AI Platform Implementation on Azure Databricks: 8–16 Week Engagement

Energy and Manufacturing companies are deploying AI to drive predictive maintenance, production optimization, and real-time operational decision-making. Most are held back by the absence of a governed, scalable platform capable of running AI workloads in production.

Frame Data AI builds that foundation on Microsoft Azure and Azure Databricks—delivering the architecture, automation, data governance, and tooling required to move from pilot to production at scale.

What This Engagement Delivers This is a fixed-scope implementation spanning 8 to 16 weeks across six workstreams:

Lakehouse Architecture Scalable Delta Lake and Unity Catalog foundation for ML, LLM, and real-time operational data analytics. Includes vector databases, semantic search for RAG, and Azure Event Hubs integration for high-velocity streaming data.

MLOps and LLMOps Full model lifecycle automation using Databricks MLflow and Model Registry—training, deployment, monitoring, drift detection, and retraining. Evaluation pipelines for LLM accuracy and responsible AI compliance included.

RAG and Knowledge Engineering Generative AI pipelines with governed connectors to enterprise data sources. Vector indexing via Mosaic AI Vector Search, document ingestion tooling, and multi-source knowledge retrieval at enterprise scale.

AI Security and Data Governance Role-based access control and data masking via Unity Catalog, secure model endpoints with Azure Private Link, model lineage, audit trails, and responsible AI guardrails for industrial environments.

Integrated Tooling and Orchestration Azure DevOps CI/CD pipelines, Azure Kubernetes Service for model hosting, Databricks Workflows and Azure Data Factory for orchestration, and Databricks Asset Bundles for repeatable workspace configuration.

Observability and Cost Optimization Model performance dashboards, pipeline monitoring, Azure Cost Management integration, cluster right-sizing, and storage lifecycle policies.

Key Deliverables • Production-ready Azure Databricks Lakehouse with Unity Catalog governance • MLflow Model Registry with training and deployment pipelines • Vector Search and RAG pipeline framework • CI/CD pipelines for automated model promotion • Observability dashboards for model performance and cloud cost • RBAC and data lineage controls • Architecture documentation, operational runbooks, and 30-day post-delivery hypercare

About Frame Data AI Frame Data AI serves Energy and Manufacturing enterprises, combining industrial domain expertise with deep Azure Databricks engineering capability across Unity Catalog, MLflow, Mosaic AI, and Workflows. Delivery is led from Houston with nearshore engineering support.