About the event
The event is a 3-half-days:
- 8 June 09:00-11:00
- 9 June 9:00-11:00
- 11 June 9:00-11:00
MLOps (Machine Learning Operations) is the set of practices that combines machine learning, software engineering, and DevOps to reliably build, deploy, monitor, and maintain ML models in production. It focuses on automation, reproducibility, and governance across the entire ML lifecycle. Thus, MLOps moves beyond individual steps and algorithms to provide a solid structure for ML development. In this event, we will guide you through the whole MLOps journey, examine its individual steps and provide a holistic view of the entire pipeline. The event combines interactive lessons with a hands-on lab where the participants will apply ideas from the course in practice.
Who is this for?
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Data scientists, developers with basic ML knowledge, or more in general practitioners who wish to expand their understanding of MLOps best practices.
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Engineers who need to incorporate more ML/AI development in their work and wish to approach this task in a well-structured way.
Key takeaways for participants
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Understand the high-level processes involved in MLOps, and in particular how MLOps provide a cohesive structure beyond individual processing steps and algorithms.
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Reflect on how the real-world development and deployment of ML-powered applications encompasses much more than the individual ML algorithms.
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Learn the key steps in the MLOps process, the key questions to ask and pitfalls to avoid in order to successfully achieve these steps, from problem definition to model deployment and continuous monitoring.
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Focus on the importance of automation, robustness and reproducibility in MLOps.
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Hands-on experience with implementing all steps of the MLOps process using open-source tools (git, MLFlow).
Prerequisites
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Basic ML methods knowledge and experience with Python programming.