1. What are the main stages of an AgenixHub MLOps rollout?
Quick Answer
Define stages and deliverables for AgenixHub MLOps rollout in faq format
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AgenixHub’s MLOps rollout can be structured into clear stages, each with concrete deliverables so mid‑market teams know exactly what they’re getting and when. The stages below assume private AI (on‑prem or VPC) with LLMs and RAG.
1. What are the main stages of an AgenixHub MLOps rollout?
Q: How does AgenixHub sequence an MLOps rollout?
A typical rollout is organised into five stages:
- Discovery & strategy – Understand goals, current state, and constraints.
- Foundation design – Define MLOps and platform architecture, standards, and governance.
- Platform build & first use case – Implement core MLOps components and take one use case to production.
- Scale‑out & optimisation – Onboard more use cases, harden performance, security, and cost controls.
- Operate & evolve – Continuous improvement, advanced automation, and capability transfer to your team.
Each stage has specific deliverables and “exit criteria” before moving on.
2. What happens in Stage 1 – Discovery & strategy?
Q: What does AgenixHub do first, and what are the outputs?
Activities
- Stakeholder interviews (business, data, IT, security, compliance).
- Inventory of current data, models, infra, and AI experiments.
- Assessment of regulatory and security constraints for private AI.
- Identification of 1–3 priority use cases for an initial MLOps‑enabled rollout.
Key deliverables
- AI & MLOps readiness assessment
- Current‑state map (data, infra, tools, skills).
- Gaps vs desired state for private AI operations.
- Use‑case and ROI brief
- Prioritized list of candidate use cases with high‑level value cases and technical feasibility.
- Target operating model outline
- Initial view of roles and responsibilities (internal vs AgenixHub) for the MLOps pipeline.
3. What is delivered in Stage 2 – Foundation design?
Q: How are the MLOps architecture and standards defined?
Activities
- Design of end‑to‑end MLOps architecture for LLM + RAG (dev → test → prod).
- Decisions on deployment model (on‑prem, private cloud, hybrid), tools, and integration points.
- Definition of standards for versioning, CI/CD, testing, observability, and governance hooks.
Key deliverables
- MLOps architecture blueprint
- Logical and physical diagrams showing:
- Data pipelines (ingestion, transformation, embedding, indexing).
- AI services (gateway, model serving, RAG APIs).
- CI/CD flows and environments (dev, staging, prod).
- Monitoring, logging, and security integration.
- Logical and physical diagrams showing:
- MLOps standards & playbook
- Versioning and branching standards.
- Testing requirements (unit, integration, AI evaluation).
- Deployment patterns (blue‑green/canary).
- Rollback procedures.
- Governance integration plan
- Where privacy/security reviews, DPIAs, and approvals sit in the pipeline.
- How RoPA entries and audit artefacts will be generated.
4. What is built in Stage 3 – Platform build & first use case?
Q: What concrete components does AgenixHub implement to get us to a first production system?
Activities
- Implement core MLOps stack (tools, infra, pipelines) according to the blueprint.
- Integrate source data systems and build initial RAG pipelines.
- Develop and productionize one high‑value use case end‑to‑end.
Key deliverables
- Core MLOps platform (v1)
- Running CI/CD pipelines for AI services.
- Containerized services deployed to managed environments (e.g., Kubernetes).
- Central AI gateway with authentication, routing, and logging.
- Initial observability stack (metrics, logs, dashboards).
- Data & RAG pipelines (v1)
- Ingestion jobs from selected source systems.
- Cleaning, redaction, and metadata enrichment.
- Embedding and indexing workflows into a vector store.
- Productionised first use case
- Application or API integrated with the AI gateway and RAG.
- Evaluation suite and baseline performance report.
- Runbooks for operations (on‑call, incident handling, performance tuning).
- Security & compliance baseline
- Implemented access controls, encryption, log retention, and audit logging for the MLOps components.
- Initial DPIA / risk assessment and documentation for the use case.
5. What happens in Stage 4 – Scale‑out & optimisation?
Q: How do we go from one use case to a multi‑use‑case platform?
Activities
- Onboard additional use cases onto the same MLOps and AI platform.
- Tune performance, reliability, and cost (compute, storage, licenses).
- Extend governance, testing, and monitoring patterns to cover new workloads.
Key deliverables
- Multi‑use‑case MLOps platform (v2)
- Shared pipelines used by multiple teams/use cases.
- Model and prompt registry with metadata per use case.
- Extended test suites and environment configurations.
- Performance and cost optimisation reports
- Analysis of latency, throughput, resource utilisation, and cost per use case.
- Changes implemented (auto‑scaling policies, model routing, caching, right‑sizing).
- Standard onboarding kit for new use cases
- Templates (API contracts, evaluation harness, config examples).
- Checklists for data, security, and governance.
- Process guide: steps to add a new use case into the MLOps pipeline.
- Enhanced governance & risk artefacts
- Updated DPIAs/assessments for additional use cases.
- Portfolio‑level view of AI systems, owners, and risk ratings.
6. What is included in Stage 5 – Operate & evolve?
Q: What does steady‑state, production MLOps look like under AgenixHub?
Activities
- Ongoing operation of the platform (optionally co‑managed with your team).
- Continuous improvement cycles (new models, prompts, RAG changes) via the MLOps pipeline.
- Regular reviews of performance, cost, risk, and user feedback.
Key deliverables
- Run & support model
- Defined SLAs and incident processes.
- On‑call and escalation structure (internal, AgenixHub, and any vendors).
- Periodic health reports for the AI platform.
- Continuous improvement backlog & releases
- Roadmap of planned enhancements and experiments.
- Regular releases executed via the same CI/CD pipeline (with evaluation and approvals).
- Cost, performance, and value dashboard
- KPIs and metrics for utilisation, cost, quality, and business impact per use case.
- Recommendations and actions taken each review cycle.
- Capability handover plan
- Training and documentation so your engineers, data teams, and ops can own more of the MLOps pipeline over time.
- Optional shift from full AgenixHub involvement to targeted expert support (architecture reviews, complex changes, audits).
7. How does AgenixHub adapt these stages to our size and maturity?
Q: We’re mid‑market; can this be lighter‑weight?
Yes. For mid‑market B2B organizations, AgenixHub typically:
- Compresses Stages 1–3 into a 3–6 month program (depending on complexity), prioritising one or two flagship use cases.
- Keeps governance and tooling lean but extensible (start simple, grow as usage and risk increase).
- Offers multiple engagement models:
- AgenixHub‑led build with knowledge transfer.
- Co‑build with your internal team from the outset.
- Advisory overlay if you already have some MLOps in place.
The staged, deliverable‑led rollout ensures you get a working, production MLOps pipeline for private AI quickly, while also establishing the patterns and capabilities needed to scale safely and economically over the following years.
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