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MLOps /LLMOps
- Anand Nerurkar
- Nov 13
- 16 min read
🧠 MLOps vs. LLMOps – The Difference
Aspect | MLOps | LLMOps (or GenAIOps) |
Purpose | Operationalize traditional ML models | Operationalize Large Language Models (LLMs) and GenAI apps |
Model Type | Predictive, classification, regression (e.g., credit scoring, fraud detection) | Generative, conversational, summarization, retrieval-augmented reasoning |
Key Artifacts Managed | Data, features, model weights, metrics | Prompts, embeddings, vector stores, model adapters, RAG pipelines |
Lifecycle Focus | Train → Validate → Deploy → Monitor → Retrain | Prompt design → Fine-tune → Deploy → Evaluate → Reinforce / Optimize |
Examples | Logistic regression, XGBoost, Random Forest | GPT, Llama, Mistral, Claude, Gemini |
Monitoring Focus | Model drift, performance decay, bias | Response quality, hallucination rate, toxicity, factual accuracy |
Tools | MLflow, Kubeflow, Azure ML, SageMaker | LangChain, Prompt Flow, Weaviate, Pinecone, LlamaIndex, Trulens |
Governance Concerns | Data quality, explainability, fairness | Content safety, bias, privacy, Responsible AI guardrails |
⚙️ Where Each Comes into Picture (in Your EA Context)
Let’s map this to your Deutsche Bank–style EA Governance model:
🔹 MLOps
Where: After Curated Data Layer and Feature Store, before deployment of analytical models.
Used For:
Credit Scoring
Risk Prediction
Fraud Detection
Customer Segmentation
Lifecycle Flow:
Curated Data → Feature Engineering → Model Training → Model Registry → CI/CD Deployment → Drift Monitoring → Retraining
Governed By:
AI/ML CoE (Execution)
EARB (Architecture Review)
SARB (Operational Readiness)
Technology Council (Tools, Platforms)
🔹 LLMOps (GenAIOps)
Where: After model selection or fine-tuning of an LLM-based architecture — such as RAG or Agentic systems.
Used For:
Document Summarization
Policy Risk Analysis (GenAI Assistant)
Customer Chatbots
Regulatory Q&A using vector embeddings
Lifecycle Flow:
Document Ingestion → Chunking & Embedding → Vector Store → Prompt Template & Context → LLM (Base / Fine-Tuned) → Evaluation → Guardrails → Deployment → Feedback Loop
Key Steps:
Prompt Engineering & Template Management
Version control of prompts & templates.
Embedding Management
Store document embeddings in a vector DB (Pinecone, FAISS, Azure Search Vector).
Evaluation Loop
Evaluate responses for factual accuracy, hallucination, bias.
Human Feedback Loop
Collect feedback to improve prompts or fine-tune model.
Guardrails
Enforce compliance (no PII leakage, ethical use, domain restrictions).
Continuous Optimization
Update retrieval context, prompt templates, or fine-tuned models.
Governed By:
GenAI CoE (under AI/ML CoE umbrella)
Technology Council (platforms & standards)
Responsible AI Board (safety, ethics, bias)
EARB/SARB for architecture and deployment validation
🏗️ Putting It Together – Combined Lifecycle
Here’s how both coexist in the enterprise:
Data Ingestion → Data Lake (Raw → Curated → Analytics)
↓
Feature Engineering → Feature Store → MLOps Pipeline (for ML)
↓ ↓
Document Ingestion → Embeddings → LLMOps Pipeline (for GenAI)
↓
Deployed Models & LLM Services → Monitored, Governed, Retrained via AI/ML CoE
🧩 Governance Alignment
Governance Body | Role |
Technology Council | Approves MLOps & LLMOps platforms, standards, and tools (Azure ML, MLflow, LangChain, Prompt Flow) |
AI/ML CoE | Defines lifecycle policies, pipelines, and monitoring templates |
Responsible AI Board | Defines fairness, transparency, guardrails, and ethical principles |
EARB (Architecture) | Reviews MLOps / LLMOps pipeline designs and integrations |
SARB (Solution) | Validates production readiness, SLAs, and monitoring coverage |
🧠 In Summary (Interview-Ready Answer)
“MLOps is the automation framework for traditional machine learning models — handling training, deployment, drift monitoring, and retraining. As we move into GenAI, we extend MLOps into LLMOps, which operationalizes Large Language Models — covering prompt management, vector stores, retrieval pipelines, guardrails, and continuous evaluation. In our EA governance, both fall under the AI/ML CoE and are standardized by the Technology Council. MLOps governs structured model lifecycles, while LLMOps ensures safe, explainable, and compliant deployment of GenAI capabilities.”
