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.Read more.
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The techniques and tools covered in Machine Learning Data Lifecycle in Production are most similar to the requirements found in Data Scientist job advertisements.
Machine Learning Data Lifecycle in Production is a part of one structured learning path.
Machine Learning Engineering for Production (MLOps)