Crafting Robust LLM Inference Pipelines

Introduction: From Training to Production-Ready LLMs

Welcome back, future MLOps architect! In our previous chapters, we laid the groundwork for understanding LLMOps and the unique challenges of working with Large Language Models. We’ve seen how crucial it is to manage the lifecycle of these powerful models. Now, it’s time to shift our focus from training these behemoths to serving them efficiently and reliably in a production environment.

Deploying LLMs for inference comes with its own set of fascinating challenges. Unlike traditional machine learning models, LLMs are often massive, requiring significant computational resources (especially GPUs) and memory. They also generate output token by token, which demands careful handling for latency and throughput. This chapter is your guide to building robust, scalable, and cost-efficient LLM inference pipelines. We’ll break down the journey a user’s prompt takes, from initial input to final response, exploring each critical stage and how to optimize it.

By the end of this chapter, you’ll understand the core components of an LLM inference pipeline, delve into advanced GPU optimization techniques, and learn effective scaling strategies to handle varying loads. Get ready to transform raw LLMs into high-performing, user-facing services!

Core Concepts: The Anatomy of an LLM Inference Pipeline

Think of an LLM inference pipeline as a sophisticated assembly line. A user’s request (a prompt) enters one end, goes through several processing steps, interacts with the mighty LLM, and emerges at the other end as a polished response. Each stage is crucial for performance, reliability, and user experience.

An LLM inference pipeline typically consists of three main stages: Pre-processing, Model Serving, and Post-processing. Let’s explore each one.

1. Pre-processing: Preparing the Prompt for the LLM

Before an LLM can work its magic, the raw user input needs to be carefully prepared. This stage is vital for ensuring the model receives input in the correct format, encoding, and structure.

Tokenization: Speaking the LLM’s Language

LLMs don’t understand human language directly. Instead, they operate on numerical representations of “tokens.” A token can be a word, a subword, or even a single character, depending on the tokenizer.

• What it is: The process of converting raw text into a sequence of numerical IDs (tokens) that the LLM can understand.
• Why it’s important: It’s the fundamental translation layer. Incorrect tokenization leads to gibberish input for the model.
• How it works: Tokenizers (like Byte Pair Encoding - BPE, or SentencePiece) are trained alongside the LLM. They have a fixed vocabulary and rules to break down text. For example, the word “unbelievable” might be tokenized into “un”, “believe”, “able”.

Prompt Engineering & Formatting: Guiding the LLM

LLMs are highly sensitive to the way prompts are structured. This goes beyond just tokenization.

• What it is: Structuring the input prompt to elicit the desired behavior from the LLM. This includes adding system messages, few-shot examples, or specific formatting (e.g., XML, JSON).
• Why it’s important: A well-engineered prompt can significantly improve model quality, reduce hallucinations, and ensure consistent output.
• How it works: You might wrap the user’s query with specific tags (<user>, <assistant>), provide context documents for Retrieval-Augmented Generation (RAG), or inject instructions ("Act as a helpful assistant...").

Input Validation: Safety and Structure Checks

Before passing input to an expensive LLM, it’s wise to perform basic checks.

• What it is: Ensuring the input adheres to expected formats, lengths, and safety guidelines.
• Why it’s important: Prevents errors, protects against prompt injection attacks, and avoids wasting GPU cycles on invalid requests.
• How it works: Checking maximum token length, detecting malicious keywords, or verifying JSON structure if the prompt is expected to be JSON.

2. Model Serving: The Heart of the Pipeline

This is where the LLM itself resides and processes the tokenized input to generate a response. This stage is typically the most resource-intensive, heavily relying on GPUs.

Loading and Managing the LLM

• What it is: Bringing the trained LLM (its weights and architecture) into memory, usually on a GPU.
• Why it’s important: The model needs to be ready to process requests quickly. Loading large models can be slow and memory-intensive.
• How it works: Specialized libraries and frameworks handle this, optimizing memory usage and ensuring efficient access to model parameters.

Specialized LLM Inference Runtimes: Unleashing GPU Power

Traditional ML serving frameworks often aren’t optimized for the unique demands of LLMs. This led to the development of highly specialized runtimes.