🏦 Unified AI/ML & GenAI Lifecycle with MLOps + LLMOps (Enterprise View)
This flow covers everything — from data ingestion → AI model → LLM orchestration → governance → continuous monitoring — structured exactly how a global bank (like Deutsche Bank, JP Morgan, or Barclays) would operationalize it.
🧩 1️⃣ Data Foundation Layer (Common for Both MLOps & LLMOps)
Objective: Establish a single governed source of truth for AI-ready data.
Flow:
Source Systems (Core Banking, LOS, CRM, Bureau, APIs)
↓
Data Ingestion (Kafka / Azure Event Hub / Data Factory)
↓
Data Lake Zones:
• Raw Zone – Immutable source data for auditability
• Curated Zone – Cleansed, standardized, enriched datasets
• Analytics Zone – Model-ready datasets and features
↓
Data Catalog (Purview / Collibra) → Metadata, lineage, data classification
Governance Checkpoint:
Data Governance Council ensures quality, privacy (GDPR), and lineage tracking.
EARB validates ingestion & data platform patterns.
⚙️ 2️⃣ MLOps Lifecycle (Traditional AI/ML Models)
Used For: Credit Scoring, Risk, Fraud, Forecasting, Churn, Recommendation.
Flow:
Curated Data → Feature Engineering → Feature Store
↓
Model Training (Azure ML / Databricks / MLflow)
↓
Experiment Tracking (metrics, params, code version)
↓
Model Registry (approved version)
↓
CI/CD Pipeline (Azure DevOps / GitHub Actions)
↓
Deployment (AKS / Azure ML Endpoint / API Gateway)
↓
Monitoring & Drift Detection (Prometheus / Evidently AI)
↓
Auto-Retraining Trigger (if drift detected)
Key MLOps Components:
Data Validation: Great Expectations / Deequ
Experiment Tracking: MLflow
Model Registry: MLflow / Azure ML Registry
Deployment: Docker, AKS, REST API
Monitoring: Grafana, Evidently AI
Automation: CI/CD pipelines, retraining triggers
Governance Touchpoints:
EARB: Architecture review of MLOps pipelines
SARB: Operational readiness & scalability validation
AI/ML CoE: Model lifecycle policy, bias testing templates
Technology Council: Approves MLOps toolset and patterns
🤖 3️⃣ LLMOps Lifecycle (Generative AI Models)
Used For: Document summarization, Risk policy Q&A, Compliance AI Assistants, Customer Chatbots, Research assistants.
Flow:
Document / Knowledge Ingestion (PDF, Policy, Email, Contracts)
↓
Document Preprocessing (OCR / Parsing / Cleaning)
↓
Chunking & Embedding Generation (LangChain / LlamaIndex)
↓
Vector Store (FAISS / Pinecone / Azure AI Search)
↓
Prompt Orchestration (Prompt Flow / LangGraph)
↓
LLM Model Invocation (GPT / Llama / Mistral / Claude / Gemini)
↓
Response Evaluation (TruLens / Ragas / Human Feedback)
↓
Guardrails (AI Safety, PII Filter, Bias Filter)
↓
Deployment (API Endpoint / Chat UI / Workflow Integration)
↓
Monitoring & Optimization (Feedback Loop, Prompt Tuning)
Key LLMOps Components:
Prompt Management & Versioning: Prompt Flow / LangChain Hub
Vector Store: FAISS / Pinecone / Weaviate / Azure Search Vector
Response Evaluation: TruLens / Ragas
Guardrails: Microsoft Presidio, NeMo Guardrails, AI Shield
Human Feedback: Reinforcement learning (RLAIF, RLHF lite)
Governance Touchpoints:
GenAI CoE (under AI/ML CoE): Defines LLMOps standards, prompt testing, vector security.
