Fraud Prevention in Online B2B Transactions
Discover cutting-edge fraud prevention strategies for B2B transactions, leveraging ML models, data infrastructure, and MLOps for enhanced precision and collaboration.
Productionizing Deep Learning Models at Scale
Explore emerging patterns and best practices for deploying machine learning and deep learning models in production environments at scale.
Designing a Real-Time Model to Prevent Fake Account Sign-Up
Develop a real-time machine learning service to prevent fake account sign-ups. Learn about statistical challenges, computational scaling, and deployment pitfalls in low-latency ML inference.
Routing to Minimize Cost and Latency in Unify - Demo 03
Optimize LLM query routing based on latency, cost, and quality budgets using Unify's Dynamic Routing with thresholds.
Bringing LLMs Everywhere Through Machine Learning Compilation
Explore machine learning compilation techniques to deploy large language models on diverse devices, from PCs to mobile phones, using GPU acceleration and Apache TVM.
AI-Driven Edge to Core Data Pipelines for Retail
Automate AI/ML model deployment in retail with an opinionated solution for continuous release cycles from edge to core data pipelines.
ML in Production: What Does Production Even Mean
Explore the concept of "production" in machine learning, its challenges, and a step-by-step approach to deploying models effectively. Gain insights from industry expert Dean Pleban.
Accelerating AI Inference Workloads with Google Cloud TPUs and GPUs
Explore key considerations for choosing TPUs and GPUs for AI inference workloads, including cost implications, deployment strategies, and optimization techniques on Google Cloud.
Accelerating Model Deployment with MLOps - Google Cloud Technical Guide for Startups
Explore MLOps on Google Cloud to accelerate model deployment. Learn about challenges, solutions, and tools like Vertex Pipelines, Model Registry, and Model Monitoring through demos and customer success stories.
Jumpstart Your AI/ML Program with Vertex AI Model Garden
Explore Vertex AI Model Garden to easily deploy and manage state-of-the-art AI models, transforming your startup with minimal coding and effort.
Efficiently Scaling and Deploying Large Language Models
Explore efficient scaling and deployment of LLMs, focusing on decentralized approaches, custom model training, and practical insights for building and implementing AI solutions.
Enabling Defense Missions with Local LLMs - Part 2
Explore LeapfrogAI, an open-source toolkit for deploying and managing LLMs in sensitive, regulated environments, addressing challenges in defense missions and air-gapped systems.
Productionizing Health Insurance Appeal Generation - AI in Production
Explore fine-tuning models for health insurance appeals, cloud resource utilization, and on-premises Kubernetes deployment. Learn about unexpected challenges in server setup and graphics card installation.
Production RL and Decision-Making with RLlib
Learn to implement production-ready reinforcement learning and decision-making systems using RLlib, exploring real-world applications, challenges, and best practices.
Introduction to ML Ops and Continuous Delivery for Machine Learning
Explore ML operations and the Continuous Delivery for Machine Learning Model in this concise introduction to MLOps.
RouteLLM - Learning to Route Large Language Models with Preference Data
Optimize LLM deployment with dynamic routing, balancing cost and performance. Learn to train routers using preference data, achieving significant cost reduction while maintaining quality responses.
Harness the Power of GenAI for Business with Databricks and Dataiku
Unlock GenAI potential for business users with Databricks and Dataiku. Learn to scale AI projects, collaborate on model building, and streamline deployment for enterprise-wide innovation and future-ready AI solutions.
Harmonizing Data Intelligence and Data-as-Code Platforms - A Cookbook Approach
Explore strategies for integrating intelligent features in data platforms while addressing conflicts with existing systems. Learn to balance unified vs. decentralized approaches for resilient, adaptable data management.
Predictive ML - How Unilever is Improving its Forecasting
Unilever's reusable predictive framework improves forecasting processes using ML models, enhancing manual predictions and filling gaps for various business metrics at different granularity levels.
Real-time Machine Learning with Pulsar Functions
Explore real-time ML predictions using Apache Pulsar Functions, demonstrated through a food delivery time estimation model incorporating live traffic data and order details.
Tune and Deploy Gemini with Vertex AI and Ground with Cloud Databases
Master Gemini Pro's lifecycle: train on custom datasets, fine-tune, deploy scalable endpoints, evaluate models, and ground with Google Cloud databases for optimal AI performance.