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 flexíveis
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.
Nível avançado
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 10 horas para completar
Inglês
Legendas: Inglês
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 DriftModel baselineProject Scoping and DesignML Deployment Challenges
Prazos flexíveis
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.
Nível avançado
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 10 horas para completar
Inglês
Legendas: Inglês
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
3 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.
3 horas para concluir
9 vídeos (Total 81 mín.), 3 leituras, 2 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 vídeos (Total 107 mín.), 1 leitura, 2 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 vídeos (Total 128 mín.), 3 leituras, 2 testes
Avaliações
Principais avaliações do INTRODUCTION TO MACHINE LEARNING IN PRODUCTION
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
por EE19 de Mai de 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!
por AC8 de Jun de 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.
por UU4 de Jun de 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!
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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