What will you learn in this Preparing for Google Cloud Certification Course
Develop the skills required to succeed in a machine learning engineering role.
Prepare comprehensively for the Google Cloud Professional Machine Learning Engineer certification exam.
Understand how to design, build, and productionalize ML models to solve business challenges using Google Cloud technologies.
Gain insights into the purpose of the Professional Machine Learning Engineer certification and its relationship to other Google Cloud certifications.
Program Overview
Introduction to AI and Machine Learning on Google Cloud
⏳ 9 hours
- Learn about Google’s AI and ML offerings and how to build ML models using Vertex AI.
Build, Train, and Deploy ML Models with Keras on Google Cloud
⏳ 13 hours
- Design and build TensorFlow input data pipelines and deploy ML models at scale with Vertex AI.
Feature Engineering
⏳ 8 hours
- Perform feature engineering using BigQuery ML, Keras, and TensorFlow, and explore features with Dataflow and Dataprep.
Machine Learning in the Enterprise
⏳ 8 hours
- Identify and use core technologies required to support effective MLOps and implement reliable training and inference workflows.
Production Machine Learning Systems
⏳ 9 hours
- Implement various flavors of production ML systems and understand the ML workflow through real-world case studies.
MLOps (Machine Learning Operations) Fundamentals
⏳ 13 hours
- Introduce participants to MLOps tools and best practices for deploying, evaluating, monitoring, and managing ML models.
Get certificate
Job Outlook
Equips learners with practical skills for roles such as Machine Learning Engineer, Data Scientist, and AI Specialist.
Prepares candidates for the Google Cloud Professional Machine Learning Engineer certification, recognized industry-wide.
Enhances qualifications for positions requiring expertise in designing and deploying ML models using Google Cloud technologies.
Specification: Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
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