Machine Learning in Production (Free Online Course) : “Machine Learning in Production” focuses on the practical application of machine learning models in real-world environments, moving beyond the development phase to deployment and scaling. This involves ensuring that models perform efficiently, remain robust, and adapt to changing data patterns once integrated into production systems. Key aspects include data pipeline automation, model monitoring, version control, and performance optimization. By implementing machine learning in production, businesses can leverage data-driven insights for continuous improvement in areas like recommendation systems, fraud detection, and predictive analytics. This field bridges the gap between research and operations, enabling organizations to turn machine learning models into valuable, scalable solutions.
What You Will Learn?
| Identify key components of the ML project lifecycle, pipeline & select the best deployment & monitoring patterns for different production scenarios. |
| Optimize model performance and metrics by prioritizing disproportionately important examples that represent key slices of a dataset. |
| Solve production challenges regarding structured, unstructured, small, and big data, how label consistency is essential, and how you can improve it. |
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Concepts Covered in the Course
Week 1: Overview of the ML Lifecycle and Deployment
Week 2: Modeling Challenges and Strategies
Week 3: Data Definition and Baseline
Skills You Will Gain
- Concept Drift
- ML Deployment Challenges
- Human-level Performance (HLP)
- Project Scoping and Design
- Model baseline
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