MLOps: Continuous delivery and automation pipelines in machine learning
This document discusses techniques for implementing and automating continuous integration (CI), continuous delivery (CD), and continuous training (CT) for machine learning (ML) systems.
Data science and ML are becoming core capabilities for solving complex real-world problems, transforming industries, and delivering value in all domains. Currently, the ingredients for applying effective ML are available to you:
Inexpensive on-demand compute resources
Specialized accelerators for ML on various cloud platforms
Rapid advances in different ML research fields (such as computer vision, natural language understanding, and recommendations AI systems).
Therefore, many businesses are investing in their data science teams and ML capabilities to develop predictive models that can deliver business value to their users.
This document is for data scientists and ML engineers who want to apply DevOps principles to ML systems (MLOps). MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops). Practicing MLOps means that you advocate for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment and infrastructure management.
Data scientists can implement and train an ML model with predictive performance on an offline holdout dataset, given relevant training data for their use case. However, the real challenge isn't building an ML model, the challenge is building an integrated ML system and to continuously operate it in production. With the long history of production ML services at Google, we've learned that there can be many pitfalls in operating ML-based systems in production. Some of these pitfalls are summarized in Machine Learning: The high-interest credit card of technical debt.
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