AutoML & No-Code AI: Data Science in 2025

Did you know? ​The Automated Machine Learning (AutoML) market, which was valued at approximately ₹20,000 crore in 2023, is projected to reach around ₹4,80,000 crore by 2032. This surge shows the increasing adoption of AutoML tools, as organizations strive to streamline ML processes and make AI more accessible.​

AutoML is transforming the way businesses approach machine learning by automating complex processes, making AI accessible to everyone, regardless of technical expertise. 

For example, Google’s AutoML helps developers build custom models for image classification without requiring deep knowledge of machine learning. Healthcare organizations use AutoML platforms to accelerate medical image analysis and assist in disease prediction.

This guide will introduce you to AutoML, explaining how it simplifies the machine learning workflow. It will also show you how mastering AutoML can open up new opportunities in your career.

How AutoML & No Code AI Are Reshaping Data Science in 2025

AutoML and no code AI platforms are making machine learning more accessible and efficient. These technologies are allowing businesses to deploy AI solutions faster and with fewer technical barriers. 

For example, in retail, brands use no code AI to predict customer churn and personalize marketing campaigns without needing a data science team. In finance, AutoML helps analysts build credit risk models and detect fraudulent transactions with just a few clicks.

As AI continues to grow, you’ll see more industries embracing these tools to streamline operations and gain a competitive edge.

What is AutoML & No Code AI? AutoML, or Automated Machine Learning, allows you to build machine learning models without deep expertise in coding or algorithms. It automates tasks like data preprocessing, feature selection, model training, and hyperparameter tuning. 

No code AI platforms take it a step further, enabling you to create AI models through a user-friendly interface, no coding required. These platforms are designed for those who may not have a background in data science but want to harness the power of AI.

Did you know? No code technologies are expected to be used in 70% of new applications produced by businesses by 2025, up from less than 25% in 2020. This statistic highlights the growing importance of these platforms in accelerating AI adoption, enabling companies to innovate without waiting on lengthy development cycles.

Also Read: Types of Machine Learning Algorithms with Use Cases Examples

Why Are Traditional Data Science Workflows Becoming Obsolete?

Traditional data science workflows require a lot of manual coding, complex data handling, and extensive expertise. With machine learning models becoming more complex, this approach is time-consuming and expensive. 

AutoML and no code platforms offer a way to simplify the process. Instead of writing code for every part of the model, these tools provide pre-built pipelines that automate tasks like data cleaning, model selection, and optimization. This shift means data scientists can focus more on problem-solving and less on repetitive tasks.

The traditional approach also requires a team of specialized data scientists to handle model deployment. With no code AI, even non-technical professionals can build, deploy, and refine models independently, making AI accessible across the organization.

Also Read: Career Opportunities in Artificial Intelligence in 2025

The Rise of No Code Platforms in AI Development

No code platforms are quickly gaining traction, particularly in industries like retail, healthcare, and finance. These platforms allow anyone to create AI-powered solutions without relying on a data science team. 

For instance, Salesforce’s Einstein AI lets users automate workflows, predict customer behaviors, and personalize marketing campaigns without any coding. In healthcare, platforms like DataRobot enable hospitals to develop AI models for disease prediction, improving patient outcomes without needing specialized expertise.

As businesses recognize the potential of no code AI, they are increasingly turning to these platforms for quick and scalable AI deployment. 

Did you know? 70% of applications used by businesses are expected to use low-code or no code technologies.

How AutoML is Accelerating AI Model Deployment?

AutoML platforms are revolutionizing how quickly and efficiently AI models are deployed. Tools like Google Cloud AutoML, Amazon SageMaker, and H2O.ai automate the machine learning pipeline, reducing the need for deep expertise and speeding up model deployment.

For instance, Google AutoML helps retail businesses build demand prediction models by automating tasks like feature engineering and hyperparameter tuning. Amazon SageMaker simplifies model training and deployment, offering pre-built algorithms and integrated hosting, reducing time-to-market. H2O.ai provides open-source automation with easy integration across industries.

In terms of time-to-market, Google AutoML accelerates the process from raw data to a trained model, while SageMaker streamlines deployment with pre-built tools. Both platforms can cut deployment time from months to weeks or even days, which is crucial for industries like finance where fast adjustments are needed.

