Introduction to Machine Learning in Production
Machine Learning Engineering for Production (MLOps),
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process 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 production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline
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Rating | Not enough ratings |
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Length | 4 weeks |
Effort | At the rate of 5 hours a week, it typically takes 3 weeks to complete this course. |
Starts | Jun 26 (47 weeks ago) |
Cost | $49 |
From | deeplearning.ai via Coursera |
Instructors | Andrew Ng, Cristian Bartolomé Arámburu |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Science Software Development Machine Learning |
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Rating | Not enough ratings |
---|---|
Length | 4 weeks |
Effort | At the rate of 5 hours a week, it typically takes 3 weeks to complete this course. |
Starts | Jun 26 (47 weeks ago) |
Cost | $49 |
From | deeplearning.ai via Coursera |
Instructors | Andrew Ng, Cristian Bartolomé Arámburu |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Science Software Development Machine Learning |
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