Building and Deploying Real-World ML Models with Ray and Weights & Biases
Discover how to leverage Ray and Weights & Biases for scalable MLOps, hyperparameter optimization, and real-world ML model deployment through case studies and practical insights.
No More Runtime Setup - Bundling, Distributing, Deploying, and Scaling LLMs Seamlessly with Ollama Operator
Discover Ollama Operator: simplify LLM deployment, bundling, and scaling across environments. Eliminate runtime complexities and dependency issues for seamless large language model management in Kubernetes.
Getting Started with Computer Vision
Learn AI and machine learning basics through computer vision, exploring model building and deployment using a rock-paper-scissors game example.
The Modern Trolley Problem - Responsible AI Principals
Explore ethical challenges in AI-driven vehicles, from blame allocation to algorithm design. Discover object recognition, obstacle avoidance, and responsible AI principles for sustainable engineering practices.
Vulnerability Management in the World of AI
Explore AI and LLM security, ensuring ethical adherence and robust protection for your organization's AI models and deployments.
Exploring MLOps and LLMOps - Architectures and Best Practices
Dive into MLOps and LLMOps architectures, components, and best practices. Learn strategies for efficient implementation and improved model performance in this comprehensive technical session.
LLM Post-Training Techniques - Lecture 14
Dive into advanced LLM post-training techniques and deployment strategies, exploring optimization methods and practical implementation approaches for efficient language model deployment.
Deploying Deep Learning Models for Inference at Production Scale
Optimize and deploy AI models at scale using NVIDIA TensorRT and Triton Inference Server. Learn production-ready techniques for efficient inference across various platforms and infrastructures.
How to Deploy Machine Learning Models in Production
Learn to train, deploy, and maintain machine learning models in production environments, covering real-time decision-making and scalable frameworks for experimentation and deployment.
An Open Source Approach to Build Guardrails for Deploying ML Models in Healthcare
Explore an open-source framework for responsible ML deployment in healthcare, focusing on evaluation and monitoring across patient sub-populations.
Deep AutoViML for TensorFlow Models and MLOps Workflows
Explore Deep AutoViML for building powerful TensorFlow models and MLOps workflows. Learn to fast prototype data pipelines and models using TF 2.4+ and Keras preprocessing layers.
From Concept to Production - Template for the Entire Machine Learning Journey
Develop a production-ready ML pipeline from concept to deployment, covering data prep, model training, and orchestration using Kubeflow on Google Cloud Platform's Vertex AI.
Unlocking the Potential of Large Models in Production - Best Practices and Solutions
Discover practical solutions for deploying and scaling LLMs in production using cloud-native technologies, focusing on KServe implementation, optimization techniques, and operational best practices.
AI Inference: The Secret to AI's Superpowers
Discover how AI Inference powers data-driven decision making, enabling more accurate predictions and smarter interactions between humans and machines in real-world applications.
Optimizing Search and Discovery Systems - Machine Learning Challenges and Solutions
Discover how Depop's search and ranking team tackles complex machine learning challenges while maintaining model reliability and enabling rapid feature development in a marketplace environment.
MLOps MLFlow - Integration with LlamaIndex
Discover how to integrate MLFlow with LlamaIndex, leveraging OpenAI models and exploring MLflow traces for enhanced machine learning operations and tracking.
Building an Auto-scaling AI Inference Service - From Setup to Deployment
Master the fundamentals of building and scaling AI inference services, from GPU utilization to auto-scaling implementation, Docker configuration, and API endpoint setup for optimal performance.