Train and Deploy Models Lightning Fast
Accelerate AI innovation by mastering techniques for rapid model training and deployment, addressing scalability and security challenges in complex AI applications.
From Analytics to AI: How Generative AI Can Unlock Data Insights and Transform Decision-Making
Explore the transition from traditional reporting to AI-enabled teams and learn a framework for leveraging AI to drive business impact.
The Coming Nuclear Winter for AI Chip Companies
Explore the future of AI chip companies and their potential challenges in a rapidly evolving technological landscape.
Data Versioning in Generative AI: A Pathway to Cost-Effective ML
Explore data versioning strategies for Generative AI, focusing on managing large-scale unstructured data and optimizing resource usage in model scoring and API interactions.
Introduction to LangChain and Retrieval Augmented Generation (RAG)
Explore LangChain's core concepts and build LLM-based applications using Retrieval Augmented Generation for document-based question answering.
Scaling AI in Production with PyTorch
Learn to scale AI in production using PyTorch, covering resilient distributed platforms, TorchServe, and strategic partner initiatives for a robust ecosystem.
Is Your Machine Learning Model Trustworthy? Evaluating AI Quality and Reliability
Explore AI quality pillars, their importance, and evaluation methods to ensure trustworthy ML models. Learn automated techniques for assessing model performance, robustness, and explainability.
Production Machine Learning Monitoring: Outliers, Drift, Explainers and Statistical Performance
Dive deep into advanced ML model monitoring techniques, including outlier detection, concept drift, explainability, and performance metrics for robust production deployments.
GenAI: Lessons Learned from Failed Implementations
Explore real-world examples of failed GenAI implementations in large US enterprises, uncovering key lessons and unique challenges beyond common concerns.
How Many Labelled Examples Do You Need for a BERT-sized Model to Beat GPT4 on Predictive Tasks?
Explore the effectiveness of in-context learning vs. labelled data for predictive tasks, comparing BERT-sized models to GPT-4 in text categorization and entity recognition.
Supercharging Search with Language Models (LLMs)
Explore how Instacart revolutionized search using Language Models, enhancing user experience with personalized recommendations and improved results.
The AI Captain: A Study of Machine Learning at the Edge
Explore the Mayflower project's AI breakthroughs and edge computing applications in maritime technology. Gain insights into cutting-edge AI implementation for autonomous navigation.
From 12 Months to 30 Days: Accelerating AI Deployment Through MLOps
Discover Ecolab's MLOps journey: Learn how they built a cloud architecture enabling rapid AI service deployment every 30-90 days to proactively address risks.
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.
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.
Evolved Structures: Generative Design and Digital Manufacturing at NASA
Explore NASA's use of generative design and AI prompt engineering to enhance spaceflight missions, reducing costs and improving performance.
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.