Responsible AI Board: Ensures safety, fairness, explainability, hallucination control.
Technology Council: Approves LLMOps frameworks & vector DBs.
EARB/SARB: Architecture & deployment validation for GenAI components.
🔄 4️⃣ Unified Continuous Lifecycle Management
Objective: Govern both traditional ML and GenAI models under one enterprise operating model.
Flow:
Data Platform → Model Development (ML / LLM) → Model Registry
↓
Deployment → Monitoring (Performance, Drift, Hallucination)
↓
Evaluation (Fairness, Bias, Explainability, Accuracy)
↓
Feedback Loop → Retraining / Prompt Optimization
Common Monitoring Themes:
Model drift (for ML)
Prompt & response drift (for LLM)
Bias/fairness across demographics
Regulatory compliance (EU AI Act, GDPR, RBI/SEBI AI guidelines)
Governance Alignment:
Layer | Governance Entity | Role |
Strategic | Steering Committee / CTO / CIO | Sets AI vision, funding, compliance direction |
Tactical | Technology Council, AI/ML CoE | Approves platforms, blueprints, standards |
Operational | EARB, SARB, Domain Architects | Ensures implementation alignment and operational readiness |
Federated | BU EA Committees | Implements BU-level AI/GenAI initiatives under central governance |
🧠 5️⃣ Responsible AI Embedded Across Both Pipelines
Key AI/GenAI Principles integrated at every stage:
Fairness: Test models for bias across gender, income, geography
Transparency: Explainable outputs via SHAP / LIME / model cards
Accountability: Traceability from dataset to decision
Security & Privacy: Masking, encryption, PII protection
Human Oversight: Human-in-loop approval for high-risk AI decisions
Artifacts:
Model Cards (for ML)
Prompt Cards (for LLM)
Audit Reports (bias, explainability, fairness)
Compliance Dashboard
🧩 6️⃣ Toolchain Summary
Layer | MLOps Tools | LLMOps Tools |
Data Ingestion | Kafka, ADF, Databricks | ADF, OCR, LangChain loaders |
Data Prep / Validation | Great Expectations | Custom Validators, LangChain Loaders |
Experiment Tracking | MLflow, Azure ML | Prompt Flow, TruLens |
Model Registry | MLflow Registry | Prompt Registry / LangGraph Hub |
Deployment | Docker, AKS, Azure ML | AKS, Azure AI Studio, API Gateway |
Monitoring | Evidently AI, Prometheus | TruLens, Ragas, Grafana |
Governance | Purview, Model Cards | Responsible AI, Guardrails |
🏁 7️⃣ Final Summary (Interview-Ready Statement)
“In our enterprise AI ecosystem, we manage traditional ML models through MLOps — covering training, deployment, and drift monitoring — and Large Language Models through LLMOps, which focuses on prompt orchestration, vector management, and responsible response evaluation. Both lifecycles share a unified data and governance foundation, governed by the AI/ML CoE and overseen by the Technology Council. MLOps ensures consistency and automation for predictive models like Credit Scoring and Fraud Detection, while LLMOps governs GenAI-driven solutions such as Policy Analysis Assistants and Customer Chatbots — ensuring fairness, compliance, and explainability across both.”
