Despite growing teams and budgets, very few machine learning models reach the production stage. This session discussed:
- What is model productionization? What makes it difficult?
- How does it relate to MLOps?
- How should one plan for production ML?
- What kinds of systems and process should be in place for ensuring successful delivery?
- What does the team structure and skillset look like?
- Aditya Patel, Director, Data Science of Glance (InMobi; Bangalore)
- Nischal HP @nischalhp, VP of Technology, Omnius (Berlin).
- Uncertainty cannot be eliminated in the performance of the model. So extensive testing, circuit breakers, fallback mechanisms, and incremental deployment mechanisms are critical.
- ML development and deployment should be thought about within the context of the product development.
- Many of the systems, methods and ideas are applicable here including versioning but need to be repurposed to suit the context
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Previous session: The first session was held on 20 May. Summary of the session is available here: https://hasgeek.com/fifthelephant/making-data-science-work-1/
About the series producer: The Fifth Elephant is platform for practitioners working with data (engineering, to application of data science for different use cases) to showcase their work and to collaborate.
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