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 forma parte de Programa especializado: Machine Learning Engineering for Production (MLOps)
Ofrecido Por

Acerca de este Curso
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Qué aprenderás
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 obtendrás
- 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)
Ofrecido por
Programa - Qué aprenderás en este curso
Week 1: Overview of the ML Lifecycle and Deployment
Week 2: Select and Train a Model
Week 3: Data Definition and Baseline
Reseñas
- 5 stars84,40 %
- 4 stars12,99 %
- 3 stars1,89 %
- 2 stars0,44 %
- 1 star0,26 %
Principales reseñas sobre INTRODUCTION TO MACHINE LEARNING IN PRODUCTION
Andew Ng is truly a world leader in the field, the way he approaches the subject and the explanations he gives are truly unparalleled. It always a pleasure taking a course he instructs.
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
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.
Acerca de Programa especializado: Machine Learning Engineering for Production (MLOps)

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