• What they are: Software libraries and engines specifically designed to accelerate LLM inference on GPUs.
• Why they’re important: They significantly reduce latency, increase throughput, and lower the cost of serving LLMs by intelligently managing GPU resources.
• How they work:
  • Quantization: Reducing the precision of model weights (e.g., from FP16 to INT8 or even INT4) to decrease memory footprint and increase computational speed, often with minimal impact on quality.
  • Continuous Batching (Paging Attention): Dynamically grouping multiple incoming requests into a single batch for GPU processing, even if they arrive at different times or have variable output lengths. This keeps the GPU busy and minimizes idle time.
  • Key-Value (KV) Cache Management: In the attention mechanism, LLMs compute “keys” and “values” for past tokens. The KV cache stores these, avoiding recomputation for subsequent tokens in a sequence, which is critical for efficient sequential generation. Specialized runtimes optimize how this cache is stored and accessed.
  • Example Runtimes (as of 2026-03-20):
    • vLLM: A highly popular, open-source library known for its efficient memory management using PagedAttention, enabling high throughput.
    • NVIDIA TensorRT-LLM: An NVIDIA library that optimizes LLMs for inference on NVIDIA GPUs, offering advanced techniques like quantization, fused kernels, and efficient KV cache management.
    • Text Generation Inference (TGI): Hugging Face’s production-ready inference container for LLMs, supporting continuous batching and other optimizations.

Model Generation: The Core Task

• What it is: The LLM taking the tokenized input and generating new tokens sequentially until a stop condition is met (e.g., maximum length, end-of-sequence token).
• Why it’s important: This is the actual “thinking” process of the LLM.
• How it works: The model predicts the next most probable token based on the input and previously generated tokens. This process repeats, often using sampling strategies (e.g., top-k, top-p) to add creativity.

3. Post-processing: Refining the LLM’s Output

Once the LLM has generated a sequence of tokens, these need to be converted back into a human-readable format and potentially further refined.

Detokenization: From IDs to Human Language

• What it is: The reverse of tokenization, converting the sequence of generated token IDs back into readable text.
• Why it’s important: Presents the LLM’s output in a consumable format for the user or downstream systems.
• How it works: The tokenizer object (used in pre-processing) usually has a decode method for this purpose.

Output Parsing & Validation: Structure and Safety

LLM outputs can sometimes be unpredictable. Post-processing helps ensure consistency and safety.

• What it is: Extracting specific information from the generated text (e.g., parsing JSON, extracting function call arguments) and performing final safety or quality checks.
• Why it’s important: Ensures the output is usable by applications, adheres to expected formats, and meets content guidelines.
• How it works: Regular expressions, JSON parsing libraries, or even another small ML model for content moderation.

Streaming vs. Batching Outputs: User Experience

How the output is delivered impacts perceived latency.

• Streaming: Sending tokens back to the user as they are generated, providing a more interactive and responsive experience.
• Batching: Waiting for the entire response to be generated before sending it back.
• Trade-offs: Streaming improves perceived latency but adds complexity to the client-side. Batching is simpler but can feel slow for long responses.

Scaling Strategies for LLM Inference

As your application grows, you’ll need to serve more requests. How do you ensure your LLM pipeline can keep up?

Horizontal Scaling: Adding More Machines

• What it is: Running multiple identical instances of your LLM inference service across different machines (or pods in Kubernetes).
• Why it’s important: Increases overall throughput and provides fault tolerance. If one instance fails, others can handle requests.
• How it works: Load balancers distribute incoming requests across available instances. Kubernetes’ Deployments and Services are excellent for managing this.

Vertical Scaling: Bigger Machines

• What it is: Increasing the resources (more powerful GPUs, more RAM, faster CPU) of a single machine running your LLM inference service.
• Why it’s important: Can be effective for very large models that require a lot of memory on a single GPU, or for applications with moderate traffic.
• How it works: Upgrading your cloud instance type or physical server hardware.
• Trade-offs: Can hit limits (e.g., single GPU memory), and doesn’t provide fault tolerance as easily as horizontal scaling.

Auto-scaling: Adapting to Demand

• What it is: Automatically adjusting the number of instances (horizontal auto-scaling) or the resources of instances (vertical auto-scaling) based on real-time load metrics.
• Why it’s important: Optimizes cost (you only pay for what you need) and ensures performance during peak demand without over-provisioning.
• How it works:
  • Kubernetes Horizontal Pod Autoscaler (HPA): Scales pods based on CPU utilization, memory usage, or custom metrics (e.g., requests per second).
  • Cloud Provider Auto-scaling Groups (ASG): Automatically adds or removes VMs based on predefined policies.
• Modern Best Practice: Combine horizontal scaling with auto-scaling to achieve both high availability and cost efficiency.

