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 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 flexÃveis
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.
NÃvel avançado
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 22 horas para completar
Inglês
Legendas: Inglês, Francês
O que você vai aprender
 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 você terá
- ML Metadata
- Convolutional Neural Network
- TensorFlow Extended (TFX)
- Data Validation
- Data transformation
Prazos flexÃveis
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.
NÃvel avançado
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Aprox. 22 horas para completar
Inglês
Legendas: Inglês, Francês
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
7 horas para concluir
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 concluir
12 vÃdeos (Total 95 mÃn.), 3 leituras, 7 testes
7 horas para concluir
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 concluir
12 vÃdeos (Total 86 mÃn.), 1 leitura, 5 testes
5 horas para concluir
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 concluir
8 vÃdeos (Total 42 mÃn.), 1 leitura, 5 testes
4 horas para concluir
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 concluir
6 vÃdeos (Total 31 mÃn.), 5 leituras, 4 testes
Avaliações
- 5 stars62,22%
- 4 stars20,47%
- 3 stars9,94%
- 2 stars5,16%
- 1 star2,18%
Principais avaliações do MACHINE LEARNING DATA LIFECYCLE IN PRODUCTION
por FF8 de set de 2021
Lessons are well structured and clear, and the labs are very instructive. Above all the course is fun!
por SC2 de jul de 2021
Interesting material. There are quite a lot of typos and many code snippets are directly from the tfx manual pages however the instructions provided and logic of the course is clear.
por WR26 de jul de 2021
It's a new course so sometimes there are mistakes in the translations or there is something off in the assignment's grading, but the content is great. :)
por PP8 de jul de 2021
Very good training about data lifecycle for ML projects
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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