==========================
====
🧩 Unified AI/ML + GenAI Architecture (Text Diagram)
┌─────────────────────────────────────────────┐
│ STRATEGIC LAYER │
│ • AI Steering Committee │
│ • CTO / CIO / CDO │
│ • Responsible AI Board │
└─────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────┐
│ TACTICAL LAYER │
│ • Technology Council │
│ • AI/ML & GenAI CoE │
│ • Data Governance Board │
│ • Security & Compliance Board │
└─────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────┐
│ OPERATIONAL LAYER │
│ • EARB – Architecture Review │
│ • SARB – Solution Review │
│ • Domain Architects / BU Leads │
│ • Project Architects │
└─────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────┐
│ FEDERATED BU EA COMMITTEES │
│ • BU-level EA, Data Scientists, MLOps/LLMOps│
│ • Implementation & Feedback Loops │
└─────────────────────────────────────────────┘
│
▼
──────────────────────────────────────────────────────────────────────────────
DATA FOUNDATION LAYER
──────────────────────────────────────────────────────────────────────────────
┌─────────────────────────────────────────────────────────────┐
│ DATA INGESTION & STORAGE │
│ • Source Systems: Core Banking, LOS, CRM, APIs │
│ • Ingestion: Kafka / ADF / Event Hub │
│ • Data Lake Zones: │
│ - Raw Zone (Immutable, Source Data) │
│ - Curated Zone (Cleansed, Standardized, Enriched) │
│ - Analytics Zone (Feature Ready) │
│ • Data Catalog / Lineage (Purview / Collibra) │
└─────────────────────────────────────────────────────────────┘
│
▼
──────────────────────────────────────────────────────────────────────────────
AI/ML & GENAI MODEL DEVELOPMENT LAYERS
──────────────────────────────────────────────────────────────────────────────
┌───────────────────────────────┬──────────────────────────────┐
│ MLOps Pipeline │ LLMOps Pipeline │
├───────────────────────────────┼──────────────────────────────┤
│ • Feature Engineering │ • Document Ingestion (OCR, │
│ (Databricks, Feature Store) │ Parsing, Chunking) │
│ • Model Training (Azure ML, │ • Embedding Generation │
│ MLflow, TensorFlow) │ (LangChain, LlamaIndex) │
│ • Experiment Tracking │ • Vector Store (FAISS, │
│ (MLflow, Weights & Biases) │ Pinecone, Azure Search) │
│ • Model Registry (Versioning) │ • Prompt Orchestration │
│ • Bias & Explainability Tests │ (Prompt Flow, LangGraph) │
│ • Model Approval (CoE + EARB) │ • LLM Inference (OpenAI, │
│ │ Azure OpenAI, Llama, etc.) │
│ │ • Response Evaluation (TruLens│
│ │ Ragas, Human Feedback) │
└───────────────────────────────┴──────────────────────────────┘
│
▼
──────────────────────────────────────────────────────────────────────────────
DEPLOYMENT & OPERATIONS
──────────────────────────────────────────────────────────────────────────────
┌───────────────────────────────┬──────────────────────────────┐
│ MLOps Deployment │ LLMOps Deployment │
├───────────────────────────────┼──────────────────────────────┤
│ • Containerization (Docker) │ • Containerization (Docker) │
│ • Deployment (AKS / ACI) │ • Deployment (AKS / AI Studio)│
│ • Model Serving API Gateway │ • Chat/Agent API Endpoints │
│ • CI/CD (Azure DevOps) │ • CI/CD (Prompt Flow Pipelines)│
│ • Monitoring: Accuracy, Drift │ • Monitoring: Prompt Quality, │
│ (Evidently AI, Grafana) │ Hallucination, Guardrails │
│ • Auto-Retraining (Scheduled) │ • Continuous Prompt Tuning │
└───────────────────────────────┴──────────────────────────────┘
│
▼
──────────────────────────────────────────────────────────────────────────────
MONITORING & GOVERNANCE
──────────────────────────────────────────────────────────────────────────────
┌─────────────────────────────────────────────────────────────┐
│ COMMON GOVERNANCE LAYER │
│ • Model Cards (AI/ML) & Prompt Cards (LLM) │
│ • Responsible AI Dashboard │
│ • Bias & Fairness Audit │
│ • Explainability Reports (SHAP, LIME) │
│ • Model Drift & Performance Metrics │
│ • Compliance with EU AI Act, GDPR, RBI Guidelines │
│ • Feedback Loop to CoE & Model Owners │
└─────────────────────────────────────────────────────────────┘
│
▼
──────────────────────────────────────────────────────────────────────────────
CONTINUOUS IMPROVEMENT
──────────────────────────────────────────────────────────────────────────────
┌─────────────────────────────────────────────────────────────┐
│ • Retraining Triggers (MLOps) │
│ • Prompt Optimization (LLMOps) │
│ • Reinforcement Learning (RLHF / RLAIF) │
│ • Continuous Feedback from Business Users │
│ • Governance Updates via Technology Council │
└─────────────────────────────────────────────────────────────┘
🧠
“In our enterprise, the AI ecosystem runs on a unified data foundation with layered governance — from Strategic Steering down to Operational EA Boards. The MLOps pipeline manages predictive models like credit scoring and fraud detection, handling model training, versioning, deployment, and drift monitoring. The LLMOps pipeline governs GenAI workloads such as document summarization, policy Q&A, and AI copilots — focusing on prompt orchestration, vector storage, and response evaluation. Both are continuously monitored through a Responsible AI layer that enforces fairness, explainability, and compliance, with feedback loops feeding into retraining and prompt optimization. This ensures a consistent, safe, and compliant AI adoption at enterprise scale.”
