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.
이 강좌는 Machine Learning Engineering for Production (MLOps) 특화 과정의 일부입니다.
제공자:


이 강좌에 대하여
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
배울 내용
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.
귀하가 습득할 기술
- 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)
제공자:

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
강의 계획표 - 이 강좌에서 배울 내용
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.
검토
- 5 stars84.96%
- 4 stars12.49%
- 3 stars1.77%
- 2 stars0.55%
- 1 star0.20%
INTRODUCTION TO MACHINE LEARNING IN PRODUCTION의 최상위 리뷰
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.
Excellent course, as always! Many thanks!
Great combination of theory + notebooks with practical examples.
Everything is perfectly structured. I will recommend this course to everyone!
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.
A great course that Andrew provided to fill the gap between machine learning/AI in academia (model-centric approach) and industry production (data-centric approach).
Machine Learning Engineering for Production (MLOps) 특화 과정 정보
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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