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)
oferecido por

Informações sobre o curso
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
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
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
oferecido por
Programa - O que você aprenderá com este curso
Week 1: Overview of the ML Lifecycle and Deployment
Week 2: Select and Train a Model
Week 3: Data Definition and Baseline
Avaliações
- 5 stars84,41%
- 4 stars12,98%
- 3 stars1,89%
- 2 stars0,44%
- 1 star0,26%
Principais avaliações do INTRODUCTION TO MACHINE LEARNING IN PRODUCTION
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!
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.
Practical and well-structured advices throughout the lifecycle of ML. Examples from real world problems & experiences make the advices more tangible and helps to reflect on own problems.
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.
Sobre Programa de cursos integrados Machine Learning Engineering for Production (MLOps)

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