Step-by-Step Guide to Deploy ML Models for Inference in Fabric

You can deploy ML models for inference in Microsoft Fabric using a clear, step-by-step method. Many teams face challenges like performing steps manually, dealing with dependency conflicts, and struggling to scale effectively. Errors often occur because environments don’t match or resources are insufficient. Tools like MLflow, Azure ML, and Synapse ML simplify the process of deploying ML models, help you track versions, maintain consistent environments, and share resources. By using these tools, you reduce mistakes and improve the performance of your models.

Key Takeaways

• Make sure you have the right permissions first. Get your data and environment ready before you deploy ML models in Microsoft Fabric. This helps you avoid mistakes and makes setup easy.

• Use MLflow and the Model Registry to help track your models. These tools let you save different versions and manage your models. This keeps your work neat and safe.

• Pick batch inference for big jobs that run in the background. Use real-time inference when you need quick results in apps.

• Set up endpoints in Azure ML with Fabric to help your models grow. Use tools like Synapse ML to score data quickly and close to where it is stored.

• Automate training and always watch your models to keep them working well. Use alerts and retrain your models when you need to.

Prerequisites

Access Setup

You need the right permissions in Microsoft Fabric before starting. You must have an Azure subscription that is not free or trial. Your account should have the Owner, Contributor, or Azure AI Developer role. These roles are needed at the workspace or resource group level. They let you create and manage online endpoints. You can also deploy models on compute resources and handle marketplace subscriptions. For example, you need permissions like /workspaces/services/aks/write or /workspaces/services/aci/write to deploy models. If you want to use AKS resources, you must be Owner or Contributor on the resource group. When you deploy something, the system checks your permissions. If you have the right access, you become the owner and admin of the workspace content.

Tip: If you do not have these permissions, ask your capacity admin to give them to you or add your workspace to a capacity.

Data and Model Requirements

You must get your data and models ready for Fabric. Your data and models need to meet Fabric’s compatibility standards. Microsoft Fabric supports MLflow APIs for tracking experiments and managing models. You can use Synapse ML to build machine learning pipelines that can grow as needed. The runtime works with both Python and R on Apache Spark. You can install custom libraries if you need them. The Spark runtime in Fabric is the same as Synapse Spark, so moving projects is easy. Batch scoring uses the PREDICT function on Spark, just like in Azure Synapse.

• Supported languages: Python, R

• Model tracking: MLflow

• Pipeline building: Synapse ML

• Batch scoring: Spark PREDICT function

Environment Prep

Get your environment ready before you deploy models. First, make a OneLake shortcut in Synapse Data Engineering. This shortcut should point to an Azure Data Lake Gen2 storage account. Next, set up an Azure ML datastore to use the same storage. Upload your datasets into the Fabric lakehouse using the shortcut. In Fabric, make a data pipeline with an Azure Machine Learning activity. Connect Fabric to your Azure ML workspace by giving your subscription ID, resource group, workspace name, and authentication credentials. Pick the batch endpoint and deployment you want to use. Set up the inputs and outputs for your batch endpoint.

You might need to set environment variables for model serving. For example, use ENABLE_FEATURE_TRACING to log feature data frames. Keep sensitive information, like API keys, as secret environment variables. This keeps your deployment safe and your models ready for inference.

Register and Track Models

MLflow Integration

MLflow helps you manage your machine learning work in Microsoft Fabric. First, you make an experiment with mlflow.create_experiment() or pick one with mlflow.set_experiment(). When you train a model, MLflow saves your settings, results, and files. It also saves your model with important details, like the MLmodel file. This file shows where your model is, what libraries you used, and the input and output types. With this, you can use your models again or change them if needed. MLflow lets you look at different training runs to see which model works best. You can keep track of your work and share it with your team.

Note: You can see MLflow experiments and runs in Microsoft Fabric's Monitor. This helps you track, fix, and manage your machine learning projects in one place.

Version Control

Always use version control for your models. In Fabric, you set important names like lakehouse and model registry in your notebooks. This keeps your work neat and easy to use again. When you log model runs with MLflow, you save test results, settings, and files. This lets you see every change you make. The MLflow Model Registry is where all your model versions are kept. Each version has its own details, like when it was made, run name, settings, and tags. You never lose old versions when you update a model. This makes it simple to retrain, test, and move models without getting mixed up.

import mlflow
mlflow.set_experiment("my_experiment")
with mlflow.start_run():
    # Train and log your model here
    mlflow.log_param("max_depth", 5)
    mlflow.log_metric("accuracy", 0.92)
    mlflow.sklearn.log_model(model, "model")

Model Registry

The model registry in Fabric is one place to keep, version, and track your machine learning models. This helps you get the same results by using the right model version. You can see all the details for each model, like how it was built, settings, and how well it works. The registry keeps track of every change, so you know who did what and when. You can also choose who can see or change models, which keeps your work safe. The registry lets data scientists and business analysts work together on the same models and data. This helps everyone avoid mistakes and stay organized.

• Central place for storage and versioning

• All details and history saved

• Control who can work on models

• Works with deployment pipelines for smooth updates

Tip: Use the model registry to check models, manage who can use them, and make sure your team always uses the right version.

Prepare Data

Data Structure

You must set up your input data the right way before using inference in Microsoft Fabric. First, make sure your features are in a vector with a fixed size. Use the Single (float) data type for these features. This is needed for ML.NET and helps your models work well. Put your data in organized formats like Lakehouse or Delta tables. These formats help you handle big datasets and keep things tidy.

If your dataset is very big, split it into smaller files for batch inference. This makes it easier to process many files at once and stops memory problems. Pick the best size for your inference jobs. Use mini-batches for fast jobs or row-by-row for models that use a lot of memory. Always match your columns to what the model needs. When you get your data ready, keