LLMOps: A Complete Guide to Production-Ready AI Systems

Introduction

Deploying a large language model in production is fundamentally different from running it in a notebook. While tools like ChatGPT make AI seem effortlessly simple, building reliable, scalable LLM applications for enterprise use requires a completely different operational framework. This is where LLMOps—Large Language Model Operations—becomes essential.

LLMOps represents the evolution of MLOps practices, specifically adapted for the unique challenges of foundation models like GPT-4, Claude, and LLaMA. Unlike traditional machine learning models that produce deterministic outputs, LLMs are probabilistic, computationally intensive, and require continuous monitoring for issues like hallucinations, bias, and context drift. According to recent surveys, 96% of enterprises are using generative AI for multiple use cases, yet over 25% cite compliance and performance concerns as barriers to external-facing deployments.

In this comprehensive guide, you’ll learn the fundamentals of LLMOps, understand how it differs from traditional MLOps, explore the complete lifecycle from development to monitoring, and discover practical implementation strategies with real-world examples. Whether you’re a data scientist, ML engineer, or DevOps professional, this article will equip you with the knowledge to operationalize LLMs effectively.

Prerequisites

Before diving into LLMOps, you should have:

• Foundational ML knowledge: Understanding of machine learning concepts, model training, and evaluation metrics
• Python proficiency: Familiarity with Python for data processing and API integration
• DevOps basics: Knowledge of CI/CD pipelines, containerization (Docker), and orchestration tools
• Cloud platform experience: Basic understanding of cloud services (AWS, Azure, or GCP)
• LLM familiarity: Experience working with LLM APIs (OpenAI, Anthropic, or open-source models)
• Version control: Proficiency with Git and collaborative development workflows

While not strictly required, experience with vector databases, prompt engineering, and observability tools will be beneficial.

Understanding LLMOps: Core Concepts

LLMOps stands for Large Language Model Operations—a specialized discipline that extends MLOps practices to address the unique operational challenges of deploying and managing large language models in production environments. Think of it as MLOps tailored for the specific demands of foundation models.

What Makes LLMOps Different from MLOps?

While LLMOps builds on MLOps principles, several key differences set it apart:

Model Architecture and Size: Traditional ML models typically range from kilobytes to a few gigabytes. LLMs, however, can exceed hundreds of gigabytes with billions or trillions of parameters. GPT-4, for instance, is estimated to have over 1 trillion parameters, requiring specialized infrastructure for training and inference.

Training Paradigm: Classical ML models are usually trained from scratch on task-specific data. LLMs leverage transfer learning—starting from pre-trained foundation models and fine-tuning them with domain-specific data. This shift changes the entire development workflow, emphasizing prompt engineering and parameter-efficient fine-tuning over full retraining.

Evaluation Complexity: ML models use clear metrics like accuracy, precision, and F1 score. LLMs require different evaluation approaches including ROUGE (Recall-Oriented Understudy for Gisting Evaluation), BLEU (Bilingual Evaluation Understudy), and human feedback loops. Measuring “hallucination rate” or “response relevance” is inherently more subjective than traditional metrics.

Computational Resources: LLMs demand GPU-accelerated infrastructure for both training and inference. A single inference request to a 70B parameter model can cost significantly more than traditional ML predictions, making cost optimization through techniques like model quantization and caching critical.

Human Feedback Integration: Unlike traditional ML, LLMs benefit substantially from Reinforcement Learning from Human Feedback (RLHF). Continuous human evaluation and feedback loops are essential for maintaining quality in production.

The LLMOps Ecosystem

The LLMOps landscape consists of several interconnected components:

Foundation Models: Pre-trained models like GPT-4, Claude, LLaMA, or Mistral that serve as the base for your applications.

Vector Databases: Systems like Chroma, Pinecone, or Weaviate that store embeddings for efficient retrieval in RAG (Retrieval-Augmented Generation) applications.

Prompt Engineering Tools: Platforms like LangChain, Humanloop, or Portkey that help manage, version, and optimize prompts.

Model Serving Infrastructure: Frameworks like vLLM, TensorRT-LLM, or cloud services that handle model deployment and inference optimization.

Observability Platforms: Tools like Weights & Biases, LangSmith, or Arize that monitor model performance, track costs, and detect issues.

The LLMOps Lifecycle: From Development to Production

The LLMOps lifecycle consists of five interconnected stages, each with specific practices and considerations.

Stage 1: Data Engineering and Preparation

Quality data is the foundation of effective LLMs. Unlike traditional ML where you might work with structured CSV files, LLM data engineering involves managing massive text corpora, creating embeddings, and building retrieval systems.

Data Collection: Aggregate data from diverse sources—internal documents, knowledge bases, customer interactions, or public datasets. For a customer support chatbot, this might include historical tickets, product documentation, and FAQs.

Data Cleaning and Preprocessing: Remove duplicates, handle missing values, and normalize text. For LLMs, this includes:

• Removing HTML tags and special characters
• Normalizing whitespace and encoding
• Filtering out toxic or biased content
• Deduplicating similar documents

Tokenization and Chunking: Break documents into manageable chunks (typically 500-1000 tokens) that fit within the model’s context window. Overlap chunks by 10-20% to maintain context continuity.

# Example: Document chunking for RAG applications
from langchain.text_splitter import RecursiveCharacterTextSplitter

def chunk_documents(documents, chunk_size=1000, overlap=200):
    """
    Split documents into chunks for vector database storage.
    
    Args:
        documents: List of document texts
        chunk_size: Maximum tokens per chunk
        overlap: Number of overlapping tokens between chunks
    """
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=chunk_size,
        chunk_overlap=overlap,
        length_function=len,
        separators=["\n\n", "\n", " ", ""]
    )
    
    chunks = []
    for doc in documents:
        doc_chunks = text_splitter.split_text(doc)
        chunks.extend(doc_chunks)
    
    return chunks

# Usage
documents = load_your_documents()
processed_chunks = chunk_documents(documents)
print(f"Created {len(processed_chunks)} chunks from {len(documents)} documents")

Vector Embeddings: Convert text into numerical representations that capture semantic meaning. Use embedding models like OpenAI’s text-embedding-3-large or open-source alternatives like sentence-transformers.

Stage 2: Model Selection and Fine-tuning

Most organizations don’t train LLMs from scratch—it’s prohibitively expensive. Instead, you’ll choose between three approaches:

Approach 1: API-based Models (fastest, least control) Use models like GPT-4, Claude, or Gemini via API. Best for rapid prototyping and applications where data privacy allows cloud processing.

Approach 2: Self-hosted Open-source Models (moderate effort, good control) Deploy models like LLaMA 3, Mistral, or Qwen on your infrastructure. Offers customization while maintaining data privacy.

Approach 3: Custom Training (highest effort, maximum control) Train domain-specific models from scratch or significantly modify existing architectures. Rarely justified except for specialized applications.

Fine-tuning Strategies:

Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) allow you to adapt models with minimal computational resources:

# Example: Fine-tuning with LoRA using Hugging Face
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer

def setup_lora_model(base_model_name, lora_r=8, lora_alpha=32):
    """
    Configure a model for LoRA fine-tuning.
    
    Args:
        base_model_name: HuggingFace model identifier
        lora_r: LoRA rank (lower = fewer parameters)
        lora_alpha: LoRA scaling factor
    """
    # Load base model
    model = AutoModelForCausalLM.from_pretrained(
        base_model_name,
        device_map="auto",
        trust_remote_code=True
    )
    
    # Configure LoRA
    lora_config = LoraConfig(
        r=lora_r,
        lora_alpha=lora_alpha,
        target_modules=["q_proj", "v_proj"],
        lora_dropout=0.05,
        bias="none",
        task_type="CAUSAL_LM"
    )
    
    # Apply LoRA to model
    model = get_peft_model(model, lora_config)
    
    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    total_params = sum(p.numel() for p in model.parameters())
    
    print(f"Trainable parameters: {trainable_params:,} ({100 * trainable_params / total_params:.2f}%)")
    
    return model

# Usage: Fine-tune LLaMA 2 7B with LoRA
model = setup_lora_model("meta-llama/Llama-2-7b-hf")

Stage 3: Prompt Engineering and Optimization

Effective prompts are critical for LLM performance. Prompt engineering involves crafting instructions that consistently produce desired outputs.

