This comprehensive multi-week targeted training is designed to upskill teams in managing migrated machine learning workloads from AWS SageMaker to Google Cloud Vertex AI. Through hands-on workshops, practical exercises, and community-driven learning, participants will develop the expertise needed to successfully transition data science, machine learning engineering, and machine learning governance operations while implementing well architected frameworks.
The targeted training ensures Sysco members and project teams are equipped with the skills necessary for successful adoption and operational utilization by focusing on:
- Master Google Core Architecture: Understand the architectural differences between AWS SageMaker and Google Cloud Vertex AI through comprehensive hands-on analysis
- Implement CI/CD for ML Workloads: Build robust automation pipelines using Git, GitHub Actions, and Vertex AI Pipelines with enterprise-grade security and compliance
- Develop Container Expertise: Learn to migrate and optimize Docker containers for ML workloads across cloud platforms with focus on security scanning and vulnerability management
- Establish Best Practices: Implement feature-based development, protected branch strategies, and automated testing for ML projects aligned with Sysco's operational standards
- Build Community Capabilities: Foster knowledge sharing and collaborative problem-solving within ML engineering teams to ensure organizational knowledge retention and cross-functional support
By the end of this program, participants will be able to:
- Design and implement end-to-end ML pipelines using Vertex AI
- Automate container builds and deployments using GitHub Actions
- Migrate Amazon workflows to Google Vertex AI with minimal disruption
- Implement secure, scalable, compliance governed CI/CD practices for AI/ML workloads
- Operationalize and optimize ML pipeline performance and governance capabilities
This training program emphasizes community-driven learning through:
Knowledge Sharing Sessions
- Technical discussions and collaboration workshops
- Peer code reviews and architecture design sessions
- Cross-team collaboration on ways of working
- Well architected framework documentation and template sharing
Mentorship Network
- Senior practitioners guide junior team members
- Cross-functional pairing between data scientists, ML engineers, and ML Ops engineers
- Industry expert guest sessions
- Alumni network for ongoing support and career development
Community Resources
- Shared repository of templates and code examples
- Internal knowledge base with troubleshooting guides
- Teams channels for real-time support and discussion
- Regular "MLOPS Hackathon" sessions for project demonstrations
Focus: Core cloud concepts, cost analysis, and infrastructure fundamentals
- Week 1: FinOps
- AWS and GCP cost management tools exploration and analysis
- ROI calculations and budget planning for Sysco-specific workloads
- Week 2: Core Architecture
- Global infrastructure analysis across regions and availability zones
- Compute and storage fundamentals optimized for ML workloads
- Edge network and CDN architecture comparison
- Week 3: Platform Enablement - IAM
- IAM configuration for SageMaker and Vertex AI resources
- CLI and SDK authentication setup for both platforms
- Service account and role management best practices
- Week 4: Data Network Architecture - Networking (VPC)
- VPC design and implementation for ML workloads
- Security groups, firewall rules, and network security policies
- Private connectivity and service networking configuration
Focus: ML-specific services, pipeline architecture, and service mapping
- Week 5: ML Pipeline Components and Architecture Exploration
- SageMaker and Vertex AI pipeline component analysis
- Pipeline orchestration and workflow management
- Component mapping and functionality translation workshops
- Week 6: Data Services Integration Architecture
- S3, Redshift, Cloud Storage, and BigQuery integration patterns
- Data lake and analytics architecture for ML workloads
- Snowflake cross-platform integration strategies
- Week 7: Container Infrastructure and Registry Management
- ECR and Artifact Registry setup and configuration
- Container image management, versioning, and security scanning
- Multi-registry management and cross-cloud synchronization
- Week 8: AWS SageMaker to Vertex AI Fundamentals
- Comprehensive service architecture comparison
- Terminology translation and workflow pattern mapping
- Migration strategy development and risk assessment
Focus: Hands-on comparison, environment setup, and data migration execution
- Week 9: Comprehensive AWS vs GCP ML Platform Analysis
- Feature-by-feature comparison across all ML services
- Compute types and sizing optimization analysis
- AutoML capabilities and model management comparison
- Week 10: Environment Setup and Authentication Configuration
- GCP project configuration and organizational structure
- Vertex AI API setup and permission management
- Cross-environment credential management and security
- Week 11: Data Migration and Storage Implementation
- S3 to Cloud Storage migration strategies and execution
- Data pipeline setup, validation, and quality assurance
- Data versioning, governance, and compliance implementation
- Week 12: Container Infrastructure Transformation
- SageMaker container architecture analysis and documentation
- Vertex AI container development and optimization
- Dockerfile migration and multi-stage build optimization
Focus: Pipeline development, automation, and production-ready implementations
