Production ML Serving and Monitoring with Kubernetes
Gain insights into building an MLOps deployment platform using Kubernetes, integrating key technologies for serving and monitoring complex deep learning models.
Rapid Iteration and ML Model Deployment at Dropbox
Discover rapid ML model iteration and deployment techniques used at Dropbox, reducing development time from weeks to under an hour.
Safely Flying the Generative AI Skies - Managing Risks in AI Coding Tools
Explore risks and strategies for safely managing generative AI coding tools in organizations, supporting innovation while minimizing potential hazards.
LLM: From Playgrounds to Production-Ready Pipelines
Explore the journey from LLM playgrounds to enterprise-scale production pipelines, addressing challenges and leveraging MLOps infrastructure for cutting-edge models.
Operationalizing Data-Centric AI - Practical Tools to Quickly Improve ML Datasets
Discover practical tools and algorithmic strategies to automatically identify and address dataset issues, improving ML model performance across various data types.
Is It Too Much to Ask for a Stable Baseline? - Evaluation and Monitoring in Machine Learning Systems
Explore challenges in ML evaluation and monitoring, focusing on stable baselines in changing environments and their implications for traditional and emerging ML systems.
Hyper-Scaling Real-Time Personalization with Privacy via On-Device Computing
Explore on-device computing for real-time personalization, balancing privacy, latency, and scale while reducing cloud reliance and enhancing ML capabilities.
Building Your LLM Factory
Discover strategies for integrating LLMs into enterprise infrastructure, overcoming challenges, and leveraging cloud computing for AI applications.
Your AI Applications Need Guardrails: How to Build Them
Learn to build robust guardrails for AI applications, ensuring consistent and accurate outputs from Large Language Models in production environments.
Getting Higher ROI on MLOps Initiatives
Discover five key lessons for building cohesive MLOps platforms, enhancing self-service capabilities, and maximizing return on investment in machine learning operations.
Stacked Up Series: A Deep Dive Into Integrating Large Language Models Into Your Product
Explore integrating large language models into products with insights from ML experts at DoorDash and VoiceFlow. Gain practical knowledge for successful implementation.
Training LLMs: Lessons from the Trenches
Discover key insights and challenges in training large language models across hundreds of GPUs, from data handling to resource management.
Fine-Tune LLMs or Integrate 3rd Party APIs? A Financial Case Study
Explore LLM integration strategies for MLOps, comparing fine-tuning and API use. Learn key considerations, challenges, and risk mitigation techniques for implementing advanced language models.
ML Platform Beyond Kaggle Paradigm - Policy-Centric Approach for End-to-End Automation
Explore a policy-centric approach to ML platforms, extending beyond model-centric views to enhance engineering productivity and directly improve business metrics.
Retrieval Augmented Generation with LangChain: ChatGPT for Your Data - Part 2
Explore Retrieval Augmented Generation using LangChain. Learn to build and optimize question-answering systems with vector stores, embedding models, and LLMs for private data applications.
Spend Less Time Troubleshooting ML Production Issues
Learn strategies to quickly identify and resolve ML production issues, reducing troubleshooting time and improving model performance for stakeholders.
Introduction to Building ML Microservices - A Hands-On Approach with Music Industry Examples
Build ML microservices using MLServer and Seldon Core. Learn to create inference graphs for A/B testing, shadow deployment, and model monitoring with music industry examples.
Applying GitOps Principles in End-to-End MLOps Projects - Interactive Workshop
Implement GitOps principles in MLOps using DVC to create an end-to-end pipeline centered around a git repository as the single source of truth.
Building a Conversation-Focused LLM on Communication Data
Explore the challenges and opportunities of building Nebula, a conversation-focused LLM trained on communication data, addressing privacy concerns and ML Ops pipelines.
Overcoming the Challenges of Building GenAI Apps
Explore challenges and solutions for deploying successful generative AI products, accelerating time-to-market and driving impact.
Creating the World's Premier Biological Foundation Model
Explore Recursion's groundbreaking biological foundation model, revolutionizing drug discovery through advanced infrastructure, data management, and large-scale training techniques.