Who's it for?
Developers, data professionals, and technical teams building on Google Cloud, and the L&D leaders and decision-makers who are building their upskilling roadmap. No two sessions require the same background. Pick the course that matches where your team is today.
Build the path that fits your team
Start with Fundamentals if your team is new to Google Cloud.
Move into Data Analytics, Data Engineering, or AI and Machine Learning based on the roles you're upskilling.
Introduction to Data Analytics on Google Cloud
Don't just collect data. Learn to make it speak.
A beginner-level course on the data analytics workflow on Google Cloud. Using a case study, hands-on labs, and demos, your team learns to go from raw datasets to clean data to dashboards stakeholders actually use. No prior data analytics or Google Cloud experience required.
You'll learn to
- Describe the data analytics workflow on Google Cloud and the different types of analytics.
- Identify Google Cloud's data analytics products and how each one fits into the workflow.
- Explore data and extract insights with BigQuery, using SQL to answer real business questions.
- Build and share visualizations with Looker and Looker Studio to influence decisions.
Best for
Anyone who performs or uses data analysis in their role, including analysts, business teams, and technical staff who are new to Google Cloud.
17 October | 10AM - 5PM | IST
Introduction to AI and Machine Learning on Google Cloud
Every smart recommendation, prediction, and chatbot runs on a model someone built and understood.
A one-day course covering Google Cloud's AI and machine learning offerings, from predictive models to generative AI. Your team moves through the full data-to-AI lifecycle: infrastructure, development, and solutions, with hands-on labs using BigQuery ML and Vertex AI.
You'll learn to
- Recognize the AI and ML framework on Google Cloud and the infrastructure behind it.
- Build a first ML model with BigQuery ML, moving from data straight to AI.
- Build generative AI projects using Gemini multimodal, prompt design, and model tuning in Vertex AI Studio.
- Build and serve an end-to-end ML model with Vertex AI, and automate it with Vertex AI Pipelines.
Best for
AI developers, data scientists, and ML engineers who want to build predictive and generative AI projects on Google Cloud. Basic knowledge of ML concepts is helpful but not required.
21 November | 10AM - 5PM | IST
Introduction to Data Engineering on Google Cloud
Raw data is easy to find. Usable data is engineered.
A one-day, hands-on course on the role of the data engineer on Google Cloud. Your team learns how data pipelines are designed, built, and automated so that everything downstream, dashboards, models, and reports, can rely on clean data.
You'll learn to
- Explain the role of a data engineer and the core data engineering tasks on Google Cloud.
- Compare data sources, sinks, formats, and storage options, including BigQuery and Cloud Storage.
- Build and deploy data pipelines using tools like Dataflow, Dataproc, and Cloud Composer. \
- Identify automation techniques and replication and migration options, including Datastream
Best for
Data engineers, database administrators, and systems administrators. Basic Google Cloud experience and familiarity with SQL are recommended.
19 December | 10AM - 5PM | IST
Talk to an expert today.
Choosing the right starting point on Google Cloud isn't always obvious. Infrastructure, data analytics, data engineering, AI and machine learning: each path fits a different team, a different skill gap, a different business goal.
Xebia Academy's consultants work with L&D leaders and technical teams every day to map that path. Tell us where your team stands today, and we'll help you decide which course to start with, how to sequence all four, and how to bring a full cohort online.
We offer flexible training formats, including private on-site sessions and customized workshops tailored to your organization’s tech stack.