Machine Learning- Google Cloud Fundamentals: Big Data & Machine Learning-11

Machine Learning

Google Cloud Fundamentals: Big Data & Machine Learning

Unlock the full potential of Google Cloud! In this one-day course, you’ll dive into building big data pipelines and machine learning models with Vertex AI, learning how to solve data challenges and leverage AI to drive innovation

Duration

1 Days

Target Audience

Who should attend?
Data professionals including data analysts, data scientists, and business analysts eager to leverage Google Cloud for data processing and machine learning.
Individuals involved in designing data pipelines, maintaining ML models, and extracting actionable insights.
Executives and IT decision-makers considering Google Cloud for empowering data science teams and driving business intelligence.

What you'll learn

Understand the data-to-AI lifecycle and key Google Cloud products. Design real-time streaming pipelines with Dataflow and Pub/Sub.Analyze large datasets using BigQuery for actionable insights.Explore machine learning solutions and tools on Google Cloud. Learn the machine learning workflow using Vertex AI.Build an end-to-end ML pipeline with AutoML for automated model training and deployment.

Prerequisites for Success

To fully benefit from this course, participants should have a basic understanding of one or more of the following:
• A database query language such as SQL for querying and analyzing data.
• Key aspects of the data engineering workflow, including extract, transform, load (ETL) processes, as well as data analysis, modeling, and deployment.
• Machine learning models, particularly the differences between supervised and unsupervised learning

COURSE AGENDA

Course Introduction

Data-to-AI Lifecycle on Google Cloud: Understand the end-to-end data-to-AI lifecycle on Google Cloud and how data moves through the system.
Data Engineering and Machine Learning: Learn how data engineering and machine learning are interconnected, and how they work together to create valuable AI-driven solutions.

Big Data & Machine Learning on Google Cloud

Google Cloud’s Infrastructure: Identify the key components of Google Cloud’s infrastructure, including storage, computing, and networking services for big data and machine learning.
Big Data and Machine Learning Products: Explore the big data and machine learning products available on Google Cloud, including BigQuery, Vertex AI, and more.
Lab: Exploring a BigQuery Public Dataset to understand how to use BigQuery for data analysis.

Data Engineering for Streaming Data

End-to-End Streaming Data Workflow: Learn how to design an end-to-end streaming data workflow, from data ingestion to real-time data visualization.
Modern Data Pipeline Challenges: Identify the challenges in building modern data pipelines and how to solve them at scale using Dataflow.
Building Collaborative Dashboards: Learn how to build real-time collaborative dashboards using data visualization tools to make data accessible and actionable.
Lab: Creating a Streaming Data Pipeline for a Real-time Dashboard with Dataflow, putting theory into practice.
Quiz: Test your understanding of the material covered in this module.

Productionizing Vertex AI Search and Conversation

Refreshing Data and Schemas: Learn how to refresh data and schemas to ensure that search engines and chat applications are up to date.
Security and Data Loss Prevention: Understand best practices for ensuring security in your search engines and conversational agents, including Data Loss Prevention (DLP) strategies.
Monitoring and Logging: Set up monitoring and logging to track performance and usage of the applications in real-time.
Troubleshooting: Learn how to diagnose and resolve issues that may arise during the deployment or use of Vertex AI Search and Conversation.