Harvard MLSys Book Reading : Machine Learning Systems Workflows
Details
The Machine Learning Systems Workflow
Source: Harvard MLSys Book - Chapter: Workflow
If ML developers are like astronauts exploring new frontiers, ML systems engineers are the rocket scientists and mission control specialists who build the engines and keep the mission on track.
In our next bi-weekly session, we are diving into the Workflow chapter of the recently released Harvard MLSys book.
Key Discussion Points:
- The ML Lifecycle vs. Software Lifecycle: Why traditional CI/CD isn't enough when data is a first-class citizen.
- Pipeline Orchestration: Transitioning from experimental notebooks to automated, reproducible production workflows.
- The Iterative Loop: Navigating the complex feedback loops between data engineering, model training, and deployment.
- Systemic Objectives: How to balance performance, scalability, and observability in a live environment.
Join us to discuss how to move from building "cool models" to engineering "dependable systems."