Here’s a table representing the growth of AI Accessibility vs. Number of AI-Specialized Data Scientists (2018-2025):

Also Read: How to Build Your Own AI System: Step-by-Step Guide

Now that you are familiar with AutoML, let’s explore how it is democratizing AI development.

AutoML: Automating Machine Learning for Everyone

AutoML tools simplify machine learning, enabling professionals without technical expertise to build powerful models. Business analysts, product managers, and others can now deploy machine learning models without needing specialized data science skills, making AI more accessible and efficient.

How AutoML Simplifies Model Selection & Hyperparameter Tuning?

One of the most challenging parts of traditional machine learning development is selecting the right model and tuning its hyperparameters. AutoML automates this process, selecting the best algorithm for your data and adjusting its parameters to improve performance.

Tools like Google AutoML and AWS SageMaker excel in automating model selection and hyperparameter tuning. Google AutoML leverages neural architecture search, automatically testing a variety of models to identify the best one based on performance. This makes it faster by eliminating the need for manual model evaluation. Similarly, AWS SageMaker offers automatic model tuning, where it uses Bayesian optimization to test different hyperparameters, reducing the time spent on trial and error. SageMaker's integration with other AWS services also streamlines the process, allowing users to easily scale and deploy models once optimized.

Both platforms eliminate the guesswork in choosing and fine-tuning models, ensuring high accuracy without requiring deep technical expertise, which speeds up deployment while improving model performance.

Also Read: Why Do We Need AWS Sagemaker?

The Role of AutoML in Feature Engineering & Data Preprocessing

Feature engineering and data preprocessing are critical steps in building effective machine learning models. With AutoML, these tasks become automated. DataRobot, for example, automatically identifies the most important features in your dataset and handles tasks like missing value imputation and scaling. 

This speeds up the model development process and ensures that your data is in the best possible shape for training, freeing up your time for other strategic tasks.

AutoML in Business Intelligence & Decision-Making

AutoML is particularly useful in business intelligence and decision-making. It empowers companies to make data-driven decisions quickly without requiring extensive expertise in machine learning.

For example, Google AutoML has changed business intelligence in retail by enabling accurate, real-time demand predictions. Its ability to automatically optimize models for speed and accuracy allows retailers to adjust inventory in real-time, reducing stockouts and overstocking. This directly improves profitability. 

AWS SageMaker has also transformed the finance industry by automating credit scoring models, providing financial institutions with faster, more reliable predictions, enabling them to make better lending decisions with reduced human error.

The key advantage of AutoML in business intelligence is its ability to significantly speed up decision-making. By automating model development and optimization, companies can focus on acting on insights. They don’t have to spend time on the complex, resource-heavy process of building models from scratch. This not only cuts down costs but also improves decision-making accuracy.

Here’s a comparison of AutoML tools:

Also Read: Introduction to Deep Learning & Neural Networks with Keras

Now, let’s look at how no code AI is making AI more accessible for developers and businesses.

How is No Code AI Making AI More Accessible?

With no code platforms, even those without technical backgrounds can build, deploy, and benefit from AI models. This shift is empowering business professionals, small and medium-sized enterprises (SMEs), and even startups to leverage AI for everything from marketing to customer analytics.

How No Code AI is Empowering Business Professionals & SMEs?

No code AI platforms are allowing business professionals to create AI-powered solutions without relying on data scientists or programmers. For example, platforms like MonkeyLearn allow marketers to easily implement text analysis for customer feedback or social media sentiment without writing any code. 

In marketing, no code AI is enhancing campaign management and customer targeting. For example, platforms like Lobster allow marketing teams to automate ad targeting and A/B testing without needing deep technical skills. 

In HR, no code AI platforms help streamline candidate screening, reducing time-to-hire by automating the sorting of resumes and matching candidates to job descriptions. For customer analytics, companies use no code tools like Zoho Analytics to extract insights from data and predict customer behavior, all without a team of data scientists.

How No Code AI is Revolutionizing Predictive Analytics?

In industries like healthcare and manufacturing, no code AI tools are transforming predictive analytics. For instance, in healthcare, Medtronic uses no code platforms to analyze patient data and predict health risks, allowing for more proactive care. 

