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No-code AI is a type in the AI application that aims to democratize AI. No-code AI means using a no-code development platform with a visual, code-free and often drag-and-drop interface to deploy AI and machine learning models. No code AI enables non-technical users to quickly classify, analyze data and easily build accurate models to make predictions without business analysts.

No code AI Platform is the closest to the ideal of “any person without prior training” when it comes to data handling. The increased level of democratization of data to personalized applications has been proven time and time again, the majority of businesses struggle to implement AI to utilize its full potential and scale the use cases to all workflows of the company.

With each model of ML built for a company, the goals of the unique model of No-code would be to conquer complexities, increase efficiency with automated machine learning, instant visualizations and Auto Feature Engineering which all provide ready results once the AI platform is up and running.

Here is how to build an AI platform with No-code

CONNECT :

Building custom AI solutions requires integrating with an existing data source or uploading your data files in just a few clicks, cleaning data, categorizing, structuring data, training and debugging the model. These take even less time on No-code to enable Auto Data Preparation. Studies claim that low code/ no-code solutions have the potential to reduce the development time up by 90%, so connecting your existing business models and data with point and click interfaces will draw the most benefits once employed.

BUILD :

No-code AI platforms allow automation through drag and drop UI. Easy-to-use AI platforms leverage the time/value/knowledge trade-off in a genuinely attractive way and allow customers with no AI coding skills to optimize day-to-day operations and to solve business issues.

Visual, often drag-and-drop, no-code AI tools make AI easy to operate and build a personalized platform for non-technical people or those who lack the time or resources to build such systems from the ground up. Building the No-code allows skipping training, testing and validating the models that are traditionally required by ML platforms.

DEPLOY :

Saving and launching takes just a few clicks and can be instantly applied on web platforms or other applications to start using on your business models.

COLLABORATE :

Inviting and corporating relevant data and integrating AI models help the ML make a better prediction and help with accuracy in all applications.

Besides the ease of starting with No-code, there are some huge advantages to no-code AI:

• Accessibility: No-code AI allows businesses to make use of AI in the first place and can act as the stepping-stone towards intensified use of data science or AI in the future. The relatively low investment paired with people building up hands-on knowledge of AI tools tackles the biggest obstacles to AI adoption at small and mid-sized companies.
• Usability: Drag-and-Drop allows anyone in the company to find an AI solution to a problem, and more often than not, in a budget-friendly way. These tools are built primarily with non-technical users and non-developers in mind.
• Speed: The best type of no-code AI application allow users to iterate through time and help machine code platforms to learn quickly. This allows for more rapid experimentation to see what can be done using one’s data – and getting back to business right afterwards. The simplicity of the process of No-code deployment and intuitive processes help significantly in the launching speed.
• Quality: No-code tools useful for people who may not have a technical talent group, to begin with. This takes significant work going into the product with defaults and safety measures that need to be carefully chosen on behalf of the user. To further tackle such risks, some AI platforms have human reviews built-in and ask for input when required. This reduces human error marginally when setting up such systems in the first place and allows direct interaction with the platform during daily operations.
• Scalability: AI itself doesn’t have any drawback to manage a task for a single or a hundred users and neither do servers that are automatically scaled up or down, depending on the load.

How HyperSense AI Platform works?

HyperSense AI uses automated machine learning to automate iterative tasks of machine learning model development. It allows data scientists and experts to build ML models with higher scale, productivity, and efficiency while sustaining the model quality. It accelerates the time to get production-ready models with greater ease and efficiency. It also decreases human errors mainly because of manual measures in ML models. It also makes data intelligence 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 a 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 with features to store registered, discovered and used data as a part of an ML pipeline. It enables reusing features for different models driving AI at scale.

HyperSense AI Studio is built with assisted analytics capabilities, provides users with the ability to build applications with suitable levels of automation and human involvement at any stage of the data science cycle based on task and business requirements. It notifies users while creating a pipeline. It reduces complexity and iterations. These workflows are not a black box, so what happens at each stage can be surfaced to the user in detail, including the results.

Guided Analytics As many enterprises have started their AI journey and are at different stages of maturity, they require a module that can leverage their existing components in an organization while choosing a specific module suitable to their business needs.

