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.
Quick Deploy Model Serving in Ranking Systems
Discover how to streamline model deployment in ranking systems using ElasticSearch plugins and MLeap bundles for efficient, error-free updates and seamless integration with existing infrastructure.
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.
The Critical Missing Component in the Production ML Stack - Data Logging for AI Applications
Explore AI observability tools for ML systems, focusing on logging libraries that enable testing, monitoring, and debugging of AI applications and data pipelines. Learn best practices to enhance MLOps.
Connecting Data Scientists to Production with the Tempo Python SDK
Prepare and test machine learning models locally, then deploy them to production using Tempo Python SDK. Bridge the gap between data scientists and DevOps for faster, more reliable model deployment.
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.
Marius: Machine Learning over Billion-Edge Graphs - 10x Faster and 5x Cheaper
Discover how Marius optimizes machine learning on billion-edge graphs, achieving 10x faster and 5x cheaper training through innovative data flow architecture and resource utilization.
Building Scalable ML Services for Rapid Development in Health and Wellness
Discover how to build and scale ML services for health and wellness products, reducing release time by 85% using open-source tools like DBT, AirFlow, and MLFlow.
Data and Machine Learning at League of Legends
Explore unique data challenges in video games, focusing on model deployment and evolving ML stacks in League of Legends since 2009.
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.
PANAMA: In-Network Aggregation for Shared Machine Learning Clusters
Explore PANAMA, a novel in-network aggregation framework for distributed ML training, enhancing performance and resource sharing in shared clusters.
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.
Productionize with Kubeflow Orchestration and Feast in GCP - Workshop
Develop a machine learning framework using Kubeflow, Feast, and Kafka in GCP. Learn to manage data features, serve endpoints, and monitor model performance with Grafana for real-time and batch predictions.