Machine Learning in Production
By External Provider
About this course
In this Machine Learning in Production course, you will build intuition about designing a production ML system end-to-end: project scoping, data needs, modeling strategies, and deployment patterns and technologies. You will learn strategies for addressing common challenges in production like establishing a model baseline, addressing concept drift, and performing error analysis. You’ll follow a framework for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need experience preparing your projects for deployment as well. Machine learning engineering for production combines the foundational concepts of machine learning with the skills and best practices of modern software development necessary to successfully deploy and maintain ML systems in real-world environments. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Modeling Challenges and Strategies Week 3: Data Definition and Baseline
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.