AI for Machine Learning: What Works, What Doesn't, and Lessons Learned

We all hear that "AI is everywhere." And it's true. 

 

Moreover, here’s a twist you might not have thought about: AI in machine learning development.

 

Yes, at Onix, we use AI to build better AI.

 

Sounds strange? Yes, a little bit. But don't rush to conclusions.

 

In short, AI helps us experiment and do things faster by automating routine tasks.

 

But let's dig deeper to clarify all nuances. In this article, we’ll reveal our machine learning product development and share honest insights from our developers.

 

AI vs. ML: Let's Explain Before We Begin

Before we continue, let's clear up some vital nuance:

 

• AI is a broad term. It allows us to build machines that think and act like humans.
• ML is a branch of AI in which computers learn patterns from data instead of following hard-coded instructions.

 

And here’s where it gets interesting:

At Onix, our specialists use general-purpose AI (LLM in machine learning or AI assistants) as tools for creating highly specialized ML models to solve various business problems.

 

In other words,

 

• The first AI helps us prepare, plan, and prototype.
• The second AI finishes the specialized product designed to solve a specific business challenge.

 

Learn more: How to Build a Machine Learning Model for Your Business

 

Benefits of Using AI in Machine Learning Product Development

When we talk about using AI for machine learning development process, the benefit that immediately comes to mind is speed.

 

And that's true.

 

However, speed is not the only advantage.

 

Faster planning and decision-making 

Using AI for ML project planning helps engineers brainstorm features, test ideas, and analyze data quality just in minutes.

 

Accelerated prototyping

Experts can use AI to create early versions of LLMs for prototyping ML products.

 

"AI lets us test hypotheses much quicker. We can prototype a feature, run simulated user interactions, and see the results in a fraction of the time. But I’m always careful to verify every AI suggestion, because speed without accuracy can be dangerous."

 

Oleksii Sheremet
ML Department Tech Lead at Onix

 

Reduced routine tasks

Leveraging AI tools for machine learning engineers means more time for creativity and less time on repetitive coding tasks. AI handles routine tasks, while developers can focus on what matters. 

 

High data quality and better preparation speed

AI tools offer automated scripts for cleaning, transforming, and normalizing datasets. This allows you to avoid the human factor and achieve a more stable quality.

 

Inside Our Workflow: How We Use AI to Build ML Products Faster

At Onix, AI does not replace our developers. We combine AI and human expertise to make faster iterations, reduce bottlenecks, and spend more energy solving complex business problems.

 

"AI does the heavy lifting with datasets, sometimes automates testing, and helps us explore ideas faster. But the creativity, the problem-solving, and the responsibility for the outcome that’s still 100% human."

 

Oleksii Sheremet
ML Department Tech Lead at Onix

 

The development of any ML solution goes through several key stages. Here is how using Generative AI in machine learning helps us at each of them:

 

Planning, problem definition, and data analysis

This is a fundamental stage that lays the foundation for a future solution. At this stage, we:

 

• define the problem,
• check if ML is the right solution,
• and understand the data we have to work with. 

 

AI at this stage is an assistant that helps with the following tasks:

 

Hypothesis generation.

Using models like ChatGPT and Claude, we brainstorm, analyze the problem description, and generate ideas about potential features that can affect the accuracy of the future model. The ability to quickly search and summarize the results is vital.

 

Automated data quality checks.

AI tools like Windsurf and PyCharm help automate data availability, relevance, and quality checks. These tools quickly create ETL scripts, verify data quality, and facilitate initial exploratory data analysis (EDA).

 

Fast data analysis.

AI tools like PyCharm help our developers instantly write scripts for visualization and statistical data analysis, flag anomalies, and identify missing values, allowing us to understand what’s happening in the data quickly.

 

Data preparation and feature engineering

Raw data is rarely ready for analysis. It may contain errors, omissions, or unstructured information. Without careful preparation, these issues can compromise the accuracy and reliability of any ML model.

 

At this stage, we transform raw data into a clean and structured set suitable for training models, as no model can perform well if trained on poor-quality data.

 

AI at this stage acts as a data processing assistant that can help us with the following tasks:

 

Automating code writing with an IDE.

For example, we use JetBrains AI Assistant, which offers in-editor code generation:

 

• creating data cleaning functions,
• adding Python-type hints, 
• and intelligent suggestions for ETL directly in the IDE.

 

Using local models for code generation.

Models like Codestral and DeepSeek-Coder, deployed on internal infrastructure, can generate code for simple standard data cleaning operations, normalization, and transformation, reducing our routine.

 

Processing sensitive data.

We use on-premises LLMs, such as Mistral, Codestral, or Phi-4, within our secure infrastructure to process personal or corporate data that cannot leave the internal network. No external API requests. No risk of leaks.

 

Read also: How to Build Machine Learning Teams for AI Projects

 

Model training and experiments

This stage involves directly training the models and running a series of experiments with different architectures and hyperparameters.

 

Here is how AI-powered tools automate some of our routine tasks:

 

Creation of code templates.
AI tools generate simple, basic, template code for training loops, loss functions, and metrics in PyTorch or TensorFlow frameworks.

 

Debugging.
If the model is training incorrectly, we can feed a snippet of code and a description of the problem to an on-premises or cloud-based AI model and get recommendations for how to fix it. This can significantly reduce the time spent troubleshooting.

 

Experiment tracking.
In some cases, where model retraining is frequent, we use MLflow Tracking, which provides an API and UI to log parameters, metrics, artifacts, and code versions during each experiment run.

 

After that, AI assistants (mostly based on local LLMs) analyze the saved results, highlight trends, and suggest ideas for subsequent iterations without manually reviewing all the logs.

 

The Impact of AI on Our Workflow: Efficiency Gains in Numbers

Let's see how AI for machine learning works in practice and consider its effectiveness in numbers.

 

 

When We Say No to AI

At Onix, we’re big fans of AI.

 

But!

 

The Onix team integrates AI only where it makes sense. We never sacrifice quality, security, or stability just for speed. AI is not our one-size-fits-all solution.

 

Every project is different, and how we use AI depends on the codebase, task complexity, team workflows, and project goals.

 

"Some projects don’t actually benefit from AI, and that’s totally fine. We don’t throw it into the mix just because it sounds cool. If a task needs precision, transparency, or has a lot of sensitive data, we’ll often stick to traditional methods and human oversight."

 

Olexandr Gergardt
Head of ML department at Onix

 

We say no to AI when:

 

• Real-time systems require split-second response, such as in aviation or industrial safety, where even milliseconds matter.
• It comes to medical applications with life-critical decisions that require a human in the loop.
• We work in highly regulated industries that require complete transparency and legal explanations of AI decisions.

 

There's no doubt that artificial intelligence is a powerful tool, but if you trust it too much when coding, things can go wrong quickly.

 

Our goal at Onix is to leverage AI responsibly to build reliable and secure software.

 

Our Honest View on Risks of Using AI

While AI offers certain benefits, we can’t ignore the risks it has:

 

Security risks

AI-generated code may contain hidden vulnerabilities or dangerous actions that require careful human review to prevent violations.

 

Ethical and privacy considerations

AI systems that process sensitive personal, medical, or corporate data may inadvertently disclose it through insecure storage, data leakage, or insufficient anonymization. 

 

Regulatory requirements like GDPR or HIPAA demand:

 

• strict consent,
• data minimization,
• and transparency.

 

Such non-compliance can lead to legal penalties, ethical concerns, and reputational risks.

 

Intellectual property concerns

There is uncertainty about the ownership of AI-generated code and questions about licensing.

 

Maintenance issues

AI-generated solutions often work for basic tasks but frequently become a source of unforeseen bugs when faced with unusual scenarios and edge cases, requiring more time for fixes and long-term maintenance costs.

 

Read also: What Are the Risks and Limitations of Machine Learning?

 

Summing Up

AI is a powerful assistant for our ML team. 

 

However, artificial intelligence isn't a replacement for our human expertise at Onix. But we believe specialists who know how to use AI in ML product development become more efficient and competitive.

 

Our promise to clients is simple: AI with a human guarantee where innovation meets responsibility, and quality always wins over speed.

 

Want to see how responsible AI use can accelerate your product launch without sacrificing quality? Talk to us.

 

FAQs

 

How does Onix ensure the quality and reliability of AI-assisted ML solutions?

We combine AI tools with rigorous human review, thorough testing, and continuous monitoring to maintain the highest quality and reliability standards.

 

How can AI speed up the development of my machine learning product?

AI automates routine tasks, accelerates data analysis, and helps prototype models faster—cutting development time significantly without sacrificing accuracy.

 

What AI tools and technologies does Onix use in its ML projects?

We leverage cloud-based and locally hosted large language models to balance power and privacy.

 

Our toolkit includes generative AI for code generation, data preprocessing, and rapid prototyping, alongside specialized AI code assistants integrated into IDEs like PyCharm and Windsurf. We tailor the mix of tools to each project’s requirements, ensuring maximum efficiency without compromising security or control.

 

Can AI replace human experts in ML product development?

No. AI is a powerful assistant, but it doesn’t replace human creativity, judgment, and expertise, which are essential for successful ML projects.

 

How do you ensure ethical AI use throughout the development process?

Ethical AI is a core priority for us. We adhere to strict data consent protocols, ensuring users understand and agree to how their data is used. We document AI decisions clearly and strive to explain model behaviors where possible.

 

We actively work to identify and mitigate bias in training data and models, and fully comply with regulations like GDPR and HIPAA. This holistic approach ensures our AI solutions are fair, trustworthy, and respect user rights.