Impact of LLMs on the Tech Stack and Product Development - MLOps Podcast #188
Explore the impact of LLMs on tech stacks and product development with Bito's CTO. Gain insights on AI-powered coding tools, model evaluation, and best practices for leveraging AI in software development.
Model Blind Spot Discovery for Better Models and Ingesting Chaos - Mini Summit #2
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.
RAG Has Been Oversimplified - Exploring Complexities in Retrieval Augmented Generation
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.
How Data Platforms Affect Machine Learning and Artificial Intelligence - MLOps Podcast
Explore how data platforms impact AI and ML applications, covering foundations, design patterns, data quality, and team structures for effective ML implementation.
Charting LLMOps Odyssey - Navigating Large Language Model Operations
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.
Handling Multi-Terabyte LLM Checkpoints - MLOps Podcast #228
Explore techniques for handling multi-terabyte LLM checkpoints, including storage options, saving and loading strategies, and infrastructure considerations for large-scale AI model training.
RecSys at Spotify - Building and Evaluating Large-Scale Recommender Systems
Explore Spotify's large-scale recommender systems, foundational embeddings for transfer learning, evaluation methods, and MLOps challenges with Senior ML Engineer Sanket Gupta.
Retrieval Augmented Generation - Techniques and Applications
Explore Retrieval Augmented Generation with Syed Asad. Dive into production issues, embedding models, inference layer experiments, and the evolving landscape of AI engineering in this insightful discussion.
Open Standards Make MLOps Easier and Silos Harder - MLOps Podcast Episode 234
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.
AWS Trainium and Inferentia - Enhancing AI Performance and Cost Efficiency
Explore AWS Trainium and Inferentia's AI accelerators for enhanced performance, cost savings, and seamless integration with popular frameworks. Learn about their benefits and implementation in ML workflows.
Fresh Data and Smart Retrieval: Milvus and Jina CLIP Explained - MLOps Mini Summit 7
Explore vector database updates, Milvus mechanics, and Jina CLIP's novel multi-task contrastive training for enhanced multimodal and text-based retrieval systems. Master data freshness in high-throughput, low-latency scenarios.
Why Data Scientists Should Learn Software Engineering Principles
Learn essential software engineering principles for data scientists to improve code quality, modularity, and readability. Enhance project scalability and model deployment capabilities.
BigQuery as a Feature Store for AI/ML Applications
Discover how to transform BigQuery into an effective feature management system for AI/ML applications. Learn about designing feature tables, addressing challenges, and optimizing workflows without additional software.
Exploring Long Context Language Models - MLOps Reading Group August 2024
Explore the potential of Long-Context Language Models to replace traditional methods like retrieval, RAG, and SQL. Dive into recent research and engage in critical discussions about LCLMs' capabilities and limitations.
Ax: A New Way to Build Complex Workflows with LLMs - MLOps Podcast
Explore Ax, a TypeScript library for building complex LLM workflows. Learn about prompt signatures, tuning, and composable prompts for RAG and agent-powered applications in production environments.
Fine-Tuning ViT Classifier with Custom Data and Docker Inference - Part 4
Fine-tune a ViT classifier with custom data and deploy using Docker and FastAPI for efficient image classification in production environments.
Installing and Using Ollama Framework for Data Science and Machine Learning
Explore Ollama framework installation, usage, and Python integration for generating, embedding, and chatting with AI models.
Azure AI Computer Vision OCR: Image and Document Intelligence Services
Aprende a utilizar Azure AI Services para OCR de imágenes y documentos con Computer Vision y Document Intelligence.
Google Vertex AI Python SDK and Gemini for Data Science and Machine Learning
Explore Google Vertex AI, SDK, and Gemini through practical demonstrations and hands-on coding examples for machine learning and data science applications.
Azure ML: Pipelines with Python SDK for Data Science and Machine Learning
Create ML pipelines with Azure Machine Learning Studio's Python SDK for efficient data science and machine learning workflows on Azure.
Azure ML: Foundation Models Inference Endpoint for Q&A
Explore Azure ML's Model Catalogue and create an Online Inference EndPoint for Foundation QA HuggingFace model predictions. Gain hands-on experience with Azure ML studio and machine learning deployment.