HyperSense AI Studio is a highly composable data science studio that allows them to integrate with the existing assets in the organization. It can be integrated using various options such as APIs, files, databases, and streaming to achieve modularity and plug-and-play capabilities for quick data collection.

Composable Architecture Visual Analytics combined with HyperSense AI Studio’s automated ML capabilities provides users with visibility into how the AI system arrives at a decision and how the decision can be improved. Without AI Model Monitoring, sometimes the decisions made can be biased. Visual analytics can reduce these suspicions by providing a fine picture of the decision process. It improves the efficiency of the AI projects and helps to create ML pipelines quickly

HyperSense AI Studio provides ML operation capabilities that leverage automation to monitor, deploy and govern operations to manage machine learning lifecycle to get better business results. With ML platforms, models can be easily deployed into the production environments. It provides constant monitoring and production diagnostics to improve the performance of existing models. It also shows an AI trust and testing framework that helps to maintain the governance process for AI projects across the entire organization. ML HyperSense AI Studio supports multiple deployment options based on business requirements and priorities. The deployment options are On-premises Cloud or Hybrid.

Overall, deploying an AI Orchestration platform helps enterprises operationalize AI enabling scalability and growth. Facilitating technologies such as machine learning and AI assists with data preparation, model construction and deployment, insight generation, and insight explanation to augment how enterprises explore and analyze the data for substantial improvements.

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Machine learning and artificial intelligence are two limbs of technology that are quickly evolving and helping businesses run. Today’s businesses rely on updated data and deep insights, which primarily depend on our ability to find and analyze them. In such a case, we need machine learning to learn from this data and give individualized assistance on a large scale to help us make better decisions. However, mastering machine learning tools and deep-diving into data science is time-consuming. This led to the development of No-Code AI!

With the advancements in ML and AI, both no-code AI platforms and libraries have expanded quite a bit. The limits to using and applying ML models in applications have deterred greatly. Machine learning has become more accessible than ever before as the industry expands.

In this blog, we introduce you to how No Code AI works, its benefits, and what it can deliver for businesses.

What exactly is No-code?

The quick definition would be that it is a bot that can deliver everything AI and ML promise without having to write any code. A no-code development platform is a graphical development interface that allows developers to create portable applications using established templates, pre-built logic models, drag-and-drop application components, links to other constituents, and so on, all without having to code.

No-code technologies are often aimed at business users, enabling them to easily transform their corporate work-cases into personalized applications without the users having prior coding skills to create applications using no-code.

No-Code is the future, and here’s why :

The objective of no-code AI is to help businesses to turn data into actionable information via predictive analytics in minutes rather than weeks or months. Programs can be built from the ground up with end-to-end scalability in mind. From rapid deployment tech to plug-and-play integration, No-Code AI has it all.

More and more businesses are shifting towards AI and ML hounding the promise of success via AI and rightfully so. By democratizing access to machine learning capabilities across any team, it is easier to optimize the businesses once the tools are properly integrated.

There is a significant demand for skills and talents in the technology business. No-Code AI would bridge this demand and supply gap by allowing non-programmers to manage the addition of fundamental functionalities, which in turn frees up IT staff to focus their efforts on more challenging tasks or strategies with higher business value. This tradeoff also saves the company valuable time and resources in the long run.

Managing and maintaining no-code machine learning environments can be done from a single comprehensive dashboard, which empowers IT professionals, to design every app within the boundaries of their organization.

The Benefits of No-Code AI

There are diverse advantages of using no-code, given that they are “effortless and suitable.” We’ll go over a bunch of them for you.

1. Evolve into data-driven business without a data science team

Because the speed at which an application can be developed is faster and has even become simpler, the IT or Data Science department is no longer flooded with requests. Work that once took months is now completed in hours or days, so putting forth your ideas and work program samples are easier than ever before. In return, No-code AI can deliver a data-driven application that can work for you independently to the data science team.

