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MLOps Community
The MLOps Community is where machine learning practitioners come together to define and implement MLOps.
Our global community is the default hub for MLOps practitioners to meet other MLOps industry professionals, share their real-world experience and challenges, learn skills and best practices, and collaborate on projects and employment opportunities. We are the world's largest community dedicated to addressing the unique technical and operational challenges of production machine learning systems.
Events
10:00 AM - 9:30 PM, Sep 4 PDT
AI Agent Builder Summit SF
3:30 PM - 10:30 PM, Jul 17 GMT
Agents in Production 2025
4:00 PM - 5:00 PM, Jul 2 GMT
Building AI That Doesn’t Break
Content
Blog
Terraform becomes messy at scale—too much duplication, manual setup, and no orchestration. Terragrunt fixes this by automating state management, reducing repetition, and handling dependencies. In 2025, its new Stacks feature enables reusable infrastructure patterns, making it the better choice for multi-environment setups despite a small learning curve.
Oct 14th, 2025 | Views 94
video
Alex Salazar, CEO of Arcade, argues that chatbots are useless without the power to take action. He claims real value in AI lies in agents that can actually do things—trigger workflows, manage authorizations, and connect to tools like Gmail or Slack. Salazar calls out why most “agents” never make it to production—security nightmares, high costs, latency, and poor accuracy—and says Arcade fixes this by giving developers the tools to build real, authorized agents fast. He challenges the industry’s obsession with data, insisting that AI’s future is about software, not datasets, and that the heavy lifting by OpenAI and Anthropic has already changed the rules.
Oct 13th, 2025 | Views 15
video
Your AI agent isn’t failing because it’s dumb—it’s failing because you refuse to test it. Chiara Caratelli cuts through the hype to show why evaluations—not bigger models or fancier prompts—decide whether agents succeed in the real world. If you’re not stress-testing, simulating, and iterating on failures, you’re not building AI—you’re shipping experiments disguised as products.
Oct 10th, 2025 | Views 301