Elevate your career in machine learning by mastering the complete end-to-end process—from infrastructure setup to model deployment. This 8-week course is designed to transform you from a model-focused data scientist into a full-fledged ML engineer capable of managing the entire ML lifecycle. You can choose between live cohort learning or a self-paced format.
Course Outline:
- Week 1: Setup and management of Docker, Kubernetes, and CI/CD pipelines.
- Week 2: Advanced data storage, processing, versioning, and labeling techniques, including Retrieval-Augmented Generation (RAG).
- Week 3: Designing, executing, and optimizing experiments for maximum model efficiency.
- Week 4: Automation and orchestration of ML processes using Dagster, Kubeflow, and Airflow.
- Weeks 5-6: Deploying, scaling, and maintaining models, including work with large language models (LLMs).
- Week 7: Monitoring, supporting, and managing model quality, including tracking data drift and LLM monitoring.
- Week 8: Vendor selection and platform integration (AWS SageMaker, GCP Vertex AI) and discussion of current trends.
Final Project:
You’ll complete a comprehensive end-to-end ML project and present it, applying all the knowledge gained during the course.
Learning Outcomes:
- A capstone project demonstrating your ability to solve real-world problems from start to finish.
- A design document detailing the architecture and description of your ML system, refined throughout the course.
- Reusable code templates providing practical solutions and frameworks for future projects.