Prompt Structure Best Practices:

1. Be explicit: Clearly state what you want, format expectations, and constraints
2. Provide examples: Few-shot prompting with 2-3 examples improves consistency
3. Set context boundaries: Define what the model should and shouldn’t do
4. Iterative refinement: Test variations and measure performance

# Example: Structured prompt template with validation
from typing import Dict, List

class PromptTemplate:
    """Manage and version prompt templates."""
    
    def __init__(self, system_prompt: str, user_template: str, version: str):
        self.system_prompt = system_prompt
        self.user_template = user_template
        self.version = version
        
    def format(self, variables: Dict[str, str]) -> List[Dict[str, str]]:
        """Format prompt with variables."""
        return [
            {"role": "system", "content": self.system_prompt},
            {"role": "user", "content": self.user_template.format(**variables)}
        ]

# Example: Customer support classification
support_prompt = PromptTemplate(
    system_prompt="""You are a customer support ticket classifier. 
    Categorize tickets into: Technical, Billing, General, or Urgent.
    Respond with only the category name.""",
    user_template="""Ticket: {ticket_text}
    
    Category:""",
    version="v1.2"
)

# Usage
ticket = "My account was charged twice for the same order"
messages = support_prompt.format({"ticket_text": ticket})

Prompt Management: Use version control for prompts, track performance metrics per prompt version, and implement A/B testing to optimize results.

Stage 4: Deployment and Model Serving

Deployment involves making your model accessible via API while ensuring low latency, high availability, and cost efficiency.

Infrastructure Considerations:

GPU Selection: Choose appropriate GPUs based on model size:

• 7B parameters: NVIDIA T4 or A10G (16-24GB VRAM)
• 13B parameters: A100 (40GB) or H100
• 70B+ parameters: Multi-GPU setup with A100 80GB or H100

Optimization Techniques:

# Example: Model quantization for efficient inference
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

def load_quantized_model(model_name, quantization="8bit"):
    """
    Load model with quantization for reduced memory usage.
    
    Args:
        model_name: HuggingFace model identifier
        quantization: "4bit" or "8bit"
    """
    if quantization == "4bit":
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype="float16",
            bnb_4bit_use_double_quant=True
        )
    else:
        bnb_config = BitsAndBytesConfig(load_in_8bit=True)
    
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        quantization_config=bnb_config,
        device_map="auto"
    )
    
    return model

# 4-bit quantization can reduce memory by 75%
model = load_quantized_model("mistralai/Mistral-7B-v0.1", quantization="4bit")

Containerization with Docker:

# Dockerfile for LLM inference service
FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04

# Install Python and dependencies
RUN apt-get update && apt-get install -y python3.10 python3-pip
WORKDIR /app

# Install inference dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy model and application code
COPY ./model /app/model
COPY ./src /app/src

# Expose API port
EXPOSE 8000

# Run inference server
CMD ["python3", "src/serve.py", "--model-path", "/app/model", "--port", "8000"]

CI/CD Pipeline: Automate testing and deployment using tools like GitHub Actions, GitLab CI/CD, or Jenkins:

# .github/workflows/deploy.yml
name: Deploy LLM Service

on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Run prompt regression tests
        run: |
          python -m pytest tests/test_prompts.py
      
      - name: Build Docker image
        run: |
          docker build -t llm-service:${{ github.sha }} .
      
      - name: Deploy to staging
        run: |
          kubectl apply -f k8s/staging/
          kubectl set image deployment/llm-service llm-service=llm-service:${{ github.sha }}

Stage 5: Monitoring and Continuous Improvement

Production LLMs require constant monitoring across multiple dimensions.