Similarly, manufacturers use no code AI tools to forecast equipment failures, preventing costly downtime and improving operational efficiency. These platforms are making it easier for companies to leverage predictive insights without needing deep expertise in data science or AI.

Also Read: Predictive Modelling in Business Analytics: Detailed Analysis

The Role of No Code AI in AI Democratization

No code AI is playing a key role in democratizing AI. The ability to create custom models for specific business needs, without needing specialized knowledge, means that AI is no longer confined to large corporations with dedicated data teams. It’s now within reach of almost anyone who understands the problem they want to solve.

Here’s how no code AI compares against traditional AI development:

Also Read: Data Science Roadmap: A 10-Step Guide to Success for Beginners and Aspiring Professionals

Now that you know how AutoML is democratizing AI development, let’s look at how companies can use it for improving business outcomes.

How Companies Are Using AutoML & No Code AI for Competitive Advantage

Companies across industries are turning to AutoML and no code AI to stay ahead of the competition. These tools simplify the machine learning process, enabling businesses to harness AI’s power without needing deep technical expertise. 

From improving fraud detection to enhancing customer experiences, AutoML and no code AI are driving innovation in ways that were once unimaginable.

AutoML in Fraud Detection & Risk Analysis

In the finance industry, AutoML is transforming how companies detect fraud and manage risk. By automating model selection and tuning, AutoML tools can quickly build and deploy models that identify suspicious activities in real-time. 

For example, PayPal uses AutoML to monitor transactions and detect anomalies that could indicate fraud, reducing false positives and improving security. This not only saves time but also allows for faster, more accurate decision-making when it matters most.

Also Read: Big Data for Fraud Detection: What You Must Know in 2025

No Code AI for Customer Segmentation & Personalization

No code AI is also revolutionizing how businesses approach customer segmentation and personalization. In retail, companies like Amazon use these platforms to tailor product recommendations to individual customers. 

With no code tools, businesses can easily segment customers based on behaviors and preferences, creating personalized marketing campaigns that increase engagement and drive sales. 

Zoho Analytics, for example, allows businesses to create detailed customer profiles without writing a single line of code, helping them deliver targeted content and offers that resonate with specific audiences.

Also Read: Segmentation in Marketing: Get Started with Effective Strategies

AI-Powered Chatbots & Virtual Assistants Built with No Code AI

No code AI is making it easier than ever for companies to build AI-powered chatbots and virtual assistants. These tools are helping businesses automate customer service and enhance user experiences. 

For example, H&M uses no code AI to develop chatbots that assist customers with product searches and orders on their website. This not only improves customer satisfaction but also reduces operational costs by automating routine inquiries. 

Similarly, Zendesk allows businesses to create custom virtual assistants that provide real-time support without any coding.

Also Read: How to create Chatbot in Python: A Detailed Guide

Now that you’re familiar with the applications of AutoML across industries, let’s look at some of the new skills AI and data professionals need.

The Changing Skillset for AI & Data Professionals in 2025

As AI technology evolves, so do the skills needed to succeed in the field. You no longer need to be a programming expert to leverage machine learning in business. These tools are making AI more accessible, and as a result, the types of roles and the skills required are changing dramatically.

New Job Roles in the Era of AutoML & No Code AI

The rise of AutoML and no code AI has created new career opportunities. These tools are enabling professionals without deep coding experience to work in AI, which has led to the emergence of new roles.

1. AI Business Analyst

This role focuses on bridging the gap between business teams and AI capabilities. AI Business Analysts work with stakeholders to identify problems, then use no code AI tools to build models that solve those problems. 

Companies like Salesforce are hiring these roles to integrate AI insights into business decision-making without the need for a data science degree.

2. No Code AI Developer

A no code AI developer specializes in using platforms like Google AutoML or AWS SageMaker to create machine learning models without writing code. 

These professionals help businesses quickly deploy AI-powered solutions, from customer segmentation to predictive analytics, making AI accessible to non-technical users.

3. AutoML Specialist

As more companies adopt AutoML platforms, there’s a growing demand for specialists who can optimize model performance. AutoML Specialists focus on using these tools to build accurate, efficient models, while fine-tuning algorithms to get the best results.

Companies like DataRobot and H2O.ai are hiring these experts to guide organizations in implementing AutoML solutions effectively.

