In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.
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 lÃmite flexibles
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Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
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. 22 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English), Francés (French)
Qué aprenderás
Identify responsible data collection for building a fair ML production system.
Implement feature engineering, transformation, and selection with TensorFlow Extended
Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.
Habilidades que obtendrás
- ML Metadata
- Convolutional Neural Network
- TensorFlow Extended (TFX)
- Data Validation
- Data transformation
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
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. 22 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English), Francés (French)
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
7 horas para completar
Week 1: Collecting, Labeling and Validating Data
This week covers a quick introduction to machine learning production systems. More concretely you will learn about leveraging the TensorFlow Extended (TFX) library to collect, label and validate data to make it production ready.
7 horas para completar
12 videos (Total 95 minutos), 3 lecturas, 7 cuestionarios
7 horas para completar
Week 2: Feature Engineering, Transformation and Selection
Implement feature engineering, transformation, and selection with TensorFlow Extended by encoding structured and unstructured data types and addressing class imbalances
7 horas para completar
12 videos (Total 86 minutos), 1 lectura, 5 cuestionarios
5 horas para completar
Week 3: Data Journey and Data Storage
Understand the data journey over a production system’s lifecycle and leverage ML metadata and enterprise schemas to address quickly evolving data.
5 horas para completar
8 videos (Total 42 minutos), 1 lectura, 5 cuestionarios
4 horas para completar
Week 4 (Optional): Advanced Labeling, Augmentation and Data Preprocessing
Combine labeled and unlabeled data to improve ML model accuracy and augment data to diversify your training set.
4 horas para completar
6 videos (Total 31 minutos), 5 lecturas, 4 cuestionarios
Reseñas
- 5 stars61,88Â %
- 4 stars20,94Â %
- 3 stars9,43Â %
- 2 stars5,09Â %
- 1 star2,64Â %
Principales reseñas sobre MACHINE LEARNING DATA LIFECYCLE IN PRODUCTION
por SS12 de oct. de 2021
The course is exciting. Lab and exercises are informative, but the answer to the quizzes are a little ambiguous.
por ZE23 de dic. de 2021
It is really good course, the detail explanation of Data LifeCycle in TFX!
por BP28 de may. de 2021
excellent course. Nice to see how we can detect data drift and skew drift
por TD21 de ago. de 2021
This was my first course with Robert, which was a very pleasant experience.
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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