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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Machine Learning Engineering for Production (MLOps)
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Über diesen Kurs
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Was Sie lernen werden
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.
Kompetenzen, die Sie erwerben
- 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)
Lehrplan - Was Sie in diesem Kurs lernen werden
Week 1: Overview of the ML Lifecycle and Deployment
Week 2: Select and Train a Model
Week 3: Data Definition and Baseline
Bewertungen
- 5 stars84,41 %
- 4 stars12,98 %
- 3 stars1,89 %
- 2 stars0,44 %
- 1 star0,26 %
Top-Bewertungen von INTRODUCTION TO MACHINE LEARNING 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.
A course that extends your vision about machine learning in production and helps you to understand that training a model and getting it with a good accurate is not enough
Good intro on key concept in MLOps. Would recommend it to anyone who is stepping into this field as well as for ML Hobbists to understand the main challenges of a ML production system
Practical and well-structured advices throughout the lifecycle of ML. Examples from real world problems & experiences make the advices more tangible and helps to reflect on own problems.
Über den Spezialisierung Machine Learning Engineering for Production (MLOps)

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