2. Deploy machine learning-driven strategies and scale them

Traditional coding has the drawback of making it difficult to update functionality, especially if the code is written in a language you are unfamiliar with. You can quickly revise the functionality with no-code in only a few hours. With the ever-evolving market, it’s never been easier to utilize No-code ML tools to leverage data directly to drive your product sales or scale your business.

3. Improve decision-making

Machine learning appears to most firms to be a sophisticated, pricey, and talent-intensive technology. But in reality, Machine learning platforms can be wonderful efforts without much investment and infrastructure needs, whether the goal is to construct a recommendation engine or a machine learning API to harness your real-time social informatics, ML can help you make better decisions by working faster with data but also develop a visual data information output you can understand quickly.

4. Eliminate costs while improving profit

The maintenance of ML and AI was scary not so long ago because of the complexity required in the pre-no-code era. But with No-code ML, even during maintenance, you don’t need a programmer which reduces the costs significantly both in terms of maintaining a data science operator and also the costs that would traditionally be levied to pay for updating the model and keeping the algorithm in check.

How to get started with No-Code?

If you decide to get started with machine learning and integrate it into your existing programme, Experience HyperSense No Code AI platform which provides unique combination of automation and customization of machine learning pipelines to help enterprise scale faster.

In high-load, data-intensive situations, they are not a replacement for specialised ML models. These technologies are only liable for what they are: no-code platforms that allow non-technical users or ML newcomers to quickly adapt to the models and apps.

It is evident now that the future with No-code Machine Learning at the core will bring great results towards businesses opting to deploy them. The sooner the deployment of ML into the system, the better will be the results from the self-taught algorithm in AI.

Operationalize Machine Learning models with MLOps

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Machine learning and artificial intelligence are two limbs of technology that are quickly evolving and helping businesses run. Today’s businesses rely on updated data and deep insights, which primarily depend on our ability to find and analyze them. In such a case, we need machine learning to learn from this data and give individualized assistance on a large scale to help us make better decisions. However, mastering machine learning tools and deep-diving into data science is time-consuming. This led to the development of No-Code AI!

With the advancements in ML and AI, both no-code AI platforms and libraries have expanded quite a bit. The limits to using and applying ML models in applications have deterred greatly. Machine learning has become more accessible than ever before as the industry expands.

In this blog, we introduce you to how No Code AI works, its benefits, and what it can deliver for businesses.

What exactly is No-code?

The quick definition would be that it is a bot that can deliver everything AI and ML promise without having to write any code. A no-code development platform is a graphical development interface that allows developers to create portable applications using established templates, pre-built logic models, drag-and-drop application components, links to other constituents, and so on, all without having to code.

No-code technologies are often aimed at business users, enabling them to easily transform their corporate work-cases into personalized applications without the users having prior coding skills to create applications using no-code.

No-Code is the future, and here’s why :

The objective of no-code AI is to help businesses to turn data into actionable information via predictive analytics in minutes rather than weeks or months. Programs can be built from the ground up with end-to-end scalability in mind. From rapid deployment tech to plug-and-play integration, No-Code AI has it all.

More and more businesses are shifting towards AI and ML hounding the promise of success via AI and rightfully so. By democratizing access to machine learning capabilities across any team, it is easier to optimize the businesses once the tools are properly integrated.

There is a significant demand for skills and talents in the technology business. No-Code AI would bridge this demand and supply gap by allowing non-programmers to manage the addition of fundamental functionalities, which in turn frees up IT staff to focus their efforts on more challenging tasks or strategies with higher business value. This tradeoff also saves the company valuable time and resources in the long run.

Managing and maintaining no-code machine learning environments can be done from a single comprehensive dashboard, which empowers IT professionals, to design every app within the boundaries of their organization.

The Benefits of No-Code AI

There are diverse advantages of using no-code, given that they are “effortless and suitable.” We’ll go over a bunch of them for you.

1. Evolve into data-driven business without a data science team

Because the speed at which an application can be developed is faster and has even become simpler, the IT or Data Science department is no longer flooded with requests. Work that once took months is now completed in hours or days, so putting forth your ideas and work program samples are easier than ever before. In return, No-code AI can deliver a data-driven application that can work for you independently to the data science team.

