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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Machine Learning Engineering for Production (MLOps)
von

Über diesen Kurs
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Was Sie lernen werden
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.
Kompetenzen, die Sie erwerben
- ML Metadata
- Convolutional Neural Network
- TensorFlow Extended (TFX)
- Data Validation
- Data transformation
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
von

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Lehrplan - Was Sie in diesem Kurs lernen werden
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.
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
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.
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.
Bewertungen
- 5 stars63,34 %
- 4 stars19,91 %
- 3 stars9,53 %
- 2 stars5,29 %
- 1 star1,90 %
Top-Bewertungen von MACHINE LEARNING DATA LIFECYCLE IN PRODUCTION
useful insights, but tfx implementation might be invasive towards exisiting mlops pipelines
It is really good course, the detail explanation of Data LifeCycle in TFX!
Best course for the professionals looking to upgrade there ML skills at production level! Thanks to the brilliant and wonderful course instructor.
It is a very informative course. I learned a lot about data, metadata, schema and feature engineering, Also, Robert Crowe sir is a very good teacher.
Über den Spezialisierung 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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