Machine Learning on Google Cloud
This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project. You learn how to build AutoML models without writing a single line of code; build BigQuery ML models using SQL, and build Vertex AI custom training jobs by using Keras and TensorFlow. You also explore data preprocessing techniques and feature engineering.
- Duration: 5 days
- Languages: English, German
- Level: Intermediate
- Group size: Up to 16
What you'll learn
- Describe the technologies, products, and tools to build an ML model, an ML pipeline, and a Generative AI project.
- Understand when to use AutoML and BigQuery ML.
- Create Vertex AI-managed datasets and add features to the Vertex AI Feature Store.
- Describe Analytics Hub, Dataplex, and Data Catalog.
- Create Vertex AI Workbench user-managed notebooks and build custom training jobs deployed via Docker containers.
- Describe batch and online predictions, model monitoring, and data quality exploration.
- Build and train supervised learning models and optimize them using loss functions and performance metrics.
- Create repeatable and scalable train, eval, and test datasets.
- Implement ML models using TensorFlow or Keras.
- Explain Vertex AI Model Monitoring and Vertex AI Pipelines.
Prerequisites
- Some familiarity with basic machine learning concepts.
- Basic proficiency with a scripting language, preferably Python.
Course outline
Introduction to AI and Machine Learning on Google Cloud
- AI/ML framework on Google Cloud
- Major components of Google Cloud infrastructure
- Data and ML products supporting the data-to-AI lifecycle
- Building ML models with BigQuery ML
- Options to build ML models on Google Cloud (Pre-trained APIs, AutoML, custom training)
- Natural Language API for text analysis
- MLOps and workflow automation
- End-to-end AutoML models on Vertex AI
- Generative AI and Large Language Models (LLMs)
Launching into Machine Learning
- Improving data quality and exploratory data analysis
- Building and training supervised learning models
- AutoML training and deployment
- BigQuery ML benefits
- Optimization and evaluation using loss functions and performance metrics
- Repeatable and scalable training, evaluation, and test datasets
TensorFlow on Google Cloud
- Creating TensorFlow and Keras machine learning models
- TensorFlow main components and the tf.data library
- tf.keras preprocessing layers
- Keras Sequential and Functional APIs
- Training and productionalizing models with Vertex AI Training Service
Feature Engineering
- Vertex AI Feature Store
- Characteristics of a good feature
- tf.keras.preprocessing for image, text, and sequence data
- Feature engineering with BigQuery ML, Keras, and TensorFlow
Machine Learning in the Enterprise
- Data management and governance tools
- Data preprocessing with Dataflow, Dataprep, and SQL
- Framework selection: AutoML vs. BigQuery ML vs. Custom training
- Hyperparameter tuning with Vertex AI Vizier
- Prediction and model monitoring with Vertex AI
- Benefits of Vertex AI Pipelines
- Best practices for model deployment, serving, and artifact organization
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Not sure which course fits?
Book a 30-minute call. We'll look at where your team is, what they need to be able to do, and put together either a specific course date or a tailored learning plan.