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.
Ce cours fait partie de la Spécialisation Machine Learning Engineering for Production (MLOps)
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Cours 1 sur 4 dans le
Niveau avancé
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Approx. 10 heures pour terminer
Anglais
Sous-titres : Anglais, Français
Ce que vous allez apprendre
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.
Compétences que vous acquerrez
- Human-level Performance (HLP)
- Concept Drift
- Model baseline
- Project Scoping and Design
- ML Deployment Challenges
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 4 dans le
Niveau avancé
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Approx. 10 heures pour terminer
Anglais
Sous-titres : Anglais, Français
Offert par
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programme de cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
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 heures pour terminer
9 vidéos (Total 81 min), 3 lectures, 3 quiz
3 heures pour terminer
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 heures pour terminer
16 vidéos (Total 107 min), 1 lecture, 3 quiz
4 heures pour terminer
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 heures pour terminer
16 vidéos (Total 128 min), 3 lectures, 3 quiz
Avis
- 5 stars84,82 %
- 4 stars12,56 %
- 3 stars1,85 %
- 2 stars0,52 %
- 1 star0,23 %
Meilleurs avis pour INTRODUCTION TO MACHINE LEARNING IN PRODUCTION
par RG4 juin 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
par SA24 juin 2021
I like the acknowledgement of the importance of data quality. Machine learning is much more than just training models. Real benefits can only be achieved when moving to real life data
par JF12 juin 2021
The course helped both validate what I knew about the topic and update me about many new trends/tools via high quality references + first hand experences from the instructor.
par RP2 août 2021
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).
À propos du Spécialisation 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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