5 days EN / DE Max 16

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. 

€3.100,00 excl. VAT

Individual scheduling

The courses are held as dedicated group sessions. Once you've booked, we'll coordinate a date that works for your team and send invitations to all participants.

Prerequisites

  • Some familiarity with basic machine learning concepts.
  • Basic proficiency with a scripting language, preferably Python.

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.

Course modules
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)
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
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
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
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
Machine Learning on Google Cloud