MLOps 2.0: Taming the LLM Lifecycle

 

1. Introduction to MLOps 2.0

Traditional MLOps practices were designed around classical ML models: structured data, small artifacts, predictable behavior, and well-defined training pipelines.
LLMs changed everything. Now you deal with:

• Massive model weights (GBs–TBs)

• Complex distributed training

• Data + prompt + parameter interactions

• New failure modes (hallucination, drift, jailbreaks)

• Continuous evaluation instead of simple accuracy metrics

MLOps 2.0 is the evolution of traditional MLOps to support Large Language Models, multimodal systems, and agentic workflows.

---

2. The LLM Lifecycle (End-to-End)

Stage 1 — Data Engineering for LLMs

LLM data ≠ classical ML data. It includes:

• Instruction datasets

• Conversation logs

• Human feedback (RLAIF/RLHF)

• Negative examples (unsafe/jailbreak attempts)

• Synthetic data generation loops

Key components:

• Data deduplication & clustering

• Toxicity & safety filtering

• Quality scoring

• Long-tail enrichment

Tools: HuggingFace Datasets, Databricks, Snowflake, Truelens, Cleanlab.

---

Stage 2 — Model Selection & Architecture

Decisions include:

• Base model (OpenAI, Claude, Llama, Gemma, Mistral)

• On-prem, cloud, or hybrid

• Embedding model choice

• Quantization level (BF16, FP8, Q4_K_M, AWQ)

• LoRA / QLoRA / AdapterFusion setup

This stage defines:

• Performance vs. latency

• Cost vs. accuracy

• Openness vs. compliance

---

Stage 3 — Fine-Tuning & Alignment

Modern pipelines:

1. Supervised Fine-Tuning (SFT)

• Task-specific datasets

• Role-specific instruction tuning

• Domain adaptation

2. RLHF / RLAIF

• Human or model-generated preference data

• Reward model training

• Proximal Policy Optimization (PPO) or DPO

3. Memory Tuning

• Retrieval-augmented fine-tuning

• Model + embeddings + vector store = hybrid intelligence

4. Guardrail Tuning

• Safety layers

• Content filters

• Jailbreak hardening

---

Stage 4 — Retrieval & Knowledge Integration (RAG 2.0)

Modern LLM systems require:

• Chunking strategies (semantic, hierarchical, windowed)

• Indexing (dense + sparse OR hybrid)

• Re-ranking (Cross-encoder re-rankers)

• Context caching

• Query rewriting / decomposition

RAG 2.0 = RAG + Agent + Memory + Tools

---

Stage 5 — Inference & Orchestration

Handling inference at scale:

• Sharded inference across GPUs

• Token streaming for user-facing apps

• Speculative decoding

• Caching layers (Prompt caches, KV caches)

• Autoscaling GPU clusters

• Cost-aware routing between models

Frameworks: vLLM, TGI, Ray Serve, Sagemaker, KServe.

---

Stage 6 — Evaluation & Observability

Evaluation for LLMs requires new metrics:

• Task accuracy (exact match, BLEU, ROUGE)

• Safety (toxicity, hallucination likelihood)

• Reasoning depth (chain-of-thought quality)

• Consistency (multi-run stability)

• Latency (TTFT, TPOT, throughput)

• Cost per token

Observability components:

• Prompt logs

• Token usage

• Drift detection

• Safety violation detection

• RAG hit/miss rate

Tools: Weights & Biases, Arize, Humanloop, TruLens, WhyLabs.

---

Stage 7 — Deployment & CI/CD for LLMs

MLOps 2.0 introduces:

1. Prompt CI/CD

• Versioned prompts

• A/B testing

• Canary rollout

• Prompt linting and static analysis

2. Model CI

• Model cards

• Linting safety checks

• Regression testing on eval datasets

3. Infrastructure CI

• Autoscaling GPU clusters

• Dependency graph checks

• Vector DB schema tests

---

Stage 8 — Governance & Compliance

Organizations need:

• Audit logs

• Data lineage

• Access controls for models

• PII scrubbing in training & inference

• License compliance (open-source vs. commercial models)

Regulations impacting LLMs:

• EU AI Act

• Digital Services Act

• HIPAA

• SOC2

• GDPR

---

3. MLOps 2.0 Architecture (Blueprint)

Core Layers

1. Data Platform

2. Model Platform

3. Prompt & RAG Platform

4. Inference Platform

5. Evaluation & Monitoring Platform

6. Governance Layer

7. Developer Experience Layer (DX)

Integrated Components

• Unified Feature Store for embeddings

• Prompt registry

• Model registry

• Evaluation dashboard

• Guardrail engine

---

4. MLOps 2.0 vs Traditional MLOps

Area	MLOps 1.0	MLOps 2.0 (LLMs)
Data	Tabular, small	Text, multimodal, huge
Training	Offline, infrequent	Continuous adaptation
Evaluation	Accuracy	Hallucination, safety, reasoning
Deployment	Single model	Model + RAG + Tools
Monitoring	Latency & metrics	Prompt drift, jailbreaks, misuse
Versioning	Code + model	Code + model + data + prompts
Governance	Basic ML policy	Full AI compliance & audits

---

5. Future: MLOps 3.0 (AgentOps)

A preview of where things are going:

• Autonomous agents with tool use

• Live memory + dynamic planning

• Multi-model orchestration

• Self-healing pipelines

• Continual learning in production