Databricks Model Serving: LLM Inference Made Easy

Deploying machine learning models, especially Large Language Models (LLMs), can be challenging. Data scientists often spend weeks or months building models, but production deployment introduces new hurdles: scaling, latency, monitoring, and integration with existing applications.

Databricks Model Serving removes these barriers by providing real-time and batch inference endpoints for models, including LLMs. This allows teams to serve AI models at scale with minimal engineering overhead, making ML-powered applications more accessible and reliable.

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Why Model Serving Matters

Imagine you’ve built a powerful text summarization LLM. Without a serving solution:

• You’d need to manually integrate the model into APIs.
• Scaling for multiple users would require complex infrastructure.
• Monitoring latency and performance could become overwhelming.

Databricks Model Serving solves this by offering:

• Real-time endpoints for low-latency inference
• Batch endpoints for large-scale processing
• Seamless integration with MLflow and Databricks pipelines
• Scalability and monitoring without complex setup

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How Model Serving Works

1. Train or Register a Model: Use MLflow or Databricks to train your LLM.
2. Deploy as Endpoint: Select the model version and deploy it as a real-time or batch endpoint.
3. Send Inference Requests: Applications can call the endpoint via REST API or Databricks SDK.
4. Monitor Performance: Track request latency, usage, and errors in the Databricks UI.

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Example: Deploying an LLM for Text Summarization

Suppose you trained a model text_summarizer using MLflow.

import mlflow
from mlflow.models import infer_signature
import requests
import json

# Register the model (already trained)
mlflow.sklearn.log_model(sk_model, "text_summarizer", registered_model_name="TextSummarizer")

# Deploy model using Databricks Model Serving UI or API
# Example: Real-time endpoint URL (from UI)
endpoint_url = "https://<databricks-instance>/model/TextSummarizer/1/invocations"

# Send inference request
input_text = {
    "text": "Databricks provides a unified platform for data engineering, AI, and analytics..."
}

response = requests.post(endpoint_url, headers={"Authorization": "Bearer <TOKEN>"},
                         data=json.dumps(input_text))
print(response.json())

Example Output:

{
  "summary": "Databricks unifies data engineering, AI, and analytics in one platform."
}

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Batch Inference Example

# Batch input
batch_texts = [
    "Databricks accelerates ML model deployment.",
    "LLM inference endpoints simplify AI integration."
]

batch_input = {"texts": batch_texts}

batch_response = requests.post(endpoint_url, headers={"Authorization": "Bearer <TOKEN>"},
                               data=json.dumps(batch_input))
print(batch_response.json())

Example Output:

{
  "summaries": [
    "Databricks speeds up ML deployment.",
    "LLM endpoints make AI integration easier."
  ]
}

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Key Benefits of Databricks Model Serving

Feature	Benefit
Real-Time Inference	Low-latency predictions for applications
Batch Inference	Efficient processing of large datasets
MLflow Integration	Seamless deployment from trained models
Scalable & Reliable	Handles multiple concurrent requests
Monitoring & Logging	Track performance, errors, and usage

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Summary

Databricks Model Serving empowers teams to deploy LLMs and other ML models effortlessly, offering both real-time and batch inference. With integrated monitoring, scalability, and MLflow support, it bridges the gap between model development and production deployment. Teams can focus on innovating with AI rather than managing complex serving infrastructure.

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The next topic is “Databricks Assistant — AI Copilot for SQL & ETL”.