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CODE: NAI22
DURATION: 3 Days | 5 Days | 10 Days
CERTIFICATIONS: CPD
This course provides an end-to-end learning path for developing, deploying, and managing machine learning models on Google Cloud Platform (GCP). Areas covered in the course are model development, data preparation, pipeline automation, model monitoring, and MLOps best practices. Participants will engage practical tools like Vertex AI, BigQuery ML, AutoML, and TensorFlow on GCP. Participants can use this course to prepare for the Google Cloud Professional Machine Learning Engineer certification and real-world ML engineering roles in cloud environments.
This course is available in the following formats:
Virtual
Classroom
Request this course in a different delivery format.
Course Outcomes
Delegates will gain the knowledge and skills to:
Understand the ML lifecycle on Google Cloud.
Prepare and manage large datasets using GCP tools.
Build and train ML models using TensorFlow and Vertex AI.
Automate model pipelines and deployment workflows.
Implement MLOps for monitoring and continuous improvement.
Optimize model performance and manage version control.
Apply security and governance best practices for ML solutions.
Prepare for the Google Cloud ML Engineer certification exam.
At the end of this course, you’ll understand:
This course is designed for machine learning engineers, data scientists, cloud developers, AI/ML practitioners, and technical professionals seeking to build and scale ML solutions on Google Cloud. It is also beneficial for those preparing for the Google Cloud Professional ML Engineer certification. Prior experience with Python, machine learning basics, and familiarity with cloud concepts is recommended.
✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
