From the course: Google Cloud Professional Machine Learning Engineer Cert Prep

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Architecting a ML solution: Next steps

Architecting a ML solution: Next steps - Google Cloud Platform Tutorial

From the course: Google Cloud Professional Machine Learning Engineer Cert Prep

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Architecting a ML solution: Next steps

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GCP Professional Machine Learning Engineer. Let's talk through this conclusion. Course 2: Architecting ML Solutions. First up, in Course 2, we talked about a continuous delivery in machine learning. This means ensuring rapid, reliable, and consistent deployment of ML models. We also talked about containerizing ML microservices such as creating modular, scalable, and maintainable ML services and using tools like Docker and Kubernetes. We also talked about reproducible workflows, including consistency and repeatability of ML workflows. Continuous integration, automating the integration of code changes from multiple contributors. We also talked through heavy versus light MLOps. This means understanding the trade offs between resource intensive and lightweight MLOps solutions. We also dove into what is the difference between a feature store versus a data warehouse and compared the purposes and benefits of a feature store and a data…

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