Visualizing the LLM Inference Pipeline

Let’s put it all together with a diagram!

Explanation of the diagram:

• User Request: The starting point, a user’s prompt.
• Pre-processing Layer: Handles all preparation steps, including tokenization and validation.
• LLM Inference Service: The core where the LLM resides on a GPU, leveraging specialized runtimes and optimizations.
• Post-processing Layer: Converts the LLM’s raw output into a usable and safe response.
• Response Delivery: Sends the final response back to the user.
• Scaling & Monitoring: Shows how monitoring feeds into auto-scaling decisions to dynamically adjust the number of inference service instances.

Step-by-Step Implementation: Building a Conceptual LLM Inference API

While deploying a full vLLM or TensorRT-LLM service with Kubernetes is beyond a single chapter’s scope, we can build a conceptual Python API that demonstrates the pipeline stages. This will give you a hands-on feel for how these components interact.

We’ll use FastAPI for our web framework, as it’s modern, fast, and great for building APIs. We’ll also use transformers for tokenization, which is a widely adopted library.

First, ensure you have Python 3.9+ (as of 2026-03-20, Python 3.10, 3.11, 3.12 are stable and recommended) and install the necessary libraries:

pip install fastapi "uvicorn[standard]" transformers

Now, let’s create a file named inference_service.py.

Step 1: Initialize FastAPI and Load Tokenizer

We’ll start by setting up our FastAPI application and loading a tokenizer. We’ll use a tokenizer for a small, common model for demonstration.

# inference_service.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoTokenizer
import time
import os

# --- Configuration ---
# Using a small, fast tokenizer for demonstration.
# For production, you'd use the tokenizer corresponding to your LLM.
TOKENIZER_MODEL_NAME = "gpt2" # Example: A widely available tokenizer
MAX_INPUT_TOKENS = 512
MAX_OUTPUT_TOKENS = 128

# --- FastAPI App Initialization ---
app = FastAPI(
    title="LLM Inference Pipeline Demo",
    description="A conceptual API demonstrating pre-processing, mock inference, and post-processing for LLMs.",
    version="1.0.0"
)

# --- Load Tokenizer ---
# In a real scenario, this would load the tokenizer for your specific LLM.
# It's loaded once at startup to avoid overhead per request.
try:
    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_MODEL_NAME)
    print(f"Tokenizer for '{TOKENIZER_MODEL_NAME}' loaded successfully.")
except Exception as e:
    print(f"Error loading tokenizer: {e}")
    print("Please ensure you have an internet connection or the model is cached locally.")
    exit(1) # Exit if tokenizer can't be loaded, as it's critical.

# --- Pydantic Model for Request Body ---
class InferenceRequest(BaseModel):
    prompt: str
    max_new_tokens: int = MAX_OUTPUT_TOKENS
    temperature: float = 0.7 # For sampling creativity
    # Add other parameters relevant to your LLM here

Explanation:

• We import FastAPI to create our web server and HTTPException for error handling.
• BaseModel from pydantic helps define the structure of our API requests, providing automatic validation.
• AutoTokenizer from transformers is used to load a pre-trained tokenizer. We choose "gpt2" as a common example.
• MAX_INPUT_TOKENS and MAX_OUTPUT_TOKENS are defined for basic validation.
• The tokenizer is loaded once when the application starts. This is a crucial optimization for performance.
• An InferenceRequest class defines what our API expects in the request body: a prompt, max_new_tokens, and temperature.

Step 2: Implement Pre-processing

Now, let’s add the pre-processing logic to our API endpoint.