🧩 Unified AI Platform Layer (Text Diagram)
──────────────────────────────────────────────────────────────────────────────
ENTERPRISE AI PLATFORM LAYER
──────────────────────────────────────────────────────────────────────────────
┌──────────────────────────────────────────────────────────────┐
│ SHARED PLATFORM SERVICES │
│--------------------------------------------------------------│
│ 1️⃣ Data Access & Feature Management │
│ • Feature Store (Azure ML / Databricks) │
│ • Metadata & Lineage (Purview, Collibra) │
│ • Data Access Controls (RBAC, ABAC, PII Masking) │
│ │
│ 2️⃣ Model Lifecycle Services │
│ • Model Registry (MLflow / Azure ML) │
│ • Versioning, Approval Workflow (EARB + CoE) │
│ • Model Deployment APIs (AKS, Azure ML Endpoints) │
│ │
│ 3️⃣ Vector & Embedding Services (for GenAI) │
│ • Vector DB (FAISS, Pinecone, Azure AI Search) │
│ • Embedding Generation (OpenAI / Sentence Transformers) │
│ • Context Retrieval APIs for RAG │
│ │
│ 4️⃣ Prompt Orchestration & LLMOps Layer │
│ • Prompt Templates, Chains, Agents (LangChain, Flow) │
│ • Prompt Versioning & Audit Logs │
│ • Guardrails (Toxicity, Hallucination Filters) │
│ │
│ 5️⃣ CI/CD & MLOps Pipeline Automation │
│ • CI/CD Pipelines (Azure DevOps / GitHub Actions) │
│ • Automated Training / Deployment (MLOps) │
│ • Continuous Evaluation (Model Drift / LLM Feedback) │
│ │
│ 6️⃣ Monitoring & Observability │
│ • Model Monitoring (Evidently AI, Grafana) │
│ • Prompt/Response Quality Metrics (TruLens, Ragas) │
│ • Audit Logs & Metrics for AI Performance Dashboard │
│ │
│ 7️⃣ Responsible AI & Compliance Services │
│ • Bias & Fairness Checker │
│ • Explainability (SHAP, LIME) │
│ • Model Cards / Prompt Cards Repository │
│ • AI Risk Rating (GDPR, EU AI Act, RBI Compliance) │
│ │
│ 8️⃣ Governance Integration Points │
│ • EARB – Architecture Review Workflow │
│ • SARB – Solution Readiness Approval │
│ • AI/ML CoE – Lifecycle Templates, Policies │
│ • Technology Council – Tools & Platform Rationalization │
│ │
│ 9️⃣ Feedback & Continuous Improvement │
│ • Human Feedback Loop (RLAIF / RLHF) │
│ • Automated Retraining Triggers │
│ • Prompt Optimization Recommendations │
└──────────────────────────────────────────────────────────────┘
│
▼
──────────────────────────────────────────────────────────────────────────────
CONSUMER / BUSINESS LAYER
──────────────────────────────────────────────────────────────────────────────
┌──────────────────────────────────────────────────────────────┐
│ • Credit Scoring, Risk Models (via MLOps APIs) │
│ • Customer GenAI Assistants (via LLMOps APIs) │
│ • Compliance Copilot, KYC Validator, Loan Advisor │
│ • Enterprise Chatbots, Regulatory Policy Search │
└──────────────────────────────────────────────────────────────┘
🧠
“we’re enabling AI through a unified AI platform layer that standardizes data, model, and orchestration services across both MLOps and LLMOps. This platform provides common capabilities like model registry, feature store, vector store, prompt orchestration, and Responsible AI monitoring. Both traditional ML and GenAI models share the same DevSecOps and governance backbone — governed by EARB, SARB, and the AI/ML CoE. The outcome is a single, auditable, and compliant platform where credit scoring, fraud detection, document summarization, and customer copilots coexist seamlessly, reducing silos and ensuring AI trust and compliance.”
