MLOps in Financial Services: Forecasting Balances and Model Deployment
Unlock MLOps potential in finance: master balance forecasting, explore time series packages, and learn production deployment tips for optimized processes and elevated performance.
Forecasting Time Series with Arrow and WASM
Explore novel time-series processing pipeline using Apache Arrow, S3, Polars/DuckDB, and WASM for efficient forecasting and MLOps deployment.
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.
ML in Production: Implementation, Tooling and Engineering - Data MLOps
Explore ML observability in e-commerce, focusing on Loblaw Digital's approach to tracking model performance, user behavior, and business impact using centralized tools and analytics.
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.
Run GenAI Projects at Scale Securely: From the Operating System to the MLOps Platform
Secure and scale GenAI projects across the entire stack, from OS to MLOps platform. Learn key considerations, best practices, and opportunities for highly regulated industries.
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.
The Golden Age of the Platform - Exploring Platform Engineering Today
Explore the future of platform engineering, examining various platform types, their implementation challenges, and strategies for effective deployment to serve organizational needs.
Engineering Cloud Native AI Platform
Explore best practices and challenges in building scalable AI/ML platforms using cloud-native technologies like Kubernetes, Kubeflow, and KServe. Deep dive into a reference platform for modern AI infrastructure.
Argus - MLOps Platform for Secure and Scalable Data Products in Regulated Environments
Explore how architectural decisions, user-centric design, and business value focus led to a successful data and engineering platform in a regulated enterprise environment.
What's Next for Enterprise Architects and Developers
Discover how Google Cloud infrastructure can transform your business, optimize resources, and accelerate innovation with the lowest TCO possible.
Move from Raw Data to Machine Learning Faster with BigQuery and Vertex AI
Accelerate ML development using BigQuery and Vertex AI. Learn to streamline data-to-AI workflows, improve quality, and support governance on Google Cloud.
MLOps in the Era of Generative AI
Explore MLOps in the GenAI era, covering faster experimentation, evolving practices, and optimal LLM usage. Gain insights into data analytics and AI development trends.
The Data Professional in the Era of Generative AI
Explore the convergence of DataOps, MLOps, and LLMs in the era of generative AI. Learn how data analytics and machine learning applications are evolving and creating value for organizations.
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.
Monitoring LLMs in Production: Beyond the Hype - Part 2
Explore techniques for monitoring and measuring drift in unstructured data models, focusing on vector/embedding drift for image and language models in production environments.
Building Production Copilots - Part 2
Explore UI paradigms, design patterns, and challenges of production copilot systems embedded in SaaS applications leveraging LLMs and generative AI models.
It Worked When I Prompted It - Challenges in LLM Production Deployment
Explore challenges and solutions for deploying LLMs in production, covering design patterns, vector databases, reliability enhancement, and cost management strategies.
Building Reliable AI Agents - LLMs in Production Conference Part 2
Explore the fundamentals of building reliable AI agents, their business applications, and key challenges in agentic automation.
Create a Contextual Chatbot with LLM and Vector Database in 10 Minutes
Learn to build a contextual chatbot using LLM and vector database, exploring embedding, search, and answer generation with Langchain.
Wardley Mapping for Prompt Engineering - Part 2
Explore Wardley Mapping for strategic prompt engineering in AI, learning to analyze value chains and target efforts effectively.