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Explore embeddings in text and code with Dr. Neelakantan. Learn how these numerical representations revolutionize NLP tasks, outperform top models, and enhance semantic search and classification.
Explore how the lakehouse combines data lakes and data warehouses in a unified architecture for modern data workloads.
Explore security risks across the machine learning lifecycle, from deployed MLOps infrastructure to the OWASP Top 10 for machine learning.
See how semantic vectors turn keyword search into vector search, with an introduction to production concerns and interacting with a Weaviate database.
See how graph embeddings turn relationships in connected data into signals that machine learning and AI can use.
Explore best practices and open-source tools for implementing Responsible AI, ensuring ethical standards in machine learning applications across industries. Learn to align AI development with human-centric principles.
Explore integrating time-series databases and tools like InfluxDB 3.0 and Bytewax to develop AI-driven applications for effective real-time data capture and management.
Innovative approach using pre-trained and custom ML models to enhance financial data processing and reporting, streamlining workflows and improving efficiency in generating insights for the finance industry.
Learn to integrate generative AI into applications using Azure OpenAI Service. Explore advanced models, enterprise features, and responsible AI practices for improved efficiency and innovation.
Unveiling AI project success: from concept to implementation. Learn key elements, strengths, limitations, and adoption stages. Gain insights on team organization and future trends for effective AI integration.
Discover strategies for enhancing LLM security using red-teaming techniques and Giskard's tools for automatic vulnerability detection in Generative AI applications.
Explore best practices for designing and deploying trusted AI solutions in critical sectors, focusing on human adoption, business alignment, risk management, and data curation challenges.
Learn practical techniques for building safe, fair AI models using open-source tools. Tackle challenges in robustness, labeling errors, bias, and data leakage with hands-on examples for machine learning and NLP projects.
Learn to customize PrivateGPT for unique AI applications using its API, Python SDK, and configuration options. Master context-aware AI development for personal, corporate, or commercial projects.
Discover advanced techniques to leverage LLMs cost-effectively. Learn strategies for using SLMs, LLM distillation, and prompt unit testing to overcome budget constraints while maintaining quality in GenAI projects.
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