MACHINE LEARNING ON GOOGLE CLOUD
The Machine Learning on Google Cloud course is designed for professionals who want to build, train, deploy, and manage machine learning solutions using Google Cloud Platform. This course focuses on practical machine learning workflows, Google Cloud ML services, and best practices for developing scalable and production-ready ML models. Participants will gain hands-on experience with data preparation, model training, evaluation, deployment, and monitoring using Google Cloud�s managed machine learning tools and services. Course Objectives By the end of this course, participants will be able to: Understand end-to-end machine learning workflows on Google Cloud Prepare and manage data for ML workloads Train, evaluate, and tune machine learning models Deploy models for batch and real-time prediction Monitor model performance and manage model lifecycle Apply security, governance, and cost optimization best practices Select appropriate Google Cloud ML services for different use cases
Course Curriculum
1
- Machine learning concepts and terminology
- ML use cases and business applications
- Overview of Google Cloud ML ecosystem
2
- Data ingestion and storage
- Data cleaning and preprocessing
- Feature engineering concepts
- Managing datasets for ML
3
- Data analysis techniques
- Identifying patterns and anomalies
- Preparing data for model training
4
- Supervised and unsupervised learning
- Choosing appropriate algorithms
- Training models at scale
- Managing training jobs
5
- Evaluation metrics
- Hyperparameter tuning
- Avoiding overfitting and underfitting
- Model selection strategies
6
- Vertex AI overview
- AutoML vs custom models
- Using pre-trained ML APIs
7
- Batch vs online prediction
- Deploying models to endpoints
- Versioning and A/B testing
- Scaling inference workloads
8
- Model monitoring and drift detection
- Logging and observability
- CI/CD concepts for ML (MLOps)
- Automating retraining pipelines
9
- IAM and access control for ML
- Data security and encryption
- Responsible AI and fairness considerations
10
- Cost drivers in ML workloads
- Resource optimization strategies
- Performance tuning best practices
11
- NLP, computer vision, and forecasting
- End-to-end ML solution design
- Mapping ML solutions to business problems
12
- Data scientists and ML engineers
- Data engineers transitioning to ML roles
- Software developers working with ML models
- Cloud architects designing ML solutions
- Professionals preparing for Google Cloud ML certifications
13
- Google Cloud Fundamentals: Big Data and Machine Learning or equivalent knowledge
- Basic understanding of machine learning concepts
- Programming experience (Python recommended)
- Familiarity with data analysis concepts
14
- 3-4 days (Instructor-led)
- 24-32 hours of training
15
- Instructor-led technical sessions
- Hands-on ML labs
- Real-world ML scenarios
- Guided demonstrations and exercises
16
- This course supports preparation for Google Cloud Professional Machine Learning Engineer
- Serves as a foundation for advanced AI and data engineering courses
17
- Comprehensive training materials
- Hands-on lab guides
- Sample ML notebooks and workflows
- Certificate of course completion
This course includes
- 17+ Activity Modules
- 40 hours + lessons
- Lifetime access
- Certificate of completion
- Available on desktop and mobile
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