# inference_service.py (continued)

# ... (previous code for imports, app, tokenizer, InferenceRequest) ...

# --- Mock LLM Inference Function ---
# In a real application, this would be a call to a specialized LLM serving engine
# like vLLM, TensorRT-LLM, or a cloud API.
def mock_llm_inference(token_ids: list[int], max_new_tokens: int) -> list[int]:
    """
    Simulates LLM token generation.
    For demonstration, it just appends some mock tokens.
    """
    print(f"Mock LLM received {len(token_ids)} input tokens. Generating {max_new_tokens} new tokens...")
    # Simulate some processing time
    time.sleep(0.1 + (len(token_ids) / 1000) * 0.05) # Simulate latency based on input size

    # Generate mock output tokens (e.g., just repeat a few tokens)
    mock_output = [token_ids[i % len(token_ids)] if token_ids else 50256 for i in range(max_new_tokens)] # 50256 is <|endoftext|> for gpt2
    
    print(f"Mock LLM generated {len(mock_output)} output tokens.")
    return mock_output

# --- API Endpoint ---
@app.post("/generate")
async def generate_text(request: InferenceRequest):
    start_time = time.time()

    # --- 1. Pre-processing ---
    print("\n--- Pre-processing ---")
    
    # Tokenization
    encoded_input = tokenizer(request.prompt, return_tensors="pt", truncation=True, max_length=MAX_INPUT_TOKENS)
    input_ids = encoded_input["input_ids"][0].tolist()
    
    if len(input_ids) == 0:
        raise HTTPException(status_code=400, detail="Input prompt is empty or tokenized to zero tokens.")
    if len(input_ids) > MAX_INPUT_TOKENS:
        # This should ideally be caught by truncation=True, but good to double check
        raise HTTPException(status_code=400, detail=f"Input prompt too long. Max {MAX_INPUT_TOKENS} tokens allowed.")

    print(f"Original prompt: '{request.prompt}'")
    print(f"Tokenized input IDs (first 10): {input_ids[:10]}...")
    print(f"Number of input tokens: {len(input_ids)}")

Explanation:

• mock_llm_inference function: This is a placeholder. In a real system, this function would make an API call to a dedicated LLM inference server (like a vLLM container, a TensorRT-LLM endpoint, or a cloud LLM service). We simulate some latency.
• @app.post("/generate"): Defines an API endpoint that accepts POST requests at /generate.
• async def generate_text(request: InferenceRequest):: The asynchronous function to handle requests. FastAPI recommends async for I/O-bound operations.
• Tokenization:
  • tokenizer(request.prompt, return_tensors="pt", truncation=True, max_length=MAX_INPUT_TOKENS): This line tokenizes the input prompt.
    • return_tensors="pt": Returns PyTorch tensors (though we convert to list).
    • truncation=True: Automatically truncates the input if it exceeds max_length.
    • max_length=MAX_INPUT_TOKENS: Sets the maximum number of tokens.
  • input_ids = encoded_input["input_ids"][0].tolist(): Extracts the token IDs as a Python list.
• Input Validation: We check if the tokenized input is empty or too long, raising an HTTPException if there’s an issue.

Step 3: Integrate Mock LLM Inference and Post-processing

Now, let’s connect our mock LLM and add the post-processing steps.

# inference_service.py (continued)

# ... (previous code for imports, app, tokenizer, InferenceRequest, mock_llm_inference) ...
# ... (previous code for @app.post("/generate") and pre-processing) ...

@app.post("/generate")
async def generate_text(request: InferenceRequest):
    start_time = time.time()

    # --- 1. Pre-processing ---
    print("\n--- Pre-processing ---")
    
    encoded_input = tokenizer(request.prompt, return_tensors="pt", truncation=True, max_length=MAX_INPUT_TOKENS)
    input_ids = encoded_input["input_ids"][0].tolist()
    
    if len(input_ids) == 0:
        raise HTTPException(status_code=400, detail="Input prompt is empty or tokenized to zero tokens.")
    if len(input_ids) > MAX_INPUT_TOKENS:
        raise HTTPException(status_code=400, detail=f"Input prompt too long. Max {MAX_INPUT_TOKENS} tokens allowed.")

    print(f"Original prompt: '{request.prompt}'")
    print(f"Tokenized input IDs (first 10): {input_ids[:10]}...")
    print(f"Number of input tokens: {len(input_ids)}")

    # --- 2. Mock LLM Inference ---
    print("\n--- Model Serving (Mock) ---")
    generated_token_ids = mock_llm_inference(input_ids, request.max_new_tokens)
    print(f"Generated token IDs (first 10): {generated_token_ids[:10]}...")
    print(f"Number of generated tokens: {len(generated_token_ids)}")

