Machine Learning in the Enterprise | Google Cloud Skills Boost

Кроки для для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Курс: Machine Learning in the Enterprise

Machine Learning in the Enterprise - Mar 20, 2024

Summary

This course encompasses a real-world practical approach to the ML Workow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataow and Dataprep to using BigQuery for preprocessing tasks.

The team is presented with three options to build machine learning models for two specic use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to expoing a trained model.

You will build a custom training machine learning model, which allows you to build a container image with lile knowledge of Docker.

The case study team examines hyperparameter tuning using Veex Vizier and how it can be used to improve model peormance. To understand more about model improvement, we dive into a bit of theory: we discuss regularization, dealing with sparsity, and many other essential concepts and principles. We end with an overview of prediction and model monitoring and how Veex AI can be used to manage ML models

● Module 1: Understanding the ML Enterprise Workow
● Module 2: Data in the Enterprise
● Module 3: Science of Machine Learning and Custom Training
● Module 4: Veex Vizier Hyperparameter Tuning
● Module 5: Prediction and Model Monitoring Using Veex AI
● Module 6: Veex AI Pipelines
● Module 7: Best Practices for ML Developmen

Machine Learning Engineer Learning Path

A Machine Learning Engineer designs, builds, productionizes, optimizes, operates, and maintains ML systems.

Summary

A Machine Learning Engineer designs, builds, productionizes, optimizes, operates, and maintains ML systems. This learning path guides you through a curated collection of on-demand courses, labs, and skill badges that provide you with real-world, hands-on experience using Google Cloud technologies essential to the ML Engineer role. Once you complete the path, check out the Google Cloud Machine Learning Engineer certification to take the next steps in your professional journey.

Machine Learning Engineer Learning Path

#MachineLearning #MachineLearningModels #MachineLearningPipeline

BADGES