Sepehr Mohammadi
AI/ML Tech Lead at apotea.se, AWS Community Builder
Stockholm, Sweden
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Sepehr (Sepi) Mohammadi is a tech lead specializing in data, AI, and machine learning, with a proven track record of building cohesive teams and innovative technologies that drive business value. His expertise lies in designing scalable platforms that leverage serverless architecture on the cloud. Sepehr has developed analytical data platforms tailored for analytics engineers and data analysts, as well as robust data infrastructures and MLOps pipelines for machine learning applications. His curiosity and drive for continuous learning fuel a dynamic cycle: learning cutting-edge concepts, building impactful solutions, and rolling them out—whether at work or through personal projects.
Area of Expertise
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Distributed AI/ML Platforms: Zero to Production in 8 Weeks with Serverless
AI and machine learning are evolving fast—businesses can’t afford to spend months building platforms from scratch while competitors surge ahead. But guess what? Putting together a robust, scalable AI/ML platform is easier than it sounds if you have a clear maturity plan and leverage the right tools. From serverless solutions like SageMaker, and Databricks to custom setups pieced together with a few well-chosen services, there’s a world of possibilities waiting for you.
In this talk, I’ll share how we rolled out an AI/ML platform at Apotea in just eight weeks—getting three products into production without the usual headaches. We’ll explore how distributed, serverless architectures can drastically cut down build times while keeping costs and complexity in check. If you’ve ever wondered whether you can scale AI without sacrificing speed or sanity, come learn how to make it happen. Let’s prove that “fast” and “scalable” don’t have to be opposites.
From vision to deployment; GenAI in Production
The generative AI space is crowded with dazzling prototypes—yet fewer than 10% make it to production, and even fewer deliver measurable business value. Why? This talk cuts through the hype to systematically diagnose the root causes, from technical pitfalls (unreliable outputs, integration debt) to organizational blockers (short-term thinking, systematic underinvestment). We continue by illustrating how we at Apotea made it to overcome these challenges, reach production, and achieve sustainable value through clear problem definition, continuous evaluation and validation, and meticulous attention to security.
One data foundation for AI/ML and Analytics; Architectural design with Event-Driven Architecture
In this presentation, we will delve into the architectural design of a unified data platform engineered to support a wide range of use cases, from analytics to advanced AI/ML-driven applications. As businesses rapidly evolve and new AI or ML models emerge regularly, it becomes essential to have a data foundation that is not only scalable but also exceptionally adaptable to integrate new use cases swiftly. We will explore how leveraging an Event-Driven Architecture, can create a scaleable, resilient and flexible architecture that meets these demands. Attendees will gain comprehensive insights into the architectural principles and practical implementation strategies necessary to build a data platform that is equipped to handle today’s analytics demands and tomorrow’s AI challenges.
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