    # --- 3. Post-processing ---
    print("\n--- Post-processing ---")
    
    # Detokenization
    full_output_text = tokenizer.decode(generated_token_ids, skip_special_tokens=True)
    print(f"Detokenized output: '{full_output_text}'")

    # Basic Output Parsing/Validation (Example: Check for sensitive keywords)
    if "badword" in full_output_text.lower():
        # In a real system, you might replace, censor, or reject the output
        print("Warning: Detected a potentially sensitive keyword in the output!")
        full_output_text = full_output_text.replace("badword", "[REDACTED]")

    end_time = time.time()
    latency = (end_time - start_time) * 1000 # in milliseconds

    print(f"Total request latency: {latency:.2f} ms")

    return {
        "status": "success",
        "generated_text": full_output_text,
        "input_tokens": len(input_ids),
        "output_tokens": len(generated_token_ids),
        "latency_ms": f"{latency:.2f}"
    }

# --- How to run this service ---
# Save the file as inference_service.py
# Run from your terminal: uvicorn inference_service:app --host 0.0.0.0 --port 8000
# Then access the API at http://localhost:8000/docs for interactive testing.

Explanation:

• Mock LLM Inference: We call our mock_llm_inference function, passing the input_ids and the requested max_new_tokens.
• Detokenization:
  • tokenizer.decode(generated_token_ids, skip_special_tokens=True): Converts the list of generated token IDs back into a human-readable string. skip_special_tokens=True ensures that tokens like [CLS], [SEP], or EOS are not included in the final output.
• Basic Output Validation: A simple example checking for a “badword.” In a real-world scenario, this could involve more sophisticated content moderation, PII detection, or structured output validation.
• Latency Calculation: We track the total time taken for the request, a crucial metric for monitoring.
• Return Value: The API returns a JSON object containing the generated text, token counts, and latency.

Step 4: Run the Service

To run this conceptual inference service:

1. Save the code as inference_service.py.
2. Open your terminal in the same directory.
3. Execute the command:

   uvicorn inference_service:app --host 0.0.0.0 --port 8000 --reload
   

   The --reload flag is handy for development, as it restarts the server automatically when you make code changes.

Now, open your web browser and navigate to http://localhost:8000/docs. You’ll see the interactive API documentation provided by FastAPI (Swagger UI).

• Click on the /generate endpoint.
• Click “Try it out.”
• In the “Request body” field, enter a prompt, for example:

  {
    "prompt": "Explain the concept of LLMOps in simple terms.",
    "max_new_tokens": 50,
    "temperature": 0.7
  }
  

• Click “Execute.”

You’ll see the request being processed in your terminal, demonstrating the pre-processing, mock inference, and post-processing steps, along with the generated (mock) text and latency.

This conceptual service provides a clear blueprint for how you’d structure an LLM inference API, even when the actual LLM serving engine is a separate, highly optimized component.

Mini-Challenge: Enhance Output Parsing

Now it’s your turn to get hands-on!

Challenge: Modify the post-processing section of inference_service.py to add a more structured output parsing step. Imagine your LLM is sometimes instructed to return JSON. Implement a basic check: if the generated text looks like JSON (starts with { and ends with }), attempt to parse it and include a parsed_json field in the API response. If it fails, just return None or an empty object for parsed_json.

Hint:

• You’ll need Python’s built-in json module.
• Use a try-except block for parsing, as LLMs can sometimes generate malformed JSON.
• Remember to add import json at the top of your file.

What to observe/learn:

• The importance of robust output handling, especially when LLMs are prompted for structured formats.
• How to integrate external libraries for post-processing.
• The graceful handling of potential errors in LLM outputs.

    parsed_json_output = None
    if full_output_text.strip().startswith("{") and full_output_text.strip().endswith("}"):
        try:
            parsed_json_output = json.loads(full_output_text)
            print("Successfully parsed JSON output.")
        except json.JSONDecodeError as e:
            print(f"Warning: Failed to parse generated text as JSON: {e}")
# ... then include parsed_json_output in your return dictionary

Common Pitfalls & Troubleshooting

Even with the best intentions, deploying LLM inference pipelines can hit snags. Here are some common pitfalls and how to approach them:

1. Underestimating GPU Resource Requirements and Costs:

   • Pitfall: LLMs are massive. A 7B parameter model might need 14GB of VRAM (for FP16), and larger models need significantly more. Running out of VRAM causes crashes or very slow CPU offloading. GPU instances are expensive.
   • Troubleshooting:
     • Monitor VRAM: Use nvidia-smi (Linux) or cloud provider metrics to track GPU memory usage.
     • Quantization: Experiment with lower precision (INT8, INT4) to reduce VRAM footprint. This is often the first and most impactful optimization.
     • Model Selection: Choose smaller, more efficient LLMs if they meet your performance requirements.
     • Batch Size: Balance batch size with latency requirements. Larger batches utilize GPUs better but can increase per-request latency.
     • Cost Monitoring: Set up detailed cost alerts and dashboards with your cloud provider.

2. Inefficient Batching or Lack of Specialized Runtimes:

   • Pitfall: If you process each request individually (batch size 1) or use a generic serving framework, your GPU will often sit idle between token generations, leading to abysmal throughput and high costs.
   • Troubleshooting:
     • Embrace Continuous Batching: This is the game-changer for LLM throughput. Use specialized runtimes like vLLM, TensorRT-LLM, or TGI which implement PagedAttention and similar techniques.
     • Benchmark: Measure throughput (tokens/sec or requests/sec) under various load conditions to identify bottlenecks.
     • Load Testing: Simulate concurrent users to stress-test your pipeline and observe how batching behaves.

3. Poor Version Control and Reproducibility:

   • Pitfall: Without strict versioning for models, tokenizers, inference code, and configurations, it becomes impossible to reproduce results, roll back to previous versions, or debug issues effectively.
   • Troubleshooting:
     • Model Registry: Use an MLOps platform’s model registry (e.g., MLflow Model Registry, Azure Machine Learning, SageMaker Model Registry) to version and track LLM artifacts.
     • Containerization (Docker): Package your inference service (including code, dependencies, and model paths) into Docker images. Version these images.
     • Infrastructure as Code (IaC): Manage your deployment configurations (Kubernetes manifests, cloud templates) using tools like Terraform or Pulumi and store them in Git.
     • GitOps: Automate deployments based on Git repositories for version-controlled infrastructure and applications.

Summary: Building the Backbone of LLM Applications

Phew! We’ve covered a lot of ground in crafting robust LLM inference pipelines. This chapter has equipped you with a deep understanding of the journey a prompt takes and the critical considerations for production deployment.

Here are the key takeaways:

• LLM Inference Pipelines consist of distinct Pre-processing, Model Serving, and Post-processing stages, each crucial for performance and reliability.
• Pre-processing involves tokenization, prompt formatting, and input validation to prepare the user’s request.
• Model Serving is the core, where the LLM generates tokens. It heavily relies on specialized inference runtimes (like vLLM, TensorRT-LLM, TGI) and GPU optimization techniques such as quantization, continuous batching, and efficient KV cache management.
• Post-processing converts the generated tokens back into human-readable text, performs output parsing, and ensures safety.
• Scaling strategies like horizontal, vertical, and especially auto-scaling are essential for handling varying user loads efficiently and cost-effectively.
• Common pitfalls include underestimating GPU costs, inefficient batching, and lack of proper version control, all of which can be mitigated with modern LLMOps practices.

You now have a solid understanding of how to build the backbone of any LLM-powered application. But what happens when you have multiple models, or you want to test new versions without impacting all users? That’s where dynamic model routing and intelligent caching come into play!

In our next chapter, we’ll dive into Dynamic Model Routing and Advanced Caching Strategies, learning how to make your LLM services even more flexible, performant, and cost-efficient. Get ready to add another layer of sophistication to your LLMOps toolkit!

References

• Hugging Face Transformers Library: https://huggingface.co/docs/transformers/index
• FastAPI Official Documentation: https://fastapi.tiangolo.com/
• vLLM GitHub Repository: https://github.com/vllm-project/vllm
• NVIDIA TensorRT-LLM GitHub Repository: https://github.com/NVIDIA/TensorRT-LLM/blob/main/README.md
• Kubernetes Horizontal Pod Autoscaler: https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
• LLMOps workflows on Azure Databricks: https://learn.microsoft.com/en-us/azure/databricks/machine-learning/mlops/llmops

This page is AI-assisted and reviewed. It references official documentation and recognized resources where relevant.