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.
Этот курс входит в специализацию ''Специализация Machine Learning Engineering for Production (MLOps)'
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Об этом курсе
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Чему вы научитесь
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.
Приобретаемые навыки
- 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.
Программа курса: что вы изучите
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.
Рецензии
- 5 stars84,92 %
- 4 stars12,52 %
- 3 stars1,84 %
- 2 stars0,48 %
- 1 star0,21 %
Лучшие отзывы о курсе INTRODUCTION TO MACHINE LEARNING IN PRODUCTION
I have been involved with deep learning for more than 5 years (in academia), nevertheless learned a lot already. I am very curious about the next courses. Thanks for putting together this course!
Concise and straight to the point! It is a good and broad introduction to the topic. I'm confident it has prepared me well for the next course of this specialization.
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.
Excellent course, you learn about the fundamentals of MLOps. A recommended course if you want to understand the life cycle of a Machine Learning algorithm in production.
Специализация Machine Learning Engineering for Production (MLOps): общие сведения
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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Is financial aid available?
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