2. Deploy machine learning-driven strategies and scale them

Traditional coding has the drawback of making it difficult to update functionality, especially if the code is written in a language you are unfamiliar with. You can quickly revise the functionality with no-code in only a few hours. With the ever-evolving market, it’s never been easier to utilize No-code ML tools to leverage data directly to drive your product sales or scale your business.

3. Improve decision-making

Machine learning appears to most firms to be a sophisticated, pricey, and talent-intensive technology. But in reality, Machine learning platforms can be wonderful efforts without much investment and infrastructure needs, whether the goal is to construct a recommendation engine or a machine learning API to harness your real-time social informatics, ML can help you make better decisions by working faster with data but also develop a visual data information output you can understand quickly.

4. Eliminate costs while improving profit

The maintenance of ML and AI was scary not so long ago because of the complexity required in the pre-no-code era. But with No-code ML, even during maintenance, you don’t need a programmer which reduces the costs significantly both in terms of maintaining a data science operator and also the costs that would traditionally be levied to pay for updating the model and keeping the algorithm in check.

How to get started with No-Code?

If you decide to get started with machine learning and integrate it into your existing programme, Experience HyperSense No Code AI platform which provides unique combination of automation and customization of machine learning pipelines to help enterprise scale faster.

In high-load, data-intensive situations, they are not a replacement for specialised ML models. These technologies are only liable for what they are: no-code platforms that allow non-technical users or ML newcomers to quickly adapt to the models and apps.

It is evident now that the future with No-code Machine Learning at the core will bring great results towards businesses opting to deploy them. The sooner the deployment of ML into the system, the better will be the results from the self-taught algorithm in AI.

Operationalize Machine Learning models with MLOps

Learn more

There’s no doubt that AI and analytics are already changing how businesses operate across different industries- whether through task automation, insight generation, or other use cases. In 2022, it will continue to transform the enterprises in the way they live and work, and leaders will shift their focus to adopting AI solutions that are more sustainable and scalable.

What 2022 is going to look like for AI and Data Analytics?

In 2021, many enterprises saw the adoption of AI in at least one function across major industries. According to McKinsey Global Survey 2021, 57 percent of respondents report AI adoption, up from 45 percent in 2020.

In 2022, preparation is the key to AI and Analytics success. It may be tempting to push AI into legacy environments as quickly as possible; it would be wiser to adopt a more careful and thoughtful approach. AI is only as good as the data it can access, so shoring up both infrastructure and data management and preparation processes will play a substantial role in adopting future AI-driven initiatives.

From a technology perspective, there are many discussions around low-code/no-code AI platforms and architectural approaches to analytics, like Data Fabric, composable data, and analytics, workforce augmentation, etc. Let’s look at some interesting statistics for this year.

Top AI and Data Analytics trends to keep you on the radar

Many new developments and breakthroughs will continue to push the boundaries of what’s possible. Here are the key areas where those breakthroughs will occur in 2022:

#1 Data Fabric will be a key foundation for the enterprise to establish a frictionless data journey

As the data increases in volume and becomes increasingly complex, and digital business accelerates, data fabric creates an agile and data-centric environment that responds quickly to the fast pace of change. According to Gartner, the data fabric concept enables frictionless access to and sharing data in a distributed data environment. It consists of end-to-end data integration and management solution that unlocks the potential of the data and reduces data to insights journey from any environment- cloud, on-premises, or edge. It reduces the time for integration for design by 30%, deployment by 30%, and maintenance by 70% because the technology designs draw on the ability to use/reuse and combine different data integration styles.

#2 Composable data and analytics fosters agility

Enterprises have more than one standard tool for analytics and BI. So, introducing new technology or tool becomes a costly affair. Composable data and analytics use/reuse components from multiple data, analytics, and AI solutions to reduce costs, boost deployment speed, and encourage collaboration. It uses AI across business intelligence, data management, and predictive analytics, evolving the analytics capabilities of an organization and enabling leaders to connect data insights to business actions.

#3 Improved decision intelligence for enterprise-wide decision support

Decision intelligence uses emerging technologies such as AI/ML to process large amounts of data to quickly extract meaningful insights for enterprises needed to drive actions for the business. In 2022, decision intelligence has the potential to make assessments not only better, but also faster, given that machine-generated decisions can be processed at speeds that humans cannot achieve.

