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.
本课程是 Machine Learning Engineering for Production (MLOps) 专项课程 专项课程的一部分
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课程信息
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
您将学到的内容有
Identify the key components of the ML lifecycle and pipeline and compare the ML modeling iterative cycle with the ML product deployment cycle.
Understand how performance on a small set of disproportionately important examples may be more crucial than performance on the majority of examples.
Solve problems for structured, unstructured, small, and big data. Understand why label consistency is essential and how you can improve it.
您将获得的技能
- Human-level Performance (HLP)
- Concept Drift
- Model baseline
- Project Scoping and Design
- ML Deployment Challenges
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
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deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
授课大纲 - 您将从这门课程中学到什么
Week 1: Overview of the ML Lifecycle and Deployment
This week covers a quick introduction to machine learning production systems focusing on their requirements and challenges. Next, the week focuses on deploying production systems and what is needed to do so robustly while facing constantly changing data.
Week 2: Select and Train a Model
This week is about model strategies and key challenges in model development. It covers error analysis and strategies to work with different data types. It also addresses how to cope with class imbalance and highly skewed data sets.
Week 3: Data Definition and Baseline
This week is all about working with different data types and ensuring label consistency for classification problems. This leads to establishing a performance baseline for your model and discussing strategies to improve it given your time and resources constraints.
审阅
- 5 stars84.83%
- 4 stars12.57%
- 3 stars1.83%
- 2 stars0.51%
- 1 star0.22%
来自INTRODUCTION TO MACHINE LEARNING IN PRODUCTION的热门评论
Very useful discussions and views. Great reflections on the value of data in the full ML cycle and the real challenges of putting a ML system in production.
Andew Ng is truly a world leader in the field, the way he approaches the subject and the explanations he gives are truly unparalleled. It always a pleasure taking a course he instructs.
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
I would recommend this course to anyone who has to implement models in production. It is an introductory course but it does have a few key concepts that are good to keep in mind.
关于 Machine Learning Engineering for Production (MLOps) 专项课程
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.

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