Step‑by‑step MLOps pipeline for AgenixHub production

Quick Answer

A production‑grade MLOps pipeline for AgenixHub’s private AI needs to manage the full lifecycle of LLMs, RAG components, and data pipelines: from data ingestion and experimentation to deployment, monitoring, and continuous improvement. For mid‑market B2B, the goal is a repeatable, automated pipeline that makes changes safe and cheap, instead of one‑off “hero” deployments.

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Below is a step‑by‑step pipeline outline tailored to AgenixHub’s typical architecture.

1. Plan and define requirements

Step 1.1 – Use‑case and KPI definition

Define the business problem, target users, and success metrics (e.g., handle time, accuracy, CSAT, cost per ticket).
Specify functional and non‑functional requirements: latency, uptime, data domains, regulatory constraints. Step 1.2 – Governance and risk gate
Classify the use case (low/medium/high risk) and determine whether a DPIA or AI‑specific risk assessment is required.
Record the processing activity (purposes, lawful basis, data categories) in your RoPA and governance tools. AgenixHub typically provides templates for use‑case charters, KPIs, and risk classification as the entry point into the pipeline.

2. Data ingestion and preparation

Step 2.1 – Data source onboarding

Identify and onboard source systems (CRM, ERP, ticketing, DMS, warehouse).
Implement ingestion pipelines (batch + streaming where needed) using your preferred stack (e.g., ETL/ELT tools, Kafka, Spark). Step 2.2 – Cleaning, transformation, and governance
Apply cleaning, normalization, PII redaction/masking, and classification.
Enforce data‑minimization rules (only relevant fields and history windows). Step 2.3 – Embedding and indexing (RAG pipeline)
Chunk documents/records, compute embeddings, build/update vector indexes.
Implement incremental updates via CDC or event streams. AgenixHub standardizes data and RAG pipelines into reusable DAGs/jobs with config‑driven source definitions, so each new use case reuses the same pattern.

3. Experimentation and model/prompt development

Step 3.1 – Sandbox environment

Provide a controlled dev environment (e.g., notebooks, feature branches) connected to anonymized or minimized data. Step 3.2 – Model and prompt design
Select base model(s) (open‑source or commercial) and design prompts or agent flows.
Implement retrieval logic, ranking, and guardrails (e.g., safety filters, tool restrictions). Step 3.3 – Evaluation datasets and test harness
Build representative evaluation sets and metrics: relevance, factuality, bias, latency, cost per call.
Implement automated tests and “golden conversations” for regression checks. AgenixHub provides evaluation and test harness templates, integrated with CI so every change to prompts, retrieval, or models is tested against these datasets.

4. Packaging and CI for AI services

Step 4.1 – Code, config, and artifact versioning

Track all code, prompts, configs, data schemas, and model references in version control.
Enforce branching strategies and code review for changes. Step 4.2 – Containerization
Package AI services (gateway, RAG APIs, model servers) as containers with minimal dependencies.
Build images via CI on each merge to main. Step 4.3 – Automated testing in CI
Run unit, integration, and AI‑specific tests (evaluation suite, load tests on staging models).
Fail the pipeline on quality or performance regressions. AgenixHub usually sets up standard CI pipelines (linting, tests, container builds, security scans) that apply to all AI services.

5. Deployment and CD to staging and production

Step 5.1 – Deploy to staging

Use infrastructure‑as‑code (e.g., Terraform/Helm) to deploy containers to a staging Kubernetes cluster or equivalent.
Connect staging to test data/indexes and non‑critical integrations. Step 5.2 – Shadow/canary testing
Run shadow tests (AI gets real traffic but responses are not used) or canary releases (small subset of users) to validate behaviour under realistic load. Step 5.3 – Promotion to production
Use automated gates:
- Evaluation metrics above thresholds.
- Latency and error rates acceptable.
- Business owner and risk/governance approvals recorded.
Deploy to production with the ability to roll back quickly. AgenixHub’s CD setup typically supports blue‑green or canary deployments for AI services, with rollout controlled via feature flags and traffic routing.

