Google Cloud Certified Professional Machine Learning Engineer (GCP-PML-201)

Unleash your machine learning potential with our comprehensive "Google Cloud Certified Professional Machine Learning Engineer" training course, expertly designed to prepare you for the official Google Cloud certification exam. This in-depth program equips you with the skills to architect, build, and deploy scalable machine learning (ML) solutions on Google Cloud Platform (GCP). From model development to production optimization, you’ll master the tools and techniques to solve real-world problems using cutting-edge ML technologies.

What You'll Learn:

• ML Solution Design: Architect end-to-end ML workflows tailored to business needs on GCP.
• Data Preparation: Process and transform data using BigQuery, Dataflow, and Dataproc.
• Model Development: Build and train ML models with AI Platform and TensorFlow.
• Model Deployment: Deploy and manage ML models at scale with AI Platform and Kubernetes.
• Optimization and Automation: Optimize ML pipelines for performance and automate workflows.
• Monitoring and Maintenance: Monitor model performance with Cloud Monitoring and retrain as needed.
• Hands-on Labs and Exam Prep: Gain practical experience with real-world ML scenarios and exercises tailored to the Professional Machine Learning Engineer exam.

Who Should Attend:

• Machine Learning Engineers: Elevate your skills in cloud-based ML on GCP.
• Data Scientists: Transition to production-ready ML engineering roles.
• Cloud Engineers: Specialize in ML solution deployment and management.
• Developers: Learn to integrate ML models into applications.
• Anyone preparing for the Professional Machine Learning Engineer certification.

Prerequisites:

• Experience with machine learning concepts and frameworks (e.g., TensorFlow, scikit-learn).
• Familiarity with GCP fundamentals (e.g., from Core Infrastructure training).
• Basic programming skills (e.g., Python) recommended.

Benefits of Attending:

• Earn a prestigious Google Cloud ML engineering certification.
• Boost your career with expertise in cloud-based machine learning.
• Design and deploy scalable ML solutions on GCP.
• Learn from expert instructors with deep ML experience.
• Receive comprehensive training materials and exam-focused resources.

Course Outline:

• Module 1: Introduction to ML Engineering on GCP
  • Role of a Professional Machine Learning Engineer
  • Overview of GCP ML tools and services
  • ML lifecycle and best practices
• Module 2: Data Preparation and Exploration
  • Ingesting data with Cloud Storage and Pub/Sub
  • Processing data with Dataflow and Dataproc
  • Exploring data with BigQuery
• Module 3: Model Design and Training
  • Building models with TensorFlow and AI Platform
  • Using BigQuery ML for rapid prototyping
  • Hyperparameter tuning and optimization
• Module 4: Model Deployment
  • Deploying models with AI Platform
  • Scaling inference with Kubernetes Engine
  • Managing model versions and endpoints
• Module 5: Automation and Orchestration
  • Automating ML pipelines with Cloud Composer
  • Using Vertex AI for end-to-end ML workflows
  • Implementing CI/CD for ML models
• Module 6: Security and Governance
  • Securing ML data and models with IAM
  • Ensuring compliance in ML workflows
  • Managing sensitive data with DLP
• Module 7: Monitoring and Optimization
  • Monitoring model performance with Cloud Monitoring
  • Detecting drift and retraining models
  • Optimizing costs and latency
• Module 8: Exam Preparation
  • Review of exam domains and objectives
  • Practice with sample ML scenarios and questions
  • Strategies for certification success