MLOps

Course 2 - Week 2

Course 2: Week 3:

Course 2: Week 4:

Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI

COURSE 3 Machine Learning Modeling Pipelines in Production - Week 1: Neural Architecture Search (NAS) - AutoML, Hyperparameter tuning, Cloud AutoML

#autoML #NAS #hyperparameter

Machine Learning Engineering for Production (MLOps) Specialization


COURSE 1 Introduction to Machine Learning in Production

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.

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. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.

Week 1: Overview of the ML Lifecycle and Deployment

Week 2: Selecting and Training a Model

Week 3: Data Definition and Baseline


COURSE 2 Machine Learning Data Lifecycle in Production

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.

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. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.

Week 1: Collecting, Labeling, and Validating data

Week 2: Feature Engineering, Transformation, and Selection

Week 3: Data Journey and Data Storage

Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types


COURSE 3 Machine Learning Modeling Pipelines in Production

In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.

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. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.

Week 1: Neural Architecture Search

Week 2: Model Resource Management Techniques

Week 3: High-Performance Modeling

Week 4: Model Analysis

Week 5: Interpretability


COURSE 4 Deploying Machine Learning Models in Production

In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system.

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. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.