Here are the salaries for roles that benefit from AutoML and no-code:

Source: Glassdoor

The Rise of AI Literacy & Citizen Data Scientists

AI literacy is becoming an essential skill for professionals in all industries. The rise of citizen data scientists (business professionals with a basic understanding of data science) has made it easier for non-technical workers to engage with data and create AI solutions. 

Platforms like Tableau and Power BI now integrate AI capabilities, allowing professionals in marketing, finance, and HR to make data-driven decisions without relying on data scientists for every analysis.

Also Read: The Ultimate Guide to Deep Learning Models in 2025: Types, Uses, and Beyond

Next, let’s explore one of the most important questions that developers have.

Will AutoML & No Code AI Replace Data Scientists?

With the rise of AutoML and no code AI, there’s a common concern: will these tools replace data scientists? While AutoML and no code platforms are powerful, they’re more about augmenting human capabilities than replacing jobs. 

These tools are making machine learning more accessible, but they still rely on human expertise to guide the process and interpret the results.

The Limits of No Code AI: When Do You Still Need Data Scientists?

No code AI platforms are fantastic for automating simple tasks and building basic models, but they have their limits. For example, they might work well for customer segmentation or predictive analytics in retail. 

However, for complex problems, like building models for healthcare diagnoses or autonomous driving, you still need a data scientist’s expertise. These tools can help automate workflows, but they can’t replace the depth of knowledge and creativity that data scientists bring to the table.

Data scientists are still crucial when it comes to:

• Developing custom models for unique, complex problems.
• Ensuring the model is interpreted correctly for real-world use.
• Addressing ethical concerns and biases that no code tools might overlook.

How AI & Human Expertise Can Collaborate for Better Model Performance?

AI and human expertise don’t have to work in isolation. In fact, combining the two leads to better outcomes. AutoML tools can automate the time-consuming parts of the machine learning process, such as feature selection and hyperparameter tuning, allowing data scientists to focus on more strategic aspects like problem formulation and model optimization. 

For example, Google AutoML helps data scientists quickly test and deploy models, but human expertise is needed to validate the results, ensure the model is aligned with business objectives, and fine-tune it for the best performance.

By collaborating with AI, data scientists can work more efficiently and creatively, solving complex problems faster.

Ethical & Bias Challenges in No-Code AI

While no code AI makes machine learning more accessible, it also comes with challenges, particularly around ethics and bias. Since these platforms often rely on pre-built models, they may unintentionally perpetuate biases in the data. 

For example, if a no code AI platform is trained on biased historical data, it might produce skewed or unfair results in areas like hiring or loan approvals. Data scientists are still necessary to ensure fairness, transparency, and accountability in AI models.

Data scientists play a key role in identifying and addressing biases in the data, and in ensuring that AI models are ethical and aligned with the values of the organization. They also help to interpret the results, especially in high-stakes industries like healthcare or criminal justice, where the impact of biased AI decisions can be profound.

Also Read: Bias in Data Mining: Types & How to Fix It

Here’s a comparison of use cases where AutoML works best vs. when custom AI development is needed:

Also Read: What Does a Machine Learning Engineer Do? Roles, Skills, Salaries, and More

Conclusion

According to industry experts, AI-driven automation can improve productivity by up to 40%. AutoML can help businesses achieve this by automating the development and deployment of ML models.

As businesses adopt AutoML, the demand for professionals skilled in both traditional machine learning and no code AI grows. This creates strong opportunities for career advancement in AI. Education and training are essential to mastering these tools and staying competitive.

If you're ready to take the next step and enhance your AI career, connect with upGrad’s career counseling for personalized guidance. You can also visit a nearby upGrad center for hands-on training to enhance your skills and open up new career opportunities!

Expand your expertise with the best resources available. Browse the programs below to find your ideal fit in Best Machine Learning and AI Courses Online.

Best Machine Learning and AI Courses Online

Discover in-demand Machine Learning skills to expand your expertise. Explore the programs below to find the perfect fit for your goals.

In-demand Machine Learning Skills

Discover popular AI and ML blogs and free courses to deepen your expertise. Explore the programs below to find your perfect fit.

Popular AI and ML Blogs & Free Courses