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)
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Acerca de este Curso
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Fechas límite flexibles
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Certificado para compartir
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100 % en línea
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Nivel avanzado
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 10 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
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
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel avanzado
• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 10 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programa - Qué aprenderás en este curso
3 horas para completar
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 completar
9 videos (Total 81 minutos), 3 lecturas, 3 cuestionarios
3 horas para completar
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 completar
16 videos (Total 107 minutos), 1 lectura, 3 cuestionarios
4 horas para completar
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 completar
16 videos (Total 128 minutos), 3 lecturas, 3 cuestionarios
Reseñas
Principales reseñas sobre 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 may. 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 NB11 de jul. de 2021
Introduces you to the basics of MLOps in a well paced mannar. Would request to add more examples of structured data sets, as many companies usually are dealing with the related problems.
Acerca de Programa especializado: 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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