🧩 Unified AI/ML + GenAI Governance RACI Matrix
──────────────────────────────────────────────────────────────────────────────
LEGEND:
R = Responsible A = Accountable C = Consulted I = Informed
──────────────────────────────────────────────────────────────────────────────
Governance Bodies:
1️⃣ AI Steering Committee / Responsible AI Board
2️⃣ Technology Council
3️⃣ Enterprise Architecture Review Board (EARB)
4️⃣ Solution Architecture Review Board (SARB)
5️⃣ AI/ML & GenAI CoE
6️⃣ Domain / BU Architects
7️⃣ Data Governance Board
──────────────────────────────────────────────────────────────────────────────
| # | Activity / Deliverable | Steering/RAI | Tech Council | EARB | SARB | AI/ML CoE | BU Arch | Data Gov |
|---|------------------------------------------------------|---------------|---------------|------|------|-------------|----------|-----------|
| 1 | Define AI/ML & GenAI Strategy, Vision | A/R | C | I | I | C | I | C |
| 2 | Approve AI/GenAI Principles (Fairness, Explainable) | A/R | C | I | I | C | I | C |
| 3 | Select AI Platforms & Tools (Azure ML, LangChain etc)| I | A/R | C | I | C | I | I |
| 4 | Define Reference Architectures (MLOps, LLMOps) | I | A/R | C | I | C/R | C | I |
| 5 | Create AI Lifecycle Policies (Approval, Retraining) | A/R | C | C | I | R | I | C |
| 6 | Establish Model Approval Workflow (EARB + CoE) | I | I | A/R | C | R | C | I |
| 7 | Approve GenAI Blueprints (RAG, Guardrails, Agents) | I | A/R | C | I | R | C | I |
| 8 | Define Data Governance for AI/ML | C | I | C | I | C | C | A/R |
| 9 | Data Quality & Bias Checks (Fairness, Lineage) | A/R | C | C | I | R | C | R |
|10 | Develop ML/LLM Models (Training, Fine-tuning) | I | I | C | I | A/R | R | C |
|11 | Perform Model Validation & Testing (Bias, Drift) | C | I | A/R | C | R | R | C |
|12 | Manage Model Registry / Vector Store | I | I | C | I | A/R | R | C |
|13 | Deploy Models via CI/CD Pipelines (MLOps/LLMOps) | I | I | C | A/R | R | R | I |
|14 | Implement Responsible AI Controls (Explainability) | A/R | C | C | I | R | C | C |
|15 | AI Monitoring: Drift, Fairness, Prompt Quality | C | I | I | A/R | R | R | C |
|16 | Model Cards / Prompt Cards Publication | I | I | C | I | A/R | R | I |
|17 | Audit & Compliance Review (EU AI Act, GDPR, RBI) | A/R | C | C | I | C | I | R |
|18 | Continuous Improvement (Retraining / Prompt Tuning) | C | I | C | A/R | R | R | C |
|19 | Knowledge Sharing, Templates, Lessons Learned | I | C | I | I | A/R | R | I |
|20 | Periodic Governance Review & Metrics Reporting | A/R | C | C | I | R | I | C |
──────────────────────────────────────────────────────────────────────────────
🧠
“We’ve extended our existing EA governance to clearly define accountability for AI and GenAI initiatives. At the top, the AI Steering Committee / Responsible AI Board owns the ethical and strategic dimensions — fairness, explainability, compliance. The Technology Council defines platforms, standards, and reference blueprints for MLOps and LLMOps. The AI/ML & GenAI CoE acts as the execution authority — responsible for model lifecycle management, bias testing, and publishing model/prompt cards. EARB ensures architectural compliance for all AI workloads, while SARB validates production readiness, security, and SLAs. Finally, the Data Governance Board ensures that underlying data used in training and embeddings complies with privacy, lineage, and quality standards. Together, this RACI structure gives clear ownership from strategy to delivery — ensuring AI/ML and GenAI initiatives are not only innovative but also responsible, compliant, and auditable.”
