Getting Started with H2O.ai Managed Cloud

H2O.ai Managed Cloud is a fully managed, isolated AI environment where data preparation, model development, and deployment all run in one place. This first lesson covers what the platform is and the problem it solves. Most teams are not short of ideas, they are slowed down by data living in one system, models being built in another, and deployment happening somewhere else. You will also see the two principles behind the setup. Managed means H2O.ai runs the infrastructure for you, including GPU compute when the workload needs it. Isolated means your environment is dedicated, so data, compute, and workloads are never shared with another customer. Covered in this video: The three capabilities the platform brings together: automated machine learning, generative AI, and model operations Why fragmented tooling causes delays, duplicated effort, and gaps in governance Where H2O Driverless AI, H2O LLM Studio, and Enterprise h2oGPTe sit in the platform What managed and isolated mean for security, privacy, and enterprise governance LINKS H2O.ai University: https://h2o.ai/university Documentation: https://docs.h2o.ai Request access or a demo: https://h2o.ai/demo Support: support@h2o.ai ABOUT H2O.ai H2O.ai builds the platform enterprises use to develop, deploy, and operate AI on their own private data, across predictive machine learning and generative AI. #H2Oai #EnterpriseAI #MLOps

Fraud detection, demand forecasting, document intelligence, and retrieval-augmented generation all run on the same platform, under the same governance model. This lesson maps what you can actually build on H2O.ai Managed Cloud. The platform is structured around three pillars: modelling and automation, where models get built and refined; AI applications and data engineering, where models become systems people can use; and deployment, monitoring, and governance, where AI runs reliably in production. Within that structure, predictive AI answers what is likely to happen, and generative AI answers how you can generate, reason, and automate knowledge. Both work across structured data like transactions and time series, and unstructured data like text, images, and audio. Covered in this video: The three platform pillars and what each one is responsible for Predictive AI use cases: fraud detection, demand forecasting, credit risk, churn prediction, anomaly detection Generative AI use cases: enterprise assistants, document intelligence, summarization, RAG, agentic workflows How operationalization is built in, covering secure deployment, scalable infrastructure, monitoring, and governance LINKS H2O.ai University: https://h2o.ai/university Documentation: https://docs.h2o.ai Request access or a demo: https://h2o.ai/demo Support: support@h2o.ai ABOUT H2O.ai H2O.ai builds the platform enterprises use to develop, deploy, and operate AI on their own private data, across predictive machine learning and generative AI. #GenerativeAI #PredictiveAnalytics #H2Oai

Follow the full activation path for H2O.ai Managed Cloud, from the invitation email through multi-factor authentication to your first login. Access to H2O.ai Managed Cloud runs through an External Okta account. This is the identity platform customers and partners use to reach H2O.ai environments and the support portal. Before you start, your H2O.ai contact is the entry point. If you are evaluating the platform, request access at h2o.ai/demo or through a sales representative, and qualified opportunities may receive a time-bound sandbox. If you are already a customer, your account representative or customer success manager provides access. Covered in this video: Step 1: receive your invitation email and find the secure activation link Step 2: activate your account Step 3: set a password that meets the criteria Step 4: configure multi-factor authentication with a security question and an authenticator app Step 5: reach your environment from the access portal or a custom link Activation links expire after a limited time. If yours has lapsed, contact your administrator or support@h2o.ai for a new one. LINKS H2O.ai University: https://h2o.ai/university Documentation: https://docs.h2o.ai Request access or a demo: https://h2o.ai/demo Support: support@h2o.ai ABOUT H2O.ai H2O.ai builds the platform enterprises use to develop, deploy, and operate AI on their own private data, across predictive machine learning and generative AI. #Okta #EnterpriseAI #H2Oai

A full walkthrough of the H2O.ai Managed Cloud homepage, broken into three zones so you can find anything in the platform quickly. When you log in you land on the homepage. This lesson breaks that dashboard into three zones: the top and centre, the right panel, and the left sidebar. What you see in the sidebar varies with your permissions and your organisation's setup, so you may have access to more or fewer tools than the recording shows. Covered in this video: Zone 1: the workspace selector, What's New, notifications, user profile and API tokens, Start Here tiles, and running-resource panels Zone 2: the core applications panel, your one-click launcher Zone 3, Part 1: Home and the App Store catalogue Zone 3, Part 2: AI Engines, Apps, App Instances, Secrets, Audit Trail, and Workspaces Zone 3, Part 3: Operations, where trained models are deployed and monitored through H2O MLOps Zone 3, Part 4: AI for Documents, with Enterprise h2oGPTe and H2O Document AI The Support button in the bottom right, for tickets and diagnostic logs LINKS H2O.ai University: https://h2o.ai/university Documentation: https://docs.h2o.ai Request access or a demo: https://h2o.ai/demo Support: support@h2o.ai ABOUT H2O.ai H2O.ai builds the platform enterprises use to develop, deploy, and operate AI on their own private data, across predictive machine learning and generative AI. #H2Oai #EnterpriseAI #ProductTour

As an administrator you do not just use H2O.ai Managed Cloud, you govern it. This walkthrough covers who has access, what they consume, and what runs across every workspace. Administrators see the platform-wide view of every engine, app, and instance, not just their own. This lesson covers each admin section in turn, and closes on the resource blocking threshold introduced in the July 2026 release, which lets you cap new resource creation at your contract limit without disrupting anything already running. Covered in this video: AI Engines, Apps, and App Instances across every user and workspace App Secrets managed centrally, and App Aliases for stable, readable URLs OAuth clients for third-party authentication into your environment Workspaces, including archived and deleted ones The H2O MLOps admin dashboard: Single Model, A/B Test, and Champion/Challenger deployments, plus models and runtimes Identity and access management: users, groups, roles, and permissions Environment Management, AI Unit Reporting, and Analytics The resource blocking threshold, and why it is entirely opt-in LINKS H2O.ai University: https://h2o.ai/university Documentation: https://docs.h2o.ai Request access or a demo: https://h2o.ai/demo Support: support@h2o.ai ABOUT H2O.ai H2O.ai builds the platform enterprises use to develop, deploy, and operate AI on their own private data, across predictive machine learning and generative AI. #PlatformAdmin #MLOps #H2Oai

Where to get help with H2O.ai Managed Cloud, and where to go next to build on what you have learned. This closing lesson wraps up the getting started series. You have seen how the platform brings predictive AI, generative AI, and MLOps into one environment, how to access your instance, how to navigate the interface, and how administrators manage users, resources, and governance. From here, the next step is yours: explore the platform, launch your first application, and build your first model. Covered in this video: The Support button in the bottom right of the platform, for tickets and diagnostic logs Reaching the team at support@h2o.ai Product documentation and release notes at docs.h2o.ai H2O.ai University learning paths, product courses, certifications, and tutorials covering H2O Driverless AI, Enterprise h2oGPTe, AI agents, LLMs, H2O Hydrogen Torch, and H2O MLOps LINKS H2O.ai University: https://h2o.ai/university Documentation: https://docs.h2o.ai Request access or a demo: https://h2o.ai/demo Support: support@h2o.ai ABOUT H2O.ai H2O.ai builds the platform enterprises use to develop, deploy, and operate AI on their own private data, across predictive machine learning and generative AI. #H2Oai #AICertification #FreeAICourses