Machine Learning in Production

Machine Learning in Production

Take your ML career to the next level by mastering the complete end-to-end process, from infrastructure setup to model deployment

In just 8 weeks, you’ll transform from being a data scientist focused on modeling to a professional who can handle the entire ML lifecycle. Choose between the live cohort, or the asynchronous course to learn at your own pace.

Week 1: Learn to set up and manage Docker, Kubernetes, and CI/CD pipelines.

Week 2: Master advanced data storage, processing, versioning, labeling techniques, and Retrieval-Augmented Generation (RAG).

Week 3: Structure, run, and optimize experiments to ensure peak model performance.

Week 4: Streamline workflows with powerful tools like Dagster, Kubeflow and AirFlow.

Week 5-6: Implement, scale, and serve your models using the latest strategies, including handling Large Language Models (LLMs).

Week 7: Keep your models performing at their best with robust monitoring and maintenance strategies, including tools and techniques for monitoring LLMs and managing data drift.

Week 8: Navigate the complexities of vendor selection and platform integration with a focus on AWS SageMaker, GCP Vertex AI, and the latest trends.

Capstone Project Presentation – Apply everything you’ve learned to complete an end-to-end ML project and present it.

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