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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176.983 kürzliche Aufrufe
Flexible Fristen
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Zertifikat zur Vorlage
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100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Fortgeschritten“
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Ca. 12 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Französisch
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
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Fortgeschritten“
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Ca. 12 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Französisch
von
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Lehrplan - Was Sie in diesem Kurs lernen werden
5 Stunden zum Abschließen
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.
5 Stunden zum Abschließen
9 Videos (Gesamt 81 min), 2 Lektüren, 4 Quiz
3 Stunden zum Abschließen
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.
3 Stunden zum Abschließen
16 Videos (Gesamt 107 min), 1 Lektüre, 3 Quiz
4 Stunden zum Abschließen
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.
4 Stunden zum Abschließen
16 Videos (Gesamt 128 min), 3 Lektüren, 3 Quiz
Bewertungen
- 5 stars85,11Â %
- 4 stars12,35Â %
- 3 stars1,80Â %
- 2 stars0,51Â %
- 1 star0,20Â %
Top-Bewertungen von INTRODUCTION TO MACHINE LEARNING IN PRODUCTION
von DG30. Aug. 2021
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.
von RG4. Juni 2021
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
von AN11. Juni 2022
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
von NB11. Juli 2021
Introduces you to the basics of MLOps in a well paced mannar. Would request to add more examples of structured data sets, as many companies usually are dealing with the related problems.
Ãœber den Spezialisierung Machine Learning Engineering for Production (MLOps)
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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