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Discover structured retrieval patterns to move beyond "plug and pray" MCP implementations and achieve reliable LLM performance in production environments.
Discover how to build unified multi-cloud GenAI platforms using SkyPilot to overcome GPU shortages, reduce costs, and eliminate vendor lock-in across AWS, GCP, Azure, and more.
Discover how to build robust testing frameworks for AI agents in production, covering regression testing, adversarial validation, and automated pipelines for reliability at scale.
Discover how to build custom AI inferencing pipelines using Infrastructure-as-code with Terraform and Python for GCP/AWS, incorporating MLOps best practices from Vector Institute's bootcamp.
Explore how small language models are revolutionizing AI by enabling advanced capabilities in resource-constrained environments and on-device applications.
Discover how to implement dynamic models in production while balancing automation with proper governance, testing, and risk management controls for machine learning operations.
Explore enterprise LLM platform development from architecture to deployment, covering model selection, infrastructure design, security, governance, and real-world applications.
Discover how National Bank of Canada uses cost-effective GenAI to automate complaint management, achieve 100% coverage, detect systemic issues, and streamline regulatory compliance processes.
Discover how Sanofi leverages AI and GenAI to transform pharmaceutical commercial operations, enhance customer engagement through personalization, and optimize sales while ensuring ethical AI practices.
Discover how fine-tuning transforms general LLMs into reliable, domain-specific tools for healthcare, legal, and enterprise applications while reducing costs and improving accuracy.
Discover how knowledge graphs and Neo4j enhance GenAI applications through GraphRAG, reducing hallucinations while uncovering hidden data patterns and relationships.
Discover actionable strategies for governing Generative AI systems across organizations with role-based implementation, risk mitigation tools, and scalable practices.
Discover how to build an LLM factory for continuous iteration, covering data curation, prompt refinement, fine-tuning, and deployment in a modular, developer-friendly setup.
Discover how containers streamline AI development with consistent environments and efficient dependency management for faster development processes.
Explore the infrastructure and software components needed to build, deploy, and orchestrate full stack AI agents in production environments with practical demonstrations.
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