This course is a comprehensive, hands on course designed to equip participants with the knowledge and skills to design, build, and deploy machine learning solutions on Google Cloud. Through a combination of theory, practical labs, and real world case studies, participants will explore core concepts such as data preprocessing, model development, training and tuning at scale, and deploying ML models using Google Cloud’s AI and ML tools. The course emphasizes applied learning with TensorFlow, Vertex AI, BigQuery ML, and other cloud native solutions, preparing participants to deliver business-ready ML systems that are scalable, efficient, and secure.

WHO SHOULD ATTEND?
This course is tailored for data scientists, machine learning engineers, software developers, and AI practitioners who want to deepen their expertise in building and operationalizing machine learning models on Google Cloud. It is also suitable for technical professionals seeking to advance their careers by gaining certification as a Google Cloud Machine Learning Engineer and for teams aiming to integrate cloud-based AI solutions into their business workflows.

COURSE OUTCOMES
Delegates will gain the skills and knowledge to:

• Gain proficiency in applying machine learning concepts using Google Cloud’s ML ecosystem.
• Build, train, and deploy scalable ML models with Vertex AI and TensorFlow.
• Implement data pipelines for preprocessing and feature engineering using BigQuery and Dataflow.
• Optimize models for performance, fairness, and efficiency.
• Prepare confidently for the Google Cloud Professional Machine Learning Engineer certification exam.

KEY COURSE HIGHLIGHTS
At the end of the course, you will understand;

• Hands-on labs and projects using Google Cloud tools.
• End-to-end coverage of ML workflows from data ingestion to model deployment.

Course Booking

Please use the “book now” or “inquire” buttons on this page to either book your space or make further enquiries.