#4 Rise of Low-code and No-code technologies

According to Gartner, by 2023, over 50% of medium to large enterprises will have adopted low-code or no-code as one of their strategic application platforms. Also, it predicts that low-code platforms will be responsible for more than 65% of application development activity by 2024. The low-code and no-code technologies enable businesses to keep up with the rapidly changing technology landscape through innovation and by empowering business users and technical programmers to build applications with little to no coding.

No-code/Low-code vs. Code-Heavy: What’s the difference?

#5 Ethical AI and Ethical Data Analytics become tangible

As enterprises power AI advancements, the lack of governmental oversights has pushed the debate over the ethics of responsible AI to the fore. In 2022, we will see how ethical AI and ethical data analytics will continue to play a significant part in the simulation of innovation and economic growth, since more organizations will realize the need for responsible tech. Fairness of algorithms and data transparency are issues that will need to be addressed in the coming years as AI adoption is more widespread than ever. It will hopefully work its way towards policymakers as well.

#6 AI will evolve more rapidly, expanding and impacting every business process

While in 2021, most of the enterprises were still in the proof-of-concept phase of AI. 2022 will see a shift towards AI-first approaches. AI applications will be at the forefront of enterprise strategies. As AI/ML models become the norm, companies will expand AI to become every part of the department and impact every business process.

#7 AI will become more widespread and accessible

Previously in 2021, only big players such as Amazon, Google, Microsoft, etc., had the deep pockets to make AI/ML models a reality. In 2022, there will be more off-the-shelf technology to make AI/ML models more accessible, like readily available functionality to make applications talk, convert speech to text, and other industry-specific use cases. Also, modern workplaces are evolving with AI getting incorporated into their processes. Humans and machines will work alongside each other for quicker results. This creates a great combination of human innovation and machine intelligence.

In 2022, AI and data analytics will not only be more prevalent but will also be more strategic. It will continue to be used to achieve productivity gains. In the coming years, AI will also be used to rethink and redesign products, services, business models, and overall strategy.

This year, the challenges of integrating, cleaning, and processing data will continue. However, at the same time, there will be a flood of more generic AI and data-analytics platforms that will help replace manual tasks, freeing up data scientists for strategic tasks. As today’s enterprises strive to be data-driven and demand that the data be most efficient to provide a better experience, more enabling technologies will be available to ease the transition and adoption of AI across the organization.

Do you agree with the points discussed in the article? If yes, which of the trends do you plan to adopt in your organization? Feel free to let us know your thoughts and comments in the section below if we’ve missed any important points.

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Introduction :

Hyperparameter optimization is the science of tuning or choosing the best set of hyperparameters for a learning algorithm. A set of optimal hyperparameter has a big impact on the performance of any machine learning algorithm. It is one of the most time-consuming yet a crucial step in machine learning training pipeline.

A Machine learning model has two types of tunable parameter :

• Model parameters
• Model hyperparameters

Model parameters vs Model hyperparameters (source)

Model parameters are learned during the training phase of a model or classifier. For example :

• coefficients in logistic regression or liner regression
• weights in an artificial neural network

Model Hyperparameters are set by user before the model training phase. For example :

• ‘c’ (regularization strength), ‘penalty’ and ‘solver’ in logistic regression
• ‘learning rate’, ‘batch size’, ‘number of hidden layers’ etc. in an artificial neural network

The choice of Machine learning model depends on the dataset, the task in hand i.e. prediction or classification. Each model has its own unique set of hyperparameter and the task of finding the best combination of these parameter is known as hyperparameter optimization.

For solving hyperparameter optimization problem there are various methods are available. For example :

• Grid Search
• Random Search
• Optuna
• HyperOpt

In this post, we will focus on Optuna library which has one of the most accurate and successful hyperparameter optimization strategy.

