Machine Learning in Production
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
68/100
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- What the provider tells you
- 32/45
- Who stands behind it
- 20/35
- How complete the listing is
- 16/20
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What you'll learn
- understanding of the ML lifecycle
- strategies for addressing modeling challenges
- knowledge of data definition and baseline techniques
- skills for deploying machine learning models
- insight into error analysis and concept drift
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