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Powering Your Generative AI Workloads with AMD and Open-Source ROCm – Farshad Ghodsian

Powering Your Generative AI Workloads with AMD and Open-Source ROCm – Farshad Ghodsian

Hallucination-Free LLMs: Strategies for Monitoring and Mitigation – Wojtek Kuberski, NannyML

Hallucination-Free LLMs: Strategies for Monitoring and Mitigation – Wojtek Kuberski, NannyML

Accelerating the LLM Lifecycle on the Cloud – Luca Antiga, Lightning AI

Accelerating the LLM Lifecycle on the Cloud – Luca Antiga, Lightning AI

From OpenAI to Opensource AI: Navigating Between Commercial Ownership and Coll… – Raphaël Semeteys

From OpenAI to Opensource AI: Navigating Between Commercial Ownership and Coll… – Raphaël Semeteys

Building Reproducible ML Processes with an Open Source Stack – Einat Orr, Treeverse

Building Reproducible ML Processes with an Open Source Stack – Einat Orr, Treeverse

Introduction to Distributed ML Workloads with Ray on Kubernetes – Abdel Sghiouar, Google Cloud

Introduction to Distributed ML Workloads with Ray on Kubernetes – Abdel Sghiouar, Google Cloud

Wikidata Knowledge Graph to Enable Equitable and Validated Ge… – Jonathan Fraine & Lydia Pintscher

Wikidata Knowledge Graph to Enable Equitable and Validated Ge… – Jonathan Fraine & Lydia Pintscher

Designing AI for Network Troubleshooting: A Langchain Proof of Concept – Jesus Illescas, Cisco

Designing AI for Network Troubleshooting: A Langchain Proof of Concept – Jesus Illescas, Cisco

Between Public Benchmarks and Business Needs – Lukasz Borchmann, Snowflake

Between Public Benchmarks and Business Needs – Lukasz Borchmann, Snowflake

Advancing Responsible AI: Unveiling the Software Carbon Efficiency Rat… – Chris Xie & Tereze Gaile

Advancing Responsible AI: Unveiling the Software Carbon Efficiency Rat… – Chris Xie & Tereze Gaile

The Dark Side of AI: The Hidden Supply Chain Risks in Open… – Jossef Kadouri & Tzachi Zornshtain

The Dark Side of AI: The Hidden Supply Chain Risks in Open… – Jossef Kadouri & Tzachi Zornshtain

Examining the Principles of Observability and Its Relevance in LLM… – Guangya Liu & Jean Detoeuf

Examining the Principles of Observability and Its Relevance in LLM… – Guangya Liu & Jean Detoeuf

Building a Data Infrastructure for AI/ML – Keith Pijanowski, MinIO

Building a Data Infrastructure for AI/ML – Keith Pijanowski, MinIO

AI and Incident Management: How to Reduce Manual Work and Speed up the Incident… – Birol Yildiz

AI and Incident Management: How to Reduce Manual Work and Speed up the Incident… – Birol Yildiz

Towards a Practical Ethics of Generative AI – Geert Hofman

Towards a Practical Ethics of Generative AI – Geert Hofman

Best Practices for GenAI applications on AWS – RAG pipeline Eval – Part 3 | Amazon Web Services

Best Practices for GenAI applications on AWS – RAG pipeline Eval – Part 3 | Amazon Web Services

AI adoption best practices

AI adoption best practices

Best Practices for GenAI applications on AWS – Model Selection – Part 2 | Amazon Web Services

Best Practices for GenAI applications on AWS – Model Selection – Part 2 | Amazon Web Services

Best Practices for GenAI applications on AWS – Model Selection – Part 1 | Amazon Web Services

Best Practices for GenAI applications on AWS – Model Selection – Part 1 | Amazon Web Services

MegaBytes- Cloud Opennebula

MegaBytes- Cloud Opennebula

MegaBytes- cc opennebula

MegaBytes- cc opennebula

Digital Twins, the Metaverse, and Augmented Reality

Digital Twins, the Metaverse, and Augmented Reality

Lessons learned optimizing Microsoft’s internal use of Azure

Lessons learned optimizing Microsoft’s internal use of Azure