Production by Default. Governed Pipelines. Measurable Quality.

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Why This Matters Now

Most enterprises already have Databricks and a few promising pilots.
What they lack is the operating model that takes AI from proof-of-concept to production.

Without it:

• 80%+ of models never see production
• Governance and cost visibility come too late
• Scripts and notebooks scatter across teams

Ascend AI closes that gap. It’s a pre-built, Databricks-native operating model that unifies CI/CD, policy packs, evaluation, serving, monitoring, and cost controls, all as code.

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What Is Ascend AI: MLOps + LLMOps Foundations?

Ascend AI turns your Lakehouse into a governed AI factory.
It fuses the latest Databricks stack: Delta, Unity Catalog, MLflow 3.x, Lakehouse Monitoring, Vector Search, Mosaic AI Gateway, with Koantek’s automation and best-practice templates.

Every model, agent, and workflow becomes production by default.

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You Get

• Blueprinted CI/CD: Databricks Asset Bundles promote jobs, notebooks, and models across environments with guardrails.
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• Unified Governance: Unity Catalog enforces fine-grained access, lineage, audit, and compliance (HIPAA, GDPR, SOX).
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• One Platform for ML + LLM: MLflow 3.x handles model and prompt versioning, evaluation, and RAG templates.
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• Observability + FinOps: Lakehouse Monitoring and Inference Tables track drift, latency, and true cost per prediction or token.
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• Secure, Repeatable Provisioning: Terraform stands up governed, network-isolated Databricks workspaces in hours.

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Business Impact

• Speed: First production deployment in weeks, not months (healthcare MVPs < 4 weeks).
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• Cost: Up to 60% OPEX reduction by automating 80% of MLOps tasks and right-sizing infrastructure.
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• Risk: Compliance on day one with UC-driven data lineage and PHI-safe access via Mosaic AI Gateway.
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• Scale: Platform teams gain throughput, more use cases without more headcount.

How It Works — A Technical Deep Dive

1. CI/CD Backbone with Databricks Asset Bundles
Everything; pipelines, jobs, models, policies, is declared in bundles and promoted via quality gates. Environments stay synchronized.

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2. Secure Provisioning (Day 0)

‍Terraform sets up workspaces, IAM roles, UC metastores, cluster policies, and private networking. Guardrails are baked in.

3. Feature + Retrieval Fabric‍

Lakeflow pipelines move raw → Bronze/Silver/Gold; the Feature Store registers reusable features; Vector Search manages embeddings for RAG.

4. Model / Agent Factory‍

Two tracks, one standard:

• ML Track: Delta-versioned datasets, evaluation via MLflow 3.x.
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• LLM Track: Agent Bricks (low-code) or DSPy (code-first) with versioned prompts and AI-judge evaluation.
  
  

5. Gated Release‍

Automated evaluation jobs run golden tests + AI judge scoring. Only passing versions are promoted—no manual shortcuts.

6. Serving & Inference‍

Databricks Model Serving and Mosaic AI Gateway handle RBAC, PII scrubbing, rate limits, and detailed logs.

7. Monitoring & FinOps

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Lakehouse Monitoring tracks data quality and model health.
Inference Tables log predictions and responses for drift analysis.
FinOps dashboards reveal real $/prediction and $/1K tokens—so AI spend stays predictable.

Who It’s For

• Platform Engineering / MLOps: standardize releases and governance.
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• ML Engineers / Data Scientists: focus on features and metrics, not plumbing.
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• CIO / CDO: gain governed AI velocity with clear ROI and risk controls.
  
  

Get Started

Ascend AI Strategy Workshop (free):
Map value to use cases and readiness in 90 minutes.

Live Demo / PoC:
Watch a governed pipeline move from dev → prod in real time.

Scope Alignment:
Receive a tailored roadmap (phases, milestones, effort) for executive buy-in.

Contact: sales@koantek.com