LLMOps vs MLOpsKey Differences for AI Engineers and Production AI Teams
MLOps and LLMOps both aim to make AI systems reliable in production — but they serve very different types of systems, with different artifacts, different failure modes, and different toolchains. Understanding when each applies is a foundational skill for any AI engineer or technical team building production AI.
MLOps
Manages traditional ML lifecycle
- → Training data and feature pipelines
- → Model training, versioning, and registry
- → Prediction serving and model drift
- → Accuracy, precision, recall, AUC
LLMOps
Manages LLM application lifecycle
- → Prompts, context, RAG, agents, tools
- → Evaluation, safety, guardrails
- → Token cost, latency, observability
- → Groundedness, faithfulness, task success
AI Engineering often needs both. Enterprise teams running traditional ML models and LLM applications simultaneously may operate MLOps and LLMOps toolchains side by side.
What is the Difference Between LLMOps and MLOps?
Quick answer
MLOps (Machine Learning Operations) focuses on managing traditional machine learning models from data collection and training through deployment and model drift monitoring. The core artifact is a trained model with versioned weights.
LLMOps (Large Language Model Operations) focuses on managing applications powered by large language models — including prompts, context windows, retrieval pipelines (RAG), tool use, agents, output evaluation, safety, cost management, and production behaviour.
The distinction matters because LLM applications fail in ways that traditional ML systems do not — hallucinations, prompt drift, retrieval failures, unsafe tool calls, runaway token costs — and these failures are not addressed by MLOps tooling. Teams building LLM-powered products need LLMOps practices, regardless of whether they also run traditional ML systems.
What is MLOps?
MLOps (Machine Learning Operations) is the discipline of operationalising the traditional machine learning lifecycle. It covers the engineering practices, automation, and tooling that take an ML model from experimentation to reliable, monitored production deployment.
Core MLOps activities
- ▸Collecting and versioning training data
- ▸Feature engineering and feature store management
- ▸Model training, hyperparameter tuning, and experiment tracking
- ▸Model versioning and model registry management
- ▸Model serving infrastructure (batch, real-time, A/B testing)
- ▸Monitoring for data drift, model drift, and prediction quality
- ▸Automated retraining pipelines triggered by drift signals
MLOps emerged to solve the gap between data scientists who train models in notebooks and engineering teams who need those models running reliably in production. It applies DevOps principles — CI/CD, monitoring, automation, collaboration — to the ML model lifecycle.
What is LLMOps?
LLMOps (Large Language Model Operations) is the set of practices, tools, and workflows for building, deploying, monitoring, evaluating, and continuously improving applications powered by large language models. Unlike MLOps, the primary artifact is not a trained model — it is the prompt template, the retrieval pipeline, and the orchestration logic.
Core LLMOps activities
- ▸Prompt design, versioning, testing, and regression detection
- ▸RAG pipeline management: ingestion, chunking, embeddings, retrieval
- ▸Model selection and routing across LLM providers
- ▸Agent tool orchestration, tracing, and safety controls
- ▸Output evaluation: groundedness, faithfulness, hallucination rate, task success
- ▸Monitoring: token usage, cost, latency, retrieval quality, user feedback
- ▸Guardrails, content moderation, and compliance monitoring
LLMOps vs MLOps: Full Comparison
| Dimension | MLOps | LLMOps |
|---|---|---|
| Primary system | Trained ML models (classifiers, regression, forecasting, recommenders) | LLM applications (RAG assistants, agents, copilots, chatbots) |
| Core artifact | Versioned model weights + training code | Prompt templates + retrieval pipeline + orchestration logic |
| Data dependency | Labelled training datasets, feature stores, data pipelines | Documents, knowledge bases, user inputs, tool outputs, context windows |
| Development process | Collect data → engineer features → train → validate → deploy → monitor → retrain | Define use case → design prompts → build RAG/agents → test outputs → deploy → monitor → improve |
| Evaluation | Accuracy, precision, recall, F1, AUC, RMSE on held-out labelled test set | Groundedness, faithfulness, answer quality, hallucination rate, task success, human review |
| Monitoring | Data drift, model drift, prediction distribution, infrastructure health | Prompt regression, retrieval quality, output quality, token cost, latency, safety, user feedback |
| Deployment | Model serving API with versioned model artifact (batch or real-time) | Application workflow: LLM provider + RAG + orchestration + tools + guardrails + evaluation pipeline |
| Failure modes | Model drift, data quality issues, feature pipeline failures, accuracy degradation | Hallucinations, poor retrieval, prompt regressions, unsafe tool calls, cost overruns, slow responses |
| Cost drivers | Training compute (GPUs), inference infrastructure, storage | Token usage (input + output), LLM API calls, embedding, reranking, vector DB queries |
| Human review | Data labelling, bias audits, model performance sign-off | Output sampling, escalation to agents, hallucination review, human-in-the-loop approvals |
| Common tools | MLflow, Weights & Biases, Kubeflow, Seldon, DVC, BentoML, SageMaker | LangSmith, LangChain, LangGraph, RAGAS, Arize, Helicone, Pinecone, OpenTelemetry |
| Example projects | Fraud detection model, demand forecast, churn classifier, recommendation engine | Support RAG assistant, AI agent workflow, document copilot, customer-facing chatbot |
LLMOps vs MLOps: Flow Diagram
How each operational approach flows — from input through deployment to monitoring.
MLOps Pipeline
Training Data
Labelled datasets, feature store
Feature Engineering
Transform raw data into model inputs
Model Training
Optimise weights against a loss function
Model Registry
Version, compare, and approve models
Deployment
Serve predictions via API or batch job
Monitoring
Data drift, model drift, accuracy decay
LLMOps Pipeline
Prompt / Context Design
System prompts, templates, few-shot examples
RAG / Tools / Agents
Retrieval pipeline, vector DB, tool definitions
LLM Orchestration
LangChain, LangGraph, routing, state management
Guardrails & Safety
Content moderation, PII filtering, policy checks
Deployment
API service, streaming, environment configs
Evaluation / Monitoring
Quality, cost, latency, retrieval, user feedback
Lifecycle Comparison
MLOps Lifecycle
Collect and label data
Gather training samples, annotate labels, manage data versioning.
Prepare features
Transform raw data into model-ready features. Build and maintain a feature store.
Train model
Run training experiments, track hyperparameters and metrics, compare runs.
Validate model
Evaluate on held-out test set, compare against baseline, check for bias.
Deploy model
Register model artifact, serve predictions via REST API or batch pipeline.
Monitor drift
Track data drift, model drift, and prediction quality over time.
Retrain
Trigger retraining when drift is detected or performance degrades.
LLMOps Lifecycle
Define use case
Define the problem, success criteria, and acceptable failure modes before building.
Design prompts
Write system prompts, instruction templates, few-shot examples. Version from day one.
Prepare knowledge / context
Ingest documents, chunk text, generate embeddings, index in vector database for RAG.
Choose model and build pipeline
Select LLM provider and model. Build RAG, tools, or agent workflow with LangChain/LangGraph.
Test outputs
Build evaluation test set. Measure groundedness, faithfulness, quality before launch.
Deploy application
Deploy as API with CI/CD, env configs, streaming, and rollback capability.
Monitor and improve
Track quality, cost, latency, user feedback. Improve prompts and retrieval iteratively.
Evaluate continuously
Run eval sets on every change. Catch regressions before they reach users.
Data: Training Data vs Context Data
Data plays a very different role in MLOps and LLMOps. In MLOps, data is the raw material that shapes model weights — quality training data leads to a better model. In LLMOps, the model weights are largely fixed (provided by the LLM provider); data shapes the context the model receives at inference time.
MLOps: Training Data
- ▸Labelled dataset with ground-truth targets
- ▸Feature engineering pipeline transforms raw data
- ▸Data versioning and lineage tracking
- ▸Training / validation / test splits
- ▸Data quality monitoring for drift
- ▸New data triggers model retraining
LLMOps: Context Data
- ▸Documents, policies, knowledge base articles
- ▸Chunked, embedded, and indexed in vector DB
- ▸Retrieved at query time and injected into prompt
- ▸Knowledge base freshness must be maintained
- ▸Poor documents → poor answers, even with great prompts
- ▸Data changes without model retraining
RAG (Retrieval-Augmented Generation) is the primary mechanism by which context data is managed in LLMOps — through document ingestion, chunking, embedding, and vector database retrieval. For a complete production RAG architecture, see the Production RAG System Architecture guide.
Models: Trained ML Models vs Foundation Models
In MLOps, teams often own and train their model weights — the model is a custom artifact tuned on domain-specific data. In LLMOps, most teams use foundation model APIs (OpenAI, Anthropic, Google Gemini, Meta Llama) rather than training their own LLMs. This shifts the model-related concerns significantly.
Model selection and routing
LLMOps teams choose which foundation model handles which request type — balancing capability, cost, and latency. A cheap model may handle simple queries; a more capable model handles complex reasoning. This routing logic is an operational concern that MLOps does not face.
Model version management
LLM providers update models without notice. A prompt that works well on one model version may behave differently on the next. LLMOps teams pin model versions in production and run regression test suites before migrating to new versions.
Fine-tuning as an edge case
Some LLMOps teams fine-tune foundation models for behaviour adaptation (tone, format, instruction following). When they do, elements of MLOps apply to that process. But fine-tuning is the exception, not the standard path — and even fine-tuned LLMs still need the full LLMOps stack for prompt management, evaluation, and monitoring.
Prompts: A Production Artifact in LLMOps
In LLMOps, prompts are not configuration — they are production code. A change to a system prompt can dramatically shift output quality, tone, groundedness, or safety. This is a core difference from MLOps, where a prompt has no equivalent concept.
MLOps: No prompt concept
- —Model inputs are structured feature vectors
- —No natural language instructions
- —Behavior shaped by training, not runtime text
- —Deployment artifact is model weights
LLMOps: Prompts are critical
- ▸System prompt defines LLM behavior at runtime
- ▸Prompts must be versioned and tested like code
- ▸Small wording changes can shift quality significantly
- ▸Prompt regression testing before every deploy
For a deep guide on writing, testing, and managing prompts for production systems, see the LLM Prompt Engineering Guide.
Evaluation: Fixed Metrics vs Quality Assessment
ML evaluation is deterministic — a prediction is right or wrong, measurable against a labelled ground truth. LLM evaluation is qualitative — a response may be helpful, grounded, faithful, and relevant without any single "correct" answer.
MLOps Evaluation Metrics
Accuracy
Fraction of predictions that match the ground truth label
Precision / Recall
Trade-off between false positives and false negatives
F1 Score
Harmonic mean of precision and recall
AUC-ROC
Discrimination ability of a classifier across thresholds
RMSE / MAE
Error magnitude for regression tasks
Log Loss
Penalises confident wrong predictions more heavily
LLMOps Evaluation Metrics
Groundedness
Is the answer supported by the retrieved context?
Faithfulness
Does the answer accurately reflect the source material?
Answer Relevancy
Does the response actually address the user's question?
Hallucination Rate
% of responses containing ungrounded claims
Task Success Rate
% of tasks completed correctly end-to-end
Retrieval Precision / Recall
Are the right context chunks being retrieved?
Monitoring: Statistical Drift vs Quality and Cost Signals
MLOps monitoring is largely statistical — detecting shifts in input distributions or prediction distributions. LLMOps monitoring is multi-dimensional — tracking output quality, retrieval performance, cost, safety, and user satisfaction simultaneously.
MLOps monitors
- 📉Data drift: Input feature distribution shifts from training distribution
- 📊Model drift: Prediction quality degrades on production samples over time
- 🎯Prediction distribution: Unexpected changes in the distribution of output predictions
- ⚙️Infrastructure health: Latency, error rates, memory, and GPU utilisation
- 🔄Pipeline failures: Data pipeline or feature store failures affecting model freshness
LLMOps monitors
- ✍️Prompt regression: Quality changes after prompt or knowledge base updates
- 🔍Retrieval quality: Context precision and recall for RAG queries over time
- ⭐Output quality: Sampled groundedness, faithfulness, and user feedback signals
- 🚫Hallucination rate: Frequency of ungrounded claims in production responses
- 🪙Token cost: Cost per request by model, route, and user segment
- 🤖Tool call failures: Rate and type of failures in agent tool invocations
- ⏱️Latency: Time to first token and end-to-end response time by route
- 🛡️Safety signals: Guardrail triggers, escalations, and content policy violations
Deployment: Model Serving vs Application Workflow
ML model deployment typically serves a single model artifact via a prediction API. LLM application deployment serves a complete application workflow — one that may include multiple LLM calls, a retrieval layer, tool integrations, guardrails, and an evaluation pipeline, all running together.
ML deployment
- ▸Single model artifact (weights + serving code)
- ▸Prediction endpoint: input features → output scores
- ▸A/B testing between model versions
- ▸Canary rollout by traffic percentage
- ▸Batch inference for offline pipelines
- ▸GPU/CPU serving infrastructure
LLM app deployment
- ▸LLM provider API + orchestration (LangChain/LangGraph)
- ▸Vector database for RAG retrieval
- ▸Tool integrations and API connections
- ▸Guardrail layer for safety enforcement
- ▸Streaming response for UX performance
- ▸Evaluation pipeline running in background
Failure Modes: ML Systems vs LLM Systems
The failure modes of LLM systems are fundamentally different from traditional ML systems — and most MLOps tooling is not designed to detect or address them.
MLOps failure examples
Model drift
Real-world data shifts away from the training distribution, degrading prediction accuracy without triggering any errors.
Data quality issues
Missing values, incorrect labels, or upstream schema changes corrupt training data or live feature pipelines.
Feature pipeline failures
ETL or feature engineering job failures cause the model to receive stale or incorrect input features.
Accuracy degradation
Gradual decline in model performance over weeks or months as the world changes and training data becomes outdated.
LLMOps failure examples
Hallucinations
LLM generates confident, plausible-sounding but factually incorrect answers — especially dangerous in medical, legal, or financial contexts.
Poor retrieval
RAG system returns irrelevant or outdated chunks, causing the LLM to generate answers based on wrong context.
Prompt regressions
A prompt change or model version update silently degrades quality across a range of user queries — often undetected without an eval suite.
Unsafe tool calls
In agentic systems, an LLM calls a tool with incorrect parameters or takes an irreversible action without appropriate validation.
Excessive token cost
A loop condition, unexpectedly long context, or multi-step agent workflow generates far more tokens than anticipated — costs spike without warning.
Slow responses
Multi-step retrieval and reranking, long agent loops, or unoptimised context windows push response times beyond acceptable latency limits.
Cost Management
Cost models differ significantly. MLOps cost is dominated by compute infrastructure — GPU training runs and inference servers that can be right-sized and budgeted. LLMOps cost is transactional — every token processed is billed, making cost directly proportional to usage in ways that can be hard to predict.
MLOps cost drivers
- ▸GPU/TPU compute for model training
- ▸Inference infrastructure (always-on servers)
- ▸Feature store storage and compute
- ▸Data pipeline processing costs
- ▸Model registry and experiment tracking
LLMOps cost drivers
- ▸Input + output tokens per LLM API call
- ▸Embedding model calls for RAG indexing
- ▸Vector database queries and storage
- ▸Reranking model invocations
- ▸Agentic loop multiplier (5–20× LLM calls per task)
- ▸Observability and tracing infrastructure
Cost optimisation in LLMOps
Key levers include: caching frequent query responses to avoid redundant LLM calls, routing simple queries to smaller/cheaper models, implementing context length limits, setting per-user or per-route token budgets, and alerting on per-request cost anomalies. Tracking cost per request — not just aggregate spend — is the foundation.
LLMOps and AI Agents
AI agents require the most intensive LLMOps of any LLM application type. Unlike a simple RAG query, an agent executes multi-step plans, calls multiple external tools, manages memory across turns, and can take actions with real-world consequences. Each of these dimensions adds operational complexity that MLOps tooling was never designed to handle.
Tool call tracing
Every tool call — its inputs, outputs, timing, and success/failure — must be logged for debugging and audit.
Cost per run
Multi-step agents can make 5–20 LLM calls per task. Track cumulative cost per run, not per step.
Loop detection
Agents can enter infinite planning loops. Iteration limits and loop detection heuristics are operational requirements.
Human-in-the-loop
High-stakes actions (writes, sends, deletes) require human approval checkpoints. Design this into the agent architecture.
Do AI Teams Need Both MLOps and LLMOps?
The answer depends on what a team is building. Many organisations now run both traditional ML models and LLM applications simultaneously — requiring both disciplines.
Traditional ML-heavy teams
Data science teams building classifiers, recommenders, and forecasting models need MLOps. Training pipelines, feature stores, model registries, and drift monitoring are their core toolchain. LLMOps may be a future addition if the team expands into LLM-powered features.
LLM application teams
Teams building RAG assistants, copilots, AI agents, and chatbots need LLMOps. They rarely need traditional model training infrastructure. Their concerns are prompts, retrieval, evaluation, cost, and safety.
Enterprise AI teams
Large organisations often maintain both: a recommendation engine with MLOps, a fraud model with MLOps, and an employee knowledge assistant with LLMOps. Platform teams may build shared infrastructure that serves both disciplines.
AI Engineering as the bridge
AI Engineering is the discipline that spans application development, LLMOps, and production deployment of AI systems. It is not limited to either MLOps or LLMOps — it encompasses both, and the full stack of skills needed to build and operate production AI systems.
Practical Example: Customer Support Automation
The same business problem — automating customer support — looks very different when approached with MLOps vs LLMOps.
MLOps approach
Collect labelled tickets
Label historical support tickets with category (billing, technical, account) and sentiment.
Train classifier
Train a text classification model to predict ticket category and priority.
Deploy prediction model
Serve predictions via API. Route incoming tickets by predicted category.
Monitor prediction quality
Track accuracy over time. Alert when category prediction accuracy falls below threshold.
Retrain on new tickets
As product features change, retrain on updated labelled data to maintain accuracy.
Output: Ticket routing and priority prediction — does not generate answers.
LLMOps approach
Build RAG over support docs
Ingest product documentation, FAQs, and troubleshooting guides. Chunk, embed, index in vector database.
Design prompts
Write system prompt instructing the LLM to answer using only retrieved context, cite sources, and escalate when uncertain.
Evaluate with test set
Build 100 representative support questions with expected answers. Run RAGAS evaluation before launch.
Deploy support assistant API
Serve via streaming API. Add guardrails for off-topic queries and confidence-based escalation.
Monitor token cost and quality
Track cost per query, hallucination rate, groundedness score, and user satisfaction (thumbs up/down).
Add human review escalation
Flag low-confidence or sensitive responses for human agent review before delivering to user.
Output: Grounded, cited answers to support questions — with monitored quality, cost, and escalation.
Learning Roadmap
Two learning paths — choose based on what you are building.
MLOps Path
For traditional ML systems
Foundation
- →Python (NumPy, Pandas, Scikit-learn)
- →ML fundamentals: supervised, unsupervised, evaluation
Data & Training
- →Data pipelines and feature engineering
- →Model training and experiment tracking (MLflow, W&B)
- →Cross-validation and model selection
Deployment & Ops
- →Model serving (FastAPI, BentoML, SageMaker)
- →Containerisation with Docker
- →Monitoring: data drift, model drift, Evidently
LLMOps Path
For LLM apps, RAG & agents
Foundation
- →Python + LLM APIs (OpenAI, Anthropic)
- →Prompt engineering and system prompt design
RAG & Evaluation
- →RAG pipeline: chunking, embeddings, vector DB
- →LangChain orchestration
- →Evaluation with RAGAS, LangSmith tracing
Agents & Production
- →LangGraph stateful agents, MCP tool integration
- →Deployment: FastAPI + cloud + streaming
- →Monitoring: cost, quality, latency, guardrails
Frequently Asked Questions — LLMOps vs MLOps
What is the main difference between LLMOps and MLOps?+
MLOps manages the lifecycle of traditional machine learning models — from data collection and feature engineering through training, deployment, and model drift monitoring. LLMOps manages the lifecycle of applications powered by large language models — including prompt design, retrieval pipelines (RAG), agent orchestration, output evaluation, cost management, and safety monitoring. The primary development artifact in MLOps is a trained model; in LLMOps it is the prompt template and retrieval configuration.
Is LLMOps a replacement for MLOps?+
No. LLMOps and MLOps address fundamentally different systems. LLMOps is not a newer version of MLOps — it is a different operational discipline for a different type of AI system. Many enterprise AI teams use both: MLOps for traditional ML models (classifiers, forecasting, recommendation systems) and LLMOps for LLM-powered applications (RAG assistants, agents, copilots). The disciplines can coexist within the same organisation.
Do LLM applications need MLOps?+
Usually not in the traditional sense. LLM applications built on foundation model APIs (OpenAI, Anthropic, Google) do not require training pipelines, feature stores, or model registries. They need the LLMOps stack instead: prompt management, retrieval pipeline governance, output evaluation, token cost tracking, and observability. However, if a team is fine-tuning LLMs or building embedding models, those components may benefit from MLOps tooling.
Why do LLM systems need a separate operations approach?+
LLM systems behave very differently from traditional ML models. Their outputs are non-deterministic natural language rather than numeric predictions. Quality is evaluated subjectively (groundedness, faithfulness, relevance) rather than through fixed metrics like accuracy or AUC. The main failure modes — hallucinations, prompt drift, retrieval failures, unsafe outputs — are not addressed by MLOps tooling. LLMOps emerged specifically to handle these production challenges.
How is LLM monitoring different from ML monitoring?+
ML monitoring tracks statistical signals: prediction distribution shifts, data drift in features, model accuracy degradation on labelled validation samples. LLM monitoring tracks qualitative signals: output quality, groundedness (is the answer grounded in retrieved context?), hallucination rate, retrieval precision and recall, prompt regression, token usage and cost per request, tool call failure rate, latency, and user satisfaction signals. LLM monitoring requires natural language quality assessment, not just statistical distribution checks.
How is LLM evaluation different from ML evaluation?+
ML evaluation uses well-defined metrics — accuracy, precision, recall, F1, AUC, RMSE — measured against a labelled test set with deterministic ground truth. LLM evaluation measures answer quality (which has no single correct answer), groundedness (is the claim supported by retrieved context?), faithfulness, retrieval precision and recall, hallucination rate, task success, and safety. These require specialised evaluation frameworks like RAGAS, LLM-as-judge, or human review panels — rather than simple label comparison.
Does RAG belong to LLMOps?+
Yes. RAG (Retrieval-Augmented Generation) is one of the core operational concerns of LLMOps. Managing the document ingestion pipeline, embedding model versions, vector database configuration, retrieval quality evaluation, chunking strategy, and context injection into prompts are all LLMOps concerns. Retrieval failure is one of the most common production failure modes in LLM applications, which is why retrieval monitoring and evaluation are central to LLMOps practice.
Do AI agents need LLMOps?+
Yes — and agents need more intensive LLMOps than simple RAG or chain-based applications. Agents execute multi-step plans, call external tools, manage memory across sessions, and can take irreversible actions. Every tool call, every planning step, and every LLM invocation in an agentic workflow needs to be traced, costed, and evaluated. Agent failure modes — infinite loops, unsafe tool calls, cost explosions, planning failures — require operational controls that go beyond what basic monitoring provides.
What tools are used in LLMOps?+
LLMOps tooling spans: orchestration (LangChain, LangGraph, LlamaIndex), tracing and observability (LangSmith, Arize, Helicone, OpenTelemetry), evaluation (RAGAS, DeepEval, MLflow), vector databases (Pinecone, Weaviate, Qdrant, pgvector), LLM providers (OpenAI, Anthropic, Google Gemini), and deployment platforms (AWS, GCP, Azure, Render). MLOps tooling typically includes MLflow, Weights & Biases, Kubeflow, Seldon, BentoML, DVC, and cloud-native model serving platforms.
Should AI engineers learn MLOps or LLMOps?+
Most AI engineers building LLM-powered applications in 2026 should prioritise LLMOps. The majority of enterprise AI product development now involves LLM APIs, RAG systems, and agentic workflows — not custom model training. MLOps knowledge is valuable if you are joining a team that trains models in-house, works with ML infrastructure, or maintains traditional ML systems alongside LLM applications. For most developers transitioning into AI engineering, LLMOps skills (evaluation, RAG, observability, agents) have higher immediate return.
Can one team use both LLMOps and MLOps?+
Yes, and this is common in mature enterprise AI teams. A recommendation engine built on a trained collaborative filtering model needs MLOps. The support assistant that handles customer queries alongside it needs LLMOps. The data science team may operate both. As organisations expand their AI footprint, they often maintain traditional ML models (classification, forecasting) and LLM applications (assistants, agents, copilots) simultaneously — each requiring its own operational approach.
Which Technovids resource should I read next?+
For a complete explanation of LLMOps — its full scope, lifecycle, components, and tools — read the What is LLMOps guide at /what-is-llmops. For the broader AI engineering discipline that encompasses both, see the AI Engineering guide at /ai-engineering. For structured live training building production RAG systems and AI agents, explore the AI Engineering Course or Production AI Engineering programme.
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