- Week 13: ML Pipeline Development and Orchestration
- Vertex AI pipeline component architecture and development
- End-to-end pipeline design with conditional logic and branching
- Third-party tool integration and testing frameworks
- Week 14: Feature-Based Development for ML Workloads
- ML code organization and repository structure design
- Version control strategies and large file management
- Experiment tracking and performance monitoring integration
- Week 15: Protected Branch Strategy and Model Validation
- ML-specific branch protection policies and automated quality checks
- Model validation gates and stakeholder approval workflows
- Comprehensive testing strategies for ML components
- Week 16: CI/CD Pipeline Implementation and Production Deployment
- GitHub Actions integration with Vertex AI pipelines
- Automated training job submission, monitoring, and alerting
- Secrets management and enterprise-grade authentication
Each week includes multiple hands-on labs designed to:
- Reinforce Theoretical Concepts: Practical application of learned principles
- Build Muscle Memory: Repeated practice with common workflows and commands
- Encourage Experimentation: Safe environment to test different approaches
- Foster Problem-Solving: Real-world scenarios and troubleshooting exercises
- Week 1-4: Individual exercises focusing on basic concepts
- Week 5-8: Small group projects with peer collaboration
- Week 9-12: Cross-team integration challenges
- Week 13-16: Capstone projects with full CI/CD implementation
Tiered Support Model
- Peer Support: Fellow participants and study groups
- Mentor Guidance: Senior practitioners and subject matter experts
- Instructor Support: Training facilitators and technical leads
- Expert Consultation: Cloud architects and vendor specialists
Communication Channels
- Slack Workspace: Real-time discussion and quick help
- GitHub Issues: Technical problem tracking and solutions
- Weekly Office Hours: Direct access to instructors and mentors
- Monthly Town Halls: Program updates and community announcements
Shared Resources
- Wiki Documentation: Collaborative knowledge base development
- Code Repository: Shared templates, examples, and best practices
- Video Library: Recorded sessions and demo content
- FAQ Database: Community-driven answers to common questions
Quality Assurance
- Peer review process for shared resources
- Regular content updates and validation
- Community feedback integration
- Expert review and approval workflows
- Lab Completion Rates: Hands-on exercise participation
- Peer Review Participation: Code review and collaboration engagement
- Knowledge Base Contributions: Community resource development
- Capstone Project Delivery: End-to-end implementation demonstration
- Active Participation Rates: Forum discussions and help requests
- Knowledge Sharing Volume: Tips, solutions, and best practices shared
- Mentorship Engagement: Mentor-mentee relationship development
- Cross-Team Collaboration: Inter-departmental project participation
- Basic understanding of cloud computing concepts
- Familiarity with containerization (Docker)
- Experience with version control (Git)
- Python programming proficiency
- AWS and GCP account access
- Account Setup: Ensure access to AWS and GCP accounts
- Tool Installation: Install required CLI tools and SDKs
- Community Onboarding: Join Slack workspace and introduce yourself
- Repository Access: Clone training materials and example code
- Learning Path Selection: Choose specialized track based on role
- Cloud CLIs: AWS CLI, Google Cloud SDK
- Development Environment: Python 3.9+, Docker Desktop
- Code Editor: VS Code with cloud extensions
- Version Control: Git client and GitHub account
- Communication: Slack desktop application
The learning doesn't stop after 16 weeks:
Alumni Network
- Quarterly reunion sessions with program updates
- Advanced topic workshops and masterclasses
- Career development and networking opportunities
- Ongoing mentorship and knowledge sharing
Continuous Learning Resources
- Monthly tech talks on emerging ML and cloud technologies
- Access to updated training materials and new labs
- Community-driven content creation and sharing
- Industry conference attendance and knowledge transfer
Professional Development
- Cloud certification preparation support
- Conference speaking opportunities and abstracts
- Open source contribution projects
- Cross-company collaboration initiatives
- Technical Lead: [David] - Overall program design and technical oversight
- Community Manager: [David, Andrea, and Dipti] - Participant engagement and support coordination
- Cloud Architect: [Javed, Greg, Accenture] - AWS and GCP expertise and best practices
- Technical Questions: Post in #technical-help Teams channel
- Program Logistics: Contact program coordinators directly
- Community Issues: Reach out to community managers
- Emergency Support: Use #urgent-help Teams channel for critical issues
- Complete account setup (AWS, GCP, GitHub)
- Install required tools and CLI utilities
- Join targeted training Teams workspace
- Introduce yourself to the community
- Clone training repository and review Week 1 materials
- Attend orientation session and meet your mentor
- Set up local development environment
- Complete pre-training assessment
Ready to begin your ML migration journey? Let's build the future of ML engineering together!
This targeted training program is designed to ensure all Sysco team members are well-prepared for each phase of the multi-project GCP migration through comprehensive hands-on learning and community collaboration.