🧠 Where Does MLOps Start?
MLOps doesn’t start at feature engineering — it starts one step before that, at the model development lifecycle orchestration layer, but it leverages outputs from the data engineering and feature engineering stages.
To be clear:
Phase | Owner | Description | MLOps Involvement |
1. Data Ingestion & Preparation | Data Engineering | Raw data from source systems (core banking, CRM, LOS) → Data Lake → Curated datasets | ✅ Indirect — MLOps consumes curated data, doesn’t manage ingestion |
2. Feature Engineering | Data Science / Feature Engineering Team | Create derived variables (e.g., income-to-debt ratio, credit utilization, age group) and store them in Feature Store | ✅ Partial — MLOps connects to the Feature Store, tracks versions, and automates feature reuse |
3. Model Development | Data Science | Train model using features, tune hyperparameters, test bias & accuracy | ✅ Core MLOps starts here — managing experiment tracking, model versioning, reproducibility |
4. Model Packaging & Registration | MLOps | Package model artifacts, register in Model Registry, record metadata and lineage | ✅ Fully within MLOps |
5. Model Deployment (CI/CD) | MLOps / DevOps | Deploy model to production endpoints (AKS, Azure ML Endpoint, SageMaker, etc.) | ✅ Fully within MLOps |
6. Model Monitoring & Retraining | MLOps | Monitor performance, detect drift, trigger retraining | ✅ Fully within MLOps |
📊 In Summary:
Feature Engineering → Input to MLOpsIt’s a pre-MLOps activity handled by data scientists and data engineers.
MLOps Starts → From Model Experimentation onwardsOnce features are ready, MLOps automates the rest:
Experiment tracking
Model versioning
Deployment
Monitoring
Retraining
🧩 Interview-Ready Answer
“MLOps begins where data engineering hands off feature-ready data.Feature engineering is a critical precursor — it produces reusable, versioned datasets in the feature store.From there, MLOps takes over — automating model training, packaging, deployment, drift monitoring, and retraining through CI/CD pipelines. In short, feature engineering feeds the MLOps pipeline; MLOps operationalizes everything that comes after.”
🧩 Where Does LLMOps Start?
LLMOps (Large Language Model Operations) starts after foundational or fine-tuned LLMs are available, and focuses on operationalizing, monitoring, and optimizing LLM lifecycle — similar to how MLOps operationalizes traditional ML models.
But since LLMs involve prompt engineering, retrieval, context management, and agent orchestration, the boundary is slightly different.
🔁 Step-by-Step Flow — and Where LLMOps Starts
Stage | Description | Responsibility | LLMOps Involvement |
1. Data Collection & Preparation | Collect unstructured data (documents, chats, PDFs, knowledge base) | Data Engineering / GenAI Data Team | ❌ Not directly (DataOps stage) |
2. Data Curation & Chunking | Clean, tokenize, chunk documents, store embeddings in vector DB (e.g., Pinecone, pgvector, FAISS) | AI Engineering / Data Science | ⚠️ Input for LLMOps (pre-processing) |
3. Model Selection / Fine-Tuning | Select base LLM (GPT, LLaMA, Mistral, Claude) and fine-tune or parameter-efficient tune (LoRA, PEFT) | Data Science / AI Team | ✅ LLMOps starts here |
4. Model Packaging & Deployment | Register fine-tuned model, deploy via model registry, endpoint (Azure AI Studio, Sagemaker Jumpstart, Hugging Face Hub) | LLMOps | ✅ Core responsibility |
5. Prompt Engineering & Orchestration | Manage prompts, templates, context injection, tools, agents, memory | AI Engineer / PromptOps / LLMOps | ✅ Core LLMOps — part of runtime orchestration |
6. Retrieval Augmented Generation (RAG) | Integrate vector DB, retriever, LLM for contextual response | AI Engineering / MLOps / LLMOps | ✅ LLMOps manages lifecycle, versioning, observability |
7. Evaluation & Testing | Test LLM with metrics (BLEU, ROUGE, hallucination, factual accuracy, toxicity, bias) | AI QA / LLMOps | ✅ Core LLMOps responsibility |
