Machine Learning Engineering

Machine Learning Engineering Learning Journey

Our Machine Learning Engineering Training Courses

Ideal starting points for aspiring MLEs or more advanced MLEs that wish to deep dive into specific subjects.

Following the boom of data and AI, one of the biggest challenges companies face is moving from early-stage concepts to robust applications that can actually deliver value in production. Solving this challenge requires having people that combine strong engineering skills with a keen affinity for machine learning: machine learning engineers (MLEs). However, with the explosion in available ML technologies and frameworks, becoming an MLE is far from trivial. That’s why we’ve designed several MLE learning journeys that focus on key concepts and core technologies, providing ideal starting points for aspiring MLEs or more advanced MLEs that wish to deep dive into specific subjects.

Machine Learning Engineering Learning Journeys

Machine Learning Engineering Learning Journeys

How do you become a machine learning engineer? Start here! We’ve put together a carefully crafted learning journey for data engineers. Knowing engineers love to figure things out on their own, we packed the program with opportunities to learn, hands-on, by solving real-life situations. Plus, there’s plenty of practical philosophy, too.

Focusing on the fundamentals, we’ll teach you how to build well-structured, production-ready ML applications in Python and how to run these using Docker and basic cloud services. Moving towards more advanced topics, we’ll dive deeper into how to use MLOps practices to automatically deploy and retrain ML applications in the Cloud and on Kubernetes, using CI/CD and infrastructure-as-code to make sure our full stack is reproducible. Finally, in our specializations we do deep dives into specific technologies such as Airflow, DBT, Spark, etc. to make you the go-to expert in these powerful tools.

Learning Journey

Fundamentals Machine Learning Engineering

Learning Goals for the fundamentals of Machine Learning Engineering


  • Understand the fundamentals of machine learning engineering
  • Build well-structured ML products in Python with guidance
  • Know how containerization works and what it simplifies

Training Courses

  • Advanced Data Science with Python / 2-days – Public & In-Company
    Packed with best practices, models, code, algorithms, and a framework to improve your projects, you’ll rapidly advance your skills by immersing yourself in two days of nothing but data science, machine learning, and Python.
  • Production Ready Machine Learning Training / 2-daysIn-Company
    Learn the best practice to bring your machine learning models into production with this practical training about how software engineers manage their code, how more advanced Python features can make your life easier, and how to go from notebooks to packages.
  • Docker Training / 1-dayPublic & In-Company
    How many times have you heard the word “container” during an IT conversation? Exactly! And that’s because containerised solutions are here to stay. But what are containers? How do we build them?
  • Google Cloud Platform Fundamentals: Big Data & Machine Learning Training / 1-dayPublic & In-Company
    You will learn to process Big Data at scale for analytics and Machine Learning. You will explore the fundamentals of building new machine learning models and creating streaming data pipelines and dashboards.

Learning Journey

Advanced Machine Learning Engineering

Learning Goals for the Advanced Machine Learning Engineering Learning Journey


  • Design, build and deploy robust end-to-end ML solutions
  • Implement ML patterns and MLOps strategies on Kubernetes or the Cloud
  • Manage and deploy infrastructure-as-code using Terraform

Training Courses

  • Kubernetes / 1 dayPublic & In-Company
    How many times have you heard the word “container” during an IT conversation? Exactly! And that’s because containerised solutions are here to stay. How do we place our solutions in Docker containers? And, most importantly: how do we run them?
  • MLOps / 2-dayPublic & In-Company
    After building a robust local solution for your ML model, one of the biggest challenges is to actually get it running in a production environment. In this training, we’ll dive deeper into the concepts of MLOps and how these can be applied in practice to move your models beyond the laptop. With hands-on exercises, we’ll explore the full life cycle of exposing your model to end-users as an API, monitoring their performance and automatically retraining models with ML pipelines.
  • Data Science with Spark Training / 3-daysPublic & In-Company
    Apache Spark is a powerful, open-source processing engine built around speed, ease of use, and advanced analytics. In this course, you will learn to unlock its full potential and master this challenging tool.

Specializations in Machine Learning Engineering

If you have kick-started your Machine Learning Engineering career with our Fundamentals and/or Advanced learning journey, or if you are already an advanced Machine Learning Engineer, you might consider becoming more specialized in a specific topic. In this way, you make yourself a highly sought after expert combined with the big benefit of having to work solely on the aspects you love the most within the data profession.

Learning goals for specialization can be:

  • Become the go-to expert in specific topics such as deep learning, streaming data and experimentation
  • Dive deep into specialized technologies such as Airflow, DBT and Spark and understand how these tools can super-charge your work

Check out our speciality courses

Our specialization courses:

  • DBT / 3 half-daysOnlineYou will learn about modelling strategies, automated testing and performance optimization. Including verified dbt Learn Certificate.
  • Deep Learning / 3 daysOnlineThis training delivers the right mix of theory and hands-on practice. You will gain the practical skills you need to implement your own Deep Learning algorithms and learn to apply them to unstructured data, such as images or text.
  • Machine learning explainability / 1 dayOnlineGet a toolbox of interpretability techniques that you can use in your daily work, understanding when and how you can and should use it.
  • Streaming architecture at scale / 2 daysOnlineThis training focuses on two key players on the streaming-side of data processing: Apache Kafka and Apache Spark!

Have any questions?

Contact Diego Teunissen, our Training Advisor if you want to know more. He’ll be happy to help you!