As organizations scale their AI and ML initiatives, the complexity of managing different operational paradigms—from traditional ML to foundation models and generative AI—presents unique challenges. This comprehensive session unveils ten essential practices across MLOps, FMOps (Foundation Model Operations), and GenAIOps that are crucial for building robust, scalable, and responsible AI systems.Through practical demonstrations using Amazon SageMaker and AWS Bedrock, attendees will discover how to implement these practices effectively in their organizations.

The session covers the entire AI/ML lifecycle, from experiment management and model training to deployment strategies and responsible AI implementation. Real-world examples will illustrate how these practices address common challenges in scaling AI initiatives, ensuring quality, and maintaining operational excellence.Whether you're a ML engineer working with traditional models, a data scientist exploring foundation models, or a technical leader planning GenAI initiatives, this session provides actionable insights and practical approaches for building and maintaining effective AI/ML systems at scale.

Detailed Session Coverage:

1. Understanding the Operational Landscape
2. • Distinguishing MLOps, FMOps, and GenAIOps
   • Key differences in operational requirements
   • Considerations for each paradigm
2. Essential MLOps Practices
3. • Experiment tracking and versioning
   • Streamlining training workflows with SageMaker
   • Best practices for model deployment and monitoring
3. Foundation Model Operations (FMOps)
4. • Systematic approach to model selection and evaluation
   • Guidance on model customization
   • Optimization strategies for resource utilization
   • Model deployment deployment options on AWS
4. GenAI Application Development and Operations
5. • Evaluation-driven development methodology
   • Implementing robust prompt management systems
   • Responsible AI guidelines and content filtering
   • Monitoring and tracing in GenAI applications

Learning Outcomes:

• Understanding of key operational differences between traditional ML, foundation models, and GenAI
• Practical knowledge of implementing AI/MLOps practices using AWS services
• Strategies for effective foundation model deployment and management
• Best practices for responsible GenAI application development

Target Audience:

• ML Engineers and Data Scientists
• DevOps Engineers working with AI/ML systems
• Technical Leaders, Architects, and app developers
• AI/ML Project Managers

Prerequisites:

• Basic understanding of AI/ML concepts
• Familiarity with Gen AI applications
• Basic knowledge of AWS services (helpful but not required)