6. Runtime operations: serving, routing, and scaling

Step 6.1 – Central AI gateway

Route all traffic through an AI gateway that:
- Handles auth, RBAC/ABAC, rate limits.
- Routes to appropriate models (small vs large) and RAG components. Step 6.2 – Autoscaling and resource management
Configure autoscaling based on CPU/GPU utilization, QPS, and latency targets.
Use horizontal and vertical scaling strategies tailored to each service (LLM, retrieval, ETL). Step 6.3 – Cost‑aware routing
Implement routing logic to:
- Use cheaper models or CPU‑based services for simple tasks.
- Reserve premium models/GPU time for complex or high‑value requests. AgenixHub bakes routing strategies and autoscaling policies into the platform so cost and performance can be tuned centrally per use case.

7. Monitoring, logging, and alerting (LLMOps)

Step 7.1 – Technical monitoring

Collect metrics on latency, throughput, errors, resource usage, and queue lengths. Step 7.2 – AI/quality monitoring
Monitor:
- Response quality scores (human or AI evaluation).
- Hallucination or escalation rates.
- Vector recall and retrieval quality in RAG. Step 7.3 – Cost and usage monitoring
Track tokens, requests, and costs per use case/team.
Use dashboards and alerts when thresholds are exceeded. Step 7.4 – Security and compliance logging
Log access, prompts, responses, and data‑source usage in a privacy‑aware way.
Integrate logs with SIEM and audit systems. AgenixHub generally deploys a unified observability stack (metrics + logs + tracing) with AI‑specific panels for quality, safety, and cost.

8. Feedback loops, drift management, and retraining

Step 8.1 – Feedback collection

Capture explicit user feedback (“good/bad answer”), escalation events, and outcome metrics (e.g., whether the AI suggestion was used). Step 8.2 – Drift detection
Monitor input data distributions, retrieval patterns, and performance metrics for drift.
Alert when metrics deviate beyond thresholds. Step 8.3 – Continuous improvement cycles
Schedule regular experiments: new prompts, model variants, or fine‑tunes.
Use A/B testing or interleaving to compare new configurations to baselines.
Trigger partial or full retraining of supporting models (e.g., classifiers, rerankers) when needed. AgenixHub sets up continuous improvement cadences (e.g., monthly/quarterly) where experiment results, drift, and feedback are reviewed with business owners.

9. Governance, documentation, and auditability

Step 9.1 – Model and system registry

Maintain a registry of models, prompts, configs, and data pipelines with metadata (owner, purpose, risk rating, versions). Step 9.2 – Policy and compliance hooks
Embed checks in the MLOps pipeline:
- Privacy/security review before enabling new data sources.
- Risk and governance sign‑off for higher‑risk use cases. Step 9.3 – Audit trail
Record who approved what, which version was live when, and key changes over time.
Make documentation and logs accessible for internal and external audits. AgenixHub leverages your existing GRC tools where possible, and adds AI‑specific registries and checklists that integrate with CI/CD to keep governance and auditability up‑to‑date automatically.

10. Operating model: who runs what?

Step 10.1 – Roles in the pipeline

Data/RAG engineers: data ingestion, transformation, indexing pipelines.
LLM engineers: prompts, RAG logic, model configuration, evaluation.
MLOps/platform engineers: infra, CI/CD, deployment, monitoring.
Security/compliance: reviews, policies, audits.
Product owners: use‑case scope, KPIs, feedback prioritization. Step 10.2 – AgenixHub’s role
Provides a ready‑made MLOps pattern tuned for private LLMs + RAG, including reference pipelines and infra templates.
Supplies specialist engineers and architects to set up and harden the pipeline, and to pair with your teams until they can own day‑to‑day operations.
Offers ongoing platform and process reviews to ensure the MLOps pipeline evolves with new models, tools, and regulatory expectations. Using this step‑by‑step MLOps pipeline, AgenixHub can help you move from ad‑hoc private AI deployments to a repeatable, secure, and cost‑controlled production flow that supports multiple use cases and continuous improvement.

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