Optuna :

Optuna is an open source hyperparameter optimization (HPO) framework to automate search space of hyperparameter. For finding an optimal set of hyperparameters, Optuna uses Bayesian method. It supports various types of samplers listed below :

• GridSampler(using grid search)
• RandomSampler(using random sampling)
• TPESampler (using Tree-structured Parzen Estimator algorithm)
• CmaEsSampler ( using CMA-ES algorithm)

Use of Optuna for hyperparameter optimization is explained using Credit Card Fraud Detection dataset on Kaggle. The problem statement is to classify a credit card transaction fraudulent or genuine(binary classification). This data contains only numerical input variables which are PCA transformation of original features. Due to confidentially issues, the original features and more background information about the data are not available.

In this case, we have used only a subset of the dataset to speed up the training time and to ensure the two different classes reach a perfectly balance. Here, the sampling method used is TPESampler . A subset of a dataset is shown in the figure below :

A subset of Credit Card Fraud Detection dataset

Importing required packages :

import optuna
from optuna import Trial, visualization
from optuna.samplers import TPESampler
from xgboost import XGBClassifier

Following are the main steps involved in HPO using Optuna for XGBoost model:

1. Define Objective Function :
   The first important step is to define an objective function. The objective should be to return a real value which has to minimize or maximize. In our case, we will be training XGBoost model and using the cross-validation score for evaluation. We will be returning this cross-validation score from an objective function which has to be maximized.
2. Define Hyperparameter Search Space :
   Optuna supports five kind of hyperparameters distribution, which are given as follows :

• Integer parameter: A uniform distribution on integers.
  n_estimators = trial.suggest_int(‘n_estimators’,100,500)
• Categorical parameter: A categorical distribution.
  criterion = trial.suggest_categorical(‘criterion’ ,[‘gini’, ‘entropy’])
• Uniform parameter: A uniform distribution in linear domain.
  subsample = trial.suggest_uniform(‘subsample’ ,0.2,0.8)
• Discrete-uniform parameter: A discretized uniform distribution in linear domain.
  max_features = trial.suggest_discrete_uniform(‘max_features’, 0.05,1,0.05)
• Loguniform parameter: A uniform distribution in log domain.
  learning_rate = trial.sugget_loguniform(‘learning_rate’ : 1e-6, 1e-3)

The below figure shows the objective function and hyperparameter for our example.

Objective Function

3. Study Objective :
   We have to understand some important terminologies mentioned in their docs, which will make our work easier. These are given as follows :

• Trial : A single call of the objective function
• Study : An optimization session, which is a set of trails
• Parameter : A variable whose value is to be optimized such as value of “n_estimators”

The Study object is used to manage optimization process. Method create_study() returns a study object. A study object has useful properties for analyzing the optimization outcome. In method of create_study(), we have to define the direction of objective function i.e. “maximize” or “minimize” and sampler for example TPESampler(). After creating study, we can call Optimize().

4. Best Trial and Result :
   Once the optimization process completed then we can obtain the best parameters value and the optimal value of the objective function.

Best trial: score 0.9427118644067797,
params {‘n_estimators’: 396, ‘max_depth’: 6, ‘reg_alpha’: 3, ‘reg_lambda’: 3, ‘min_child_weight’: 2, ‘gamma’: 0, ‘learning_rate’: 0.09041583301198859, ‘colsample_bytree’: 0.45999999999999996}

5. Trail History :
   We can get the entire history of all the trial in the form data frame by just calling study.trails_dataframe().

6. Visualizations :

Photo by Isaac Smith on Unsplash

Visualizing the hyperparameter search space can be very useful. From the visualization, we can gain some useful information on the interaction between parameters and we can see where to search next. The optuna.visualization module includes a set of useful visualizations.

i) plot_optimization_history(study): plots optimization history of all trials as well as the best score at each point.

Optimization History Plot

ii) plot_slice(study): plots the parameter relationship as slice also we can see which part of search space were explored more.

Slice Plot

iii) plot_parallel_coordinate(study) : plots the interactive visualization of the high-dimensional parameter relationship in study and scores.

Parallel Coordinate Plot

iv) plot_contor(study): plots parameter interactive chart from we can choose which hyperparameter space has to explore.

Contour Plot

Overall, Visualizations are Amazing in Optuna !!

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