8. Continuous Monitoring & Feedback Loop | Monitor drift, hallucination, latency, prompt failure, user feedback | LLMOps / Observability Team | ✅ Fully within LLMOps |
9. Continuous Improvement (CI/CD) | Retrain or re-tune based on feedback, update embeddings, prompt versions | LLMOps | ✅ Fully within LLMOps |
🚀 In Short
🧠 MLOps starts at model training → focuses on structured data models (predictive)🧠 LLMOps starts at model fine-tuning or orchestration → focuses on language models (generative)
🧩 Text Visual Diagram
[ DataOps Layer ]
├── Raw → Curated → Analytics Zones
└── Prepares unstructured & structured data
[ Feature / Embedding Engineering ]
├── Create embeddings, metadata, chunk text
└── Stored in vector DB (e.g., Pinecone, pgvector)
[ LLMOps Lifecycle ]
├── Model Fine-Tuning / Adaptation (LoRA, PEFT)
├── Model Packaging & Registry
├── Deployment (API, endpoint, container)
├── Prompt Management (templates, context)
├── RAG Integration & Tool Orchestration
├── Evaluation (factual accuracy, bias, toxicity)
├── Monitoring (drift, hallucination, feedback)
└── Continuous Improvement (CI/CD for LLMs)
🗣️
“LLMOps starts once the data is curated and embeddings are available.It operationalizes the lifecycle of large language models — from fine-tuning, prompt orchestration, and RAG integration to monitoring hallucination, drift, and user feedback. If MLOps is about managing model lifecycle for structured prediction models, LLMOps is about managing conversational and generative models end-to-end — including context, prompts, and human feedback loops.”
🧩 High-Level Definition
Term | Description | Analogy |
MLOps | CI/CD + governance + monitoring framework for traditional AI/ML models (regression, classification, clustering). | “DevOps for ML models.” |
LLMOps | CI/CD + observability + safety framework for Large Language Models (LLMs, RAG, Agents). | “DevOps for Generative AI.” |
💡
“MLOps and LLMOps are both extensions of CI/CD principles to the AI/ML lifecycle — enabling continuous integration, deployment, and monitoring of models. MLOps applies to predictive models, while LLMOps extends those principles to generative models — managing additional layers like prompt orchestration, retrieval pipelines, vector stores, and hallucination monitoring.”
⚙️ How They Map to CI/CD Concepts
CI/CD Concept | MLOps Equivalent | LLMOps Equivalent |
Code Versioning (Git) | Model versioning (Model Registry) | Model & Prompt versioning (LLM Registry) |
Build Pipeline | Feature extraction, model training | Fine-tuning, adapter training (LoRA, PEFT) |
Test Stage | Model validation (accuracy, bias, drift) | LLM evaluation (factual accuracy, toxicity, coherence) |
Deployment Pipeline | Model packaging (Docker, API) | LLM deployment (API, RAG pipeline, prompt orchestration) |
Monitoring & Feedback | Data drift, model drift | Hallucination, latency, feedback-based tuning |
Rollback & Retraining | Retrain model if performance drops | Re-fine-tune or prompt adjust if hallucination spikes |
🧠 Key Difference
MLOps deals with structured or tabular data pipelines → e.g., predicting loan eligibility, churn probability, fraud risk.
LLMOps deals with unstructured text / document / conversational pipelines → e.g., summarizing a policy document, answering customer queries.
🧩 Text Visualization
+----------------------------+
| CI/CD Base |
+----------------------------+
| |
| |
+-------------+ +----------------+
| |
+-----v-----+ +----v-----+
| MLOps | | LLMOps |
+-----------+ +----------+
| Model Dev | | LLM Fine-tuning |
| Train/Test| | Prompt Mgmt |
| Deploy | | RAG Pipeline |
| Monitor | | Drift/Halluc. |
+-----------+ +----------------+
🗣️
“Yes, MLOps and LLMOps can both be seen as CI/CD pipelines for AI — they automate model development, deployment, and monitoring. However, LLMOps extends the scope by managing not just model versions but also prompts, context, embeddings, and safety — making it essential for GenAI lifecycle management.”
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