Key Metrics to Track:

Performance Metrics:

• Latency (P50, P95, P99 response times)
• Throughput (requests per second)
• Token usage and cost per request
• GPU utilization and memory consumption

Quality Metrics:

• Response relevance score
• Hallucination rate
• Toxicity detection
• User satisfaction (thumbs up/down)

Operational Metrics:

• Error rates and types
• Cache hit rates
• API availability (uptime)
• Data drift detection

# Example: Basic monitoring with custom metrics
from datetime import datetime
from typing import Dict
import time

class LLMMonitor:
    """Track LLM performance and quality metrics."""
    
    def __init__(self):
        self.metrics = []
    
    def log_request(self, 
                   prompt: str, 
                   response: str,
                   latency: float,
                   tokens_used: int,
                   model: str):
        """Log individual request metrics."""
        
        metric = {
            "timestamp": datetime.now().isoformat(),
            "model": model,
            "latency_ms": latency * 1000,
            "tokens": tokens_used,
            "prompt_length": len(prompt),
            "response_length": len(response),
            "cost_estimate": self._calculate_cost(tokens_used, model)
        }
        
        self.metrics.append(metric)
        
        # Alert on high latency
        if latency > 5.0:
            self._alert(f"High latency detected: {latency:.2f}s")
    
    def _calculate_cost(self, tokens: int, model: str) -> float:
        """Estimate cost based on token usage."""
        # Example pricing (adjust for your provider)
        pricing = {
            "gpt-4": 0.03 / 1000,  # $0.03 per 1K tokens
            "gpt-3.5": 0.002 / 1000
        }
        return tokens * pricing.get(model, 0)
    
    def _alert(self, message: str):
        """Send alert (integrate with your monitoring system)."""
        print(f"[ALERT] {message}")
    
    def get_summary(self) -> Dict:
        """Get performance summary."""
        if not self.metrics:
            return {}
        
        latencies = [m["latency_ms"] for m in self.metrics]
        costs = [m["cost_estimate"] for m in self.metrics]
        
        return {
            "total_requests": len(self.metrics),
            "avg_latency_ms": sum(latencies) / len(latencies),
            "p95_latency_ms": sorted(latencies)[int(len(latencies) * 0.95)],
            "total_cost": sum(costs),
            "avg_cost_per_request": sum(costs) / len(costs)
        }

# Usage
monitor = LLMMonitor()

start = time.time()
# ... make LLM request ...
latency = time.time() - start

monitor.log_request(
    prompt="Summarize this document...",
    response="Summary: ...",
    latency=latency,
    tokens_used=450,
    model="gpt-4"
)

print(monitor.get_summary())

Best Practices for Production LLMOps

Based on successful enterprise implementations, here are critical best practices:

1. Implement Robust Evaluation Pipelines

Don’t rely solely on manual testing. Create automated evaluation sets that cover edge cases, common queries, and adversarial inputs. Use both automated metrics (ROUGE, semantic similarity) and human evaluation for nuanced quality assessment.

2. Version Everything

Version control isn’t just for code. Track versions of:

• Prompts and prompt templates
• Model weights and configurations
• Training datasets and embeddings
• Evaluation metrics and test sets

Tools like DVC (Data Version Control) or MLflow can help manage this complexity.

3. Design for Failure

LLMs will fail—they’ll hallucinate, refuse valid requests, or produce biased outputs. Build defensive systems:

• Implement fallback responses for low-confidence outputs
• Add content moderation filters
• Use confidence thresholds to trigger human review
• Log failures for continuous improvement

4. Optimize for Cost

LLM inference is expensive. Reduce costs through:

• Caching: Store responses for identical or similar queries
• Request batching: Process multiple requests together
• Model routing: Use smaller models for simple queries, larger ones for complex tasks
• Prompt compression: Remove unnecessary tokens while preserving meaning

5. Embrace the Fine-tuning Flywheel

Continuously improve your models using production data:

1. Deploy initial model with prompt engineering
2. Collect user interactions and feedback
3. Filter high-quality examples
4. Fine-tune smaller, specialized model
5. Deploy improved model and repeat

This pattern, successfully used by GitHub Copilot, can dramatically improve performance while reducing costs.

Common Pitfalls and Troubleshooting

Issue 1: High Latency and Slow Responses

Symptoms: Response times exceed 3-5 seconds, poor user experience

Root Causes:

• Inefficient prompt design (too many tokens)
• Large model size relative to hardware
• Network bottlenecks
• Cold start problems

Solutions:

• Use model quantization (8-bit or 4-bit) to reduce memory requirements
• Implement streaming responses for better perceived performance
• Deploy models closer to users (edge deployment)
• Keep models “warm” with periodic health checks
• Consider smaller, task-specific models instead of general-purpose large ones

Issue 2: Hallucinations and Factual Errors

Symptoms: Model generates plausible but incorrect information

Root Causes:

• Insufficient context or outdated training data
• Over-reliance on parametric knowledge
• Poorly designed prompts

Solutions:

• Implement RAG (Retrieval-Augmented Generation) with up-to-date knowledge bases
• Use chain-of-thought prompting to improve reasoning
• Add confidence scoring and citation requirements
• Implement fact-checking layers using external APIs
• Fine-tune on domain-specific accurate data

# Example: RAG implementation to reduce hallucinations
from typing import List
import chromadb

class RAGSystem:
    """Retrieval-Augmented Generation to ground responses in facts."""
    
    def __init__(self, collection_name: str):
        self.client = chromadb.Client()
        self.collection = self.client.get_or_create_collection(collection_name)
    
    def add_documents(self, documents: List[str], ids: List[str]):
        """Add source documents to vector database."""
        self.collection.add(documents=documents, ids=ids)
    
    def retrieve_context(self, query: str, n_results: int = 3) -> str:
        """Retrieve relevant context for query."""
        results = self.collection.query(
            query_texts=[query],
            n_results=n_results
        )
        
        # Combine retrieved documents
        context = "\n\n".join(results['documents'][0])
        return context
    
    def generate_with_context(self, query: str, llm_function) -> str:
        """Generate response using retrieved context."""
        context = self.retrieve_context(query)
        
        prompt = f"""Based on the following information, answer the question.
        If the information doesn't contain the answer, say "I don't have enough information."

        Context:
        {context}

        Question: {query}

        Answer:"""
        
        return llm_function(prompt)

# Usage
rag = RAGSystem("product_knowledge")
rag.add_documents(
    documents=["Product A costs $99", "Product B launched in 2024"],
    ids=["doc1", "doc2"]
)

response = rag.generate_with_context(
    "What is the price of Product A?",
    llm_function=your_llm_api_call
)

Issue 3: Cost Overruns

Symptoms: Monthly LLM API bills exceeding budget, unpredictable costs

Root Causes:

• Inefficient prompts with unnecessary tokens
• No rate limiting or usage controls
• Lack of monitoring and cost attribution

Solutions:

• Implement request throttling and user quotas
• Use token counting before requests to estimate costs
• Cache frequent queries aggressively
• Route simple queries to cheaper models
• Monitor costs per user, feature, and endpoint

Issue 4: Data Privacy and Compliance Issues

Symptoms: Sensitive data potentially exposed, compliance concerns

Root Causes:

• Sending confidential data to third-party APIs
• Insufficient data governance
• Lack of audit trails

Solutions:

• Deploy models on-premises or in private cloud for sensitive data
• Implement PII detection and masking before LLM processing
• Use differential privacy techniques during fine-tuning
• Maintain comprehensive audit logs
• Ensure compliance with GDPR, HIPAA, or industry-specific regulations

Real-World Implementation Example

Let’s build a complete LLMOps pipeline for a customer support chatbot using open-source tools.

# Complete example: Production-ready customer support bot
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Optional
import chromadb
from transformers import pipeline
import time

app = FastAPI()

# Initialize components
class SupportBot:
    def __init__(self):
        # Vector database for RAG
        self.chroma_client = chromadb.Client()
        self.kb_collection = self.chroma_client.create_collection("support_kb")
        
        # LLM (using lightweight model for example)
        self.llm = pipeline("text-generation", model="gpt2")
        
        # Monitoring
        self.monitor = LLMMonitor()
        
    def add_knowledge(self, articles: list):
        """Add support articles to knowledge base."""
        for i, article in enumerate(articles):
            self.kb_collection.add(
                documents=[article],
                ids=[f"article_{i}"]
            )
    
    def answer_query(self, query: str) -> dict:
        """Answer customer query with context from KB."""
        start_time = time.time()
        
        # Retrieve relevant context
        results = self.kb_collection.query(
            query_texts=[query],
            n_results=2
        )
        context = "\n".join(results['documents'][0])
        
        # Generate response (simplified)
        prompt = f"Context: {context}\nQuestion: {query}\nAnswer:"
        response = self.llm(prompt, max_length=150)[0]['generated_text']
        
        # Extract just the answer
        answer = response.split("Answer:")[-1].strip()
        
        # Log metrics
        latency = time.time() - start_time
        self.monitor.log_request(
            prompt=query,
            response=answer,
            latency=latency,
            tokens_used=len(prompt.split()) + len(answer.split()),
            model="gpt2"
        )
        
        return {
            "answer": answer,
            "sources": results['ids'][0],
            "latency_ms": latency * 1000,
            "confidence": 0.85  # Placeholder for real confidence scoring
        }

# Initialize bot
bot = SupportBot()

# Add sample knowledge base
bot.add_knowledge([
    "Our return policy allows returns within 30 days of purchase with original receipt.",
    "Shipping takes 3-5 business days for standard delivery.",
    "Premium support is available 24/7 via email and phone."
])

# API endpoints
class Query(BaseModel):
    question: str
    user_id: Optional[str] = None

@app.post("/chat")
async def chat(query: Query):
    try:
        result = bot.answer_query(query.question)
        return result
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/metrics")
async def get_metrics():
    return bot.monitor.get_summary()

# Run with: uvicorn main:app --reload

Conclusion

LLMOps represents a fundamental shift in how we build and maintain AI systems. While the technology is powerful, production success requires systematic approaches to data management, model deployment, monitoring, and continuous improvement. The key takeaways:

Start Simple: Begin with API-based models and prompt engineering before investing in custom infrastructure.

Measure Everything: Implement comprehensive monitoring from day one—latency, cost, quality, and user satisfaction.

Iterate Continuously: Use production feedback to improve prompts, fine-tune models, and optimize performance.

Design for Failure: LLMs are probabilistic systems; build defensive layers, fallbacks, and human-in-the-loop mechanisms.

Prioritize ROI: Not every problem needs the largest model; match model capability to task complexity for optimal cost-effectiveness.

As LLMs continue to evolve with capabilities like multi-modal understanding, longer context windows, and improved reasoning, LLMOps practices will evolve in parallel. Emerging trends include autonomous agents (AgentOps), specialized vertical models, and increasingly sophisticated evaluation frameworks.

The organizations that master LLMOps today—building systematic, observable, and cost-effective systems—will have a significant competitive advantage as AI becomes central to business operations.

Next Steps

1. Experiment: Build a simple RAG application using LangChain and ChromaDB
2. Monitor: Implement basic metrics tracking for your current LLM applications
3. Learn: Explore LLMOps platforms like Weights & Biases, LangSmith, or Humanloop
4. Connect: Join LLMOps communities and follow case studies from production deployments
5. Optimize: Start measuring and reducing inference costs through caching and model routing

---

References:

1. IBM - What Are Large Language Model Operations (LLMOps)? - https://www.ibm.com/think/topics/llmops - Comprehensive overview of LLMOps fundamentals and enterprise implementation strategies
2. Databricks - LLMOps: Operationalizing Large Language Models - https://www.databricks.com/glossary/llmops - Best practices for LLMOps lifecycle stages and technical requirements
3. AI Accelerator Institute - What is LLMOps? Complete 2025 Industry Guide - https://www.aiacceleratorinstitute.com/your-guide-to-llmops/ - Recent developments and emerging technologies in LLMOps
4. Neptune.ai - LLMOps: What It Is, Why It Matters, and How to Implement It - https://neptune.ai/blog/llmops - Practical implementation guidance with code examples
5. ZenML - LLMOps in Production: Case Studies - https://www.zenml.io/blog/llmops-in-production-287-more-case-studies-of-what-actually-works - Real-world case studies from enterprise LLMOps deployments
6. Tredence - LLMOps Guide: How it Works, Benefits and Best Practices - https://www.tredence.com/llmops - Enterprise maturity model and platform comparison