From POC to Production: Deploying & Observing Enterprise AI
Master the operational lifecycle for scaling Generative & Agentic AI reliably, safely, and economically using Databricks as the reference platform.
The world isn't waiting.
Neither should you.
*Based on industry reports from Gartner, McKinsey, and World Economic Forum 2025–2026.
of Fortune 500 companies are piloting multi-agent AI systems
of AI leaders say agentic AI is their top investment priority in 2026
demand-supply gap for certified agentic AI professionals
Generative AI and Agentic AI prototypes can be built quickly. The real challenge begins in production—where enterprises must manage reliability, response quality, latency, cost, security, governance, and continuous improvement.
This webinar presents a practical enterprise framework for taking AI from experimentation to production using Databricks as the reference platform. Move beyond "Build → Deploy → Hope" and master the operational lifecycle required to scale AI applications reliably, safely, and economically at enterprise scale.
The Enterprise AI Production Gap: Understand why successful AI prototypes struggle with enterprise reliability, security, and governance expectations.
Databricks Reference Architecture: Explore an architecture connecting knowledge bases, model serving, MLflow, and Unity Catalog governance.
Evolution to LLMOps & AgentOps: Manage operational practices across prompts, tools, retrieval, memory, workflows, and multi-model execution loops.
The 4 Layers of AI Observability: Track service health, application tracing, AI quality metrics, and governance/cost FinOps.
Quality Gates & Release Control: Establish automated release thresholds, golden datasets, and regression testing before production deployment.
Closed-Loop Operating Model: Connect production telemetry directly back into continuous model evaluation and improvement releases.
Shift from managing basic endpoints to building a repeatable enterprise production lifecycle: BUILD → EVALUATE → DEPLOY → OBSERVE → GOVERN → IMPROVE.
Instrument multi-layer observability that covers infrastructure, execution traces, response quality, safety, and token costs.
Implement Unity Catalog access controls, model lineage, usage visibility, and cost governance at scale.
Integrate evaluation scorers into automated CI/CD deployment pipelines and quality gates.
Enterprise & Solution Architects
Data & AI Architects
AI/ML and GenAI Engineers
MLOps & LLMOps Teams
Data & AI Platform Leaders
AI Governance & Risk Management Teams
Basic understanding of Generative AI concepts, RAG pipelines, or LLM architectures.
Familiarity with cloud data platforms, MLOps concepts, or enterprise software development.
Live Webinar Access & Interactive Q&A Session
Full On-Demand Session Recording
Downloadable Enterprise AI Reference Architecture Slide Deck (PDF)
Learn from the best
Anirban Ghatak
Director, AI & Data Science | ADaSci
Anirban Ghatak is a seasoned AI & Data Science leader with over 21 years of experience building, scaling, and leading analytics, BI, and data science business units with full P&L ownership and C-level reporting. An alumnus of BITS Pilani and IIM Indore, Anirban is an ex-founder and intrapreneur specialized in taking enterprise AI practices to scale. He is a prominent industry voice and speaker on the evolution of work in an Agentic AI world, advocating for the transition from systems of execution to hybrid systems of autonomous orchestration and robust AI governance.