Enterprise AI Platform: Experiment to Production Guide

Q: What is an enterprise AI platform?

An enterprise AI platform is shared infrastructure that enables multiple teams to develop, deploy, monitor, and govern AI models. It includes model serving, feature stores, experiment tracking, MLOps pipelines, and governance controls.

Q: How do you move AI from experimentation to production?

Key steps: 1) Standardize development environments, 2) Build automated training pipelines, 3) Implement model validation gates, 4) Create deployment automation (CI/CD for ML), 5) Set up production monitoring, 6) Establish rollback procedures.

Q: What is the difference between MLOps and AI platforms?

MLOps focuses on operational processes (training, deployment, monitoring). An AI platform encompasses MLOps plus data management, feature engineering, governance, experiment tracking, and developer experience — the full lifecycle.

Q: How long does it take to build an enterprise AI platform?

A minimum viable AI platform takes 6-9 months. A mature platform with full governance, multi-team support, and self-service capabilities takes 18-24 months. Start with the most painful bottleneck and iterate.

Satyam Kumar

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Enterprise AI Platforms

Designing Enterprise AI Platforms: From Experimentation to Production at Scale

By Satyam KumarFebruary 20, 202632 min read

enterprise AI platform AI platform design MLOps AI experimentation AI production AI platform architecture

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Satyam Kumar

Founder & AI Architect, AppScale LLP

人工智能和云架构师。帮助团队构建可扩展到数百万的系统。

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Designing Enterprise AI Platforms: From Experimentation to Production at Scale

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