Back to Machine Learning in Production
Learner Reviews & Feedback for Machine Learning in Production by DeepLearning.AI
3,226 ratings
About the 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
Top reviews
RG
Jun 5, 2021
really a great course. It'll really change your way of thinking ML in production use and will help you better understand how can you leverage the power of ML in a way that I'll really create a value
DT
Aug 15, 2021
Excellent course, as always. Very well explain for both Data Sicientist, Software engineer and Manager (with some basics undertsanding of ML). One of these courses that Data Sientist should follow.
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