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.
Este curso faz parte do Programa de cursos integrados Machine Learning Engineering for Production (MLOps)
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Informações sobre o curso
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Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
NÃvel avançado
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 12 horas para completar
Inglês
Legendas: Inglês, Francês
O que você vai aprender
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.
Habilidades que você terá
- Human-level Performance (HLP)
- Concept Drift
- Model baseline
- Project Scoping and Design
- ML Deployment Challenges
Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
NÃvel avançado
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 12 horas para completar
Inglês
Legendas: Inglês, Francês
oferecido por
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programa - O que você aprenderá com este curso
5 horas para concluir
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 horas para concluir
9 vÃdeos (Total 81 mÃn.), 2 leituras, 4 testes
3 horas para concluir
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 horas para concluir
16 vÃdeos (Total 107 mÃn.), 1 leitura, 3 testes
4 horas para concluir
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 horas para concluir
16 vÃdeos (Total 128 mÃn.), 3 leituras, 3 testes
Avaliações
- 5 stars85,14%
- 4 stars12,33%
- 3 stars1,79%
- 2 stars0,51%
- 1 star0,20%
Principais avaliações do INTRODUCTION TO MACHINE LEARNING IN PRODUCTION
por SP18 de fev de 2022
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.
por EE19 de mai de 2021
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!
por AN11 de jun de 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
por RG4 de jun de 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
Sobre Programa de cursos integrados 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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