for multiple skills: Building an effective and monetizable AI model is not just the realm of a data scientist alone. It needs data engineering skills and domain knowledge also.
3. The constant evolution of the ecosystem in terms of new techniques, approaches, methodologies, and tools

There is no easy way out to address the first challenge, at least not so far. So, let us brush that under the carpet for now.

The need for having multiple resources with complementing skills is an area where a no-code AI platform can add tremendous value. The average data scientist spends half of his/her time preparing and cleaning the data needed to build models and the other half fine-tuning the model for optimum performance. No Code AI platforms (such as Subex HyperSense) can step in with automated data engineering and ML programming accelerators that go a long way in alleviating the requirement of having a multi-skilled team.  What’s more, it empowers even Citizen Data Scientists with the ability to build competent AI models without having the need to know any programming language or having any background in data engineering. Platforms like HyperSense provide advanced automated data exploration, data preparation, and multi-source data integration capabilities using simple drag-and-drop interfaces. It combines this ability with a rich visual representation of the results at every step of the process so that one does not need to wait until the end to realize an error that was done in an early step and have to go back and make changes everywhere.

As I briefly touched upon a while back, getting the data ready is one-half of the battle won. The plethora of options on the other half is still perplexing – Is it a bird? Is it a plane? Oh no, it is Superman! Well, in our context – it would be more like – Is it DBSCAN? Is it a Gaussian Mixture? Oh no, it is K-Means! Feature engineering and experimenting with different algorithms to get the most optimum results is a specialized skill. It requires an in-depth understanding of the data set, domain knowledge, and principles of how various algorithms work. Here again, No Code AI platforms like HyperSense come to the table with significant value adds. With capabilities like autonomous feature engineering and multi-algorithm trial and benchmarking, I daresay that it makes building models almost child’s play. Please do not get me wrong. I am not for a moment suggesting that these platforms will result in the extinction of the technical data scientist role, on the contrary, it will make them more efficient and give them superpowers to solve greater problems in lesser time while managing and guiding teams of citizen data scientists to solve the more mundane, yet, problem statements of existential importance.

So far, so good; and having brushed one challenge under the carpet and discussed the other one, there is one more – The constant evolution of AI techniques, methodologies, tools, and technologies. Today, just being able to build a model which performs well on a pre-defined set of metrics does not cut ice anymore. It is just not enough for a model to be simply accurate. As the AI landscape evolves, the chorus for the Explainability and Accountability in models is reaching a fever pitch. Why did K-Means give you a better result than Gaussian Mixture? Will, you then get the same result if a feature was modified or a new one added? Why did the model predict a similar outcome for most customers belonging to a certain ethnicity? Is the model replicating the bias and vagaries present in the historical data set or the person building the model? If there have been policies and practices in a business where any sort of decision bias crept into day-to-day functioning, it is but natural that the data sets you work on will have those biases and the model you build will continue to persuade you to make decisions with the same biases as before. As an organization that is striving to disrupt and transform your industry, it is pertinent that you identify and weed out such biases sooner than later before your AI models hit scale and it becomes a wild animal out of its cage.

As No Code AI platforms evolve, model explainability is something that is already getting addressed. Platforms like HyperSense give you the option to open up the proverbial ‘black-box’ and peep inside to see why a model behaved the way it did. It provides the analyst or the data scientist with an opportunity to tinker around advanced settings and fine-tune them to meet the objectives. Model accountability and ethics is a whole different ball game altogether. It is not restricted just to technology but also the frailties of human beings as a species. I am sure the evolving AI ecosystem will eventually figure out a way to make the world free of human biases – but hey, where’s the fun then? Human biases do make the world interesting and despicable in equal measure and I believe the holy grail for AI will be to strike a balance between the two.

Until then, let us empower more and more creative and business stakeholders to explore and unleash the true power of AI using No Code platforms like HyperSense so that the world can be a better place for all life forms.

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