
Explore embedding-based retrieval for AI apps, including key limitations and mitigation techniques. Learn about Chroma, an open-source embedding database for LLM applications.

Discover strategies to eliminate bad data at the source, improve data quality ownership, and enhance observability in Analytics/ML stacks. Learn from experts in data engineering and product management.

Explore MLOps in the era of generative AI with insights on developing, training, and serving large language models. Gain practical knowledge on evaluating use cases and addressing challenges in AI product development.

Explore collaboration, strategy, and data monetization in AI. Learn to become a multiplier, leverage effective strategies, and maximize data potential in the generative AI era.

Explore ML platform development, community building, and advocacy at Wolt. Gain insights on onboarding data scientists, creating user-friendly documentation, and implementing DevRel strategies for effective ML deployments.

Explore MLOps vs ML Orchestration with Ketan Umare, discussing Union.AI, Flyte, community-driven development, real-time data handling, and democratizing machine learning for broader accessibility and efficiency.

Explore how Prosus AI leverages generative AI across 80+ tech companies, enhancing work impact. Gain insights from 10,000 daily AI users in a global portfolio of 800+ AI experts.

Develop and deploy scalable RAG-based LLM applications. Learn to handle data loading, preprocessing, embedding, and serving on clusters. Explore Anyscale Endpoints for cost-effective open-source model serving.

Discover techniques for model blind spot detection and handling unstructured data at scale. Learn to build resilient AI models through data diagnosis, enhancement, and automated evaluation systems.

Explore MLOps strategies for scaling computer vision, including Flyte for AI product development, its application at Recogni, and lessons from running AI models at scale for planetary satellite imagery analysis.

Explore MLOps challenges, LLM applications, and industry trends with experts Patrick Barker and Farhood Etaati. Gain insights on tool adoption, OSS MLOps, and the future of AI implementation in various sectors.

Explore the complexities of Retrieval Augmented Generation (RAG) beyond its oversimplified portrayal. Gain insights into practical challenges and nuanced considerations for developers working with RAG systems.

Explore how data platforms impact AI and ML applications, covering foundations, design patterns, data quality, and team structures for effective ML implementation.

Explore vector embeddings for semantic search, learning techniques to create meaningful representations and apply them in information retrieval systems. Gain insights from an experienced technologist.

Explore data management strategies for effective GenAI application with QuantumBlack AI experts. Learn about challenges, solutions, and best practices for scaling impact and improving data quality in organizations.

Navigate the iterative development of LLM applications, addressing unique challenges in LLMOps design. Learn strategies for overcoming obstacles and adapting to the evolving landscape of large language model operations.

Explore modern data management practices, including data contracts and observability, to improve handling and decision-making in the age of AI and cloud computing.

Explore open standards in MLOps for easier data system integration, scalability, and collaboration. Learn about Ibis, Apache Arrow, and their impact on data engineering and machine learning workflows.

Explore chaos engineering to build reliable systems under unpredictable conditions. Learn benefits, trends, and integration with ML systems for improved resilience and observability.

Explore robotics, computer vision, and recommender systems with Miguel Fierro. Gain insights on ML applications, sports analytics, and AI integration in various fields from a Microsoft Principal Data Science Manager.

Explore RealChar's Rivia, an AI application leveraging generative AI and machine learning to handle mundane calls and customer service, freeing human time for meaningful tasks. Gain insights on AI challenges and future prospects.