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Machine learning engineering

What is machine learning engineering?

Machine learning engineering is about creating real business value (true impact) through data. The biggest challenge is rarely training a model — it’s making sure it actually works in practice and continues to deliver value. Successful machine learning projects are those where the model isn’t just a tech experiment, but a tool that creates genuine commercial advantage, implemented and usable in the business.

Typical machine learning tasks we work on include:

  • Development of predictive models, such as customer behavior analysis or fraud detection
  • Feature engineering and data processing to make sure data is meaningful for the model
  • Automation of ML pipelines, so models can be trained and updated continuously without manual effort
  • Deployment of models into production with a focus on scalability, performance, and stability
  • Monitoring and evaluation to ensure models maintain precision over time (drift detection)

If your organization works with large amounts of data but struggles to identify concrete and operational use cases, reach out — we’re happy to have an informal conversation.

What types of projects do we do?

We develop machine learning solutions that can be implemented in real production environments. In our experience, many AI projects fail because they weren’t designed with real-world operations in mind — and therefore never deliver true impact. Our mission is to do it differently. That’s why our focus is always business-first: the commercial goal comes first, and machine learning is just one of the tools to get there.

Examples of relevant projects include:

  • Dynamic pricing based on demand and market trends
  • Automatic categorization of customer inquiries for faster response
  • Anomaly detection in financial transactions to identify fraud
  • Predictive maintenance to reduce downtime in production facilities

We’re not tied to any specific technologies or frameworks — we build project teams with both in-house specialists and our validated network of expert subcontractors. That means we always choose the solution that best fits your specific case.

What technologies do we work with?

Our approach is pragmatic: we choose the technology that best solves the task and can realistically be maintained in your organization. The technologies we most commonly work with include:

  • TensorFlow & PyTorch for deep learning models
  • Scikit-learn for classical machine learning
  • MLflow & Kubeflow for model management and automation
  • Amazon SageMaker & Google Vertex AI for cloud-based machine learning
  • Apache Spark MLlib for analysis of massive datasets

When do we train our own models, and when do we use standard models?

Many organizations are tempted to build too much from scratch because it’s technically challenging and interesting. But in most cases, it’s more efficient to use existing models and adapt them to your needs. We have the expertise to make a well-qualified decision in your specific case.

We always assess what creates the most value:

  • - Using pre-trained models: When off-the-shelf models already solve the problem well
  • - Fine-tuning existing models: When performance can be improved using your own data
  • - Training from scratch: Only if there’s a unique need existing models can’t meet

Our mindset is that AI should first and foremost be a business decision — not a tech exercise.

When do machine learning projects fail — and when do they succeed?

AI success rarely comes down to technology alone — it’s about business understanding and execution. The most common reasons ML projects fail are:

  • - Unclear goals: Without a clear business case, the project often ends as an experiment with no real value
  • - Poor data quality: AI models are only as good as the data they’re trained on
  • - Lack of operational integration: A model without a clear implementation plan rarely gets used
  • - Too much complexity too early: A simple model that works is better than a complex one that never goes live

We make sure machine learning projects don’t end as interesting pilots — but become real solutions that drive business value.

Do you have a relevant project?

If you’re considering using AI in your organization — or have an existing AI project that hasn’t delivered the expected value — let’s have a no-strings-attached conversation.

Fill out the form below and we’ll give you our honest assessment of your opportunities.