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
224.888 kürzliche Aufrufe
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % 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. 10 Stunden zum Abschließen
Englisch
Untertitel: Englisch
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 DriftModel baselineProject Scoping and DesignML Deployment Challenges
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % 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. 10 Stunden zum Abschließen
Englisch
Untertitel: Englisch
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
3 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.
3 Stunden zum Abschließen
9 Videos (Gesamt 81 min), 3 Lektüren, 2 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, 2 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, 2 Quiz
Bewertungen
Top-Bewertungen von INTRODUCTION TO MACHINE LEARNING 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 EE19. Mai 2021
Excellent course, as always! Many thanks! Great combination of theory + notebooks with practical examples.\n\nEverything is perfectly structured. I will recommend this course to everyone!
von AC8. Juni 2021
I have been working in a large payments technology company for last one year and I can vouch for all the processes Andrew beautifully summarised. It does help a lot working in the industry.
von UU4. Juni 2021
The content of this course has been especially useful for me. I wish there were more emphasis on the tools recommendation as well, but the theoretical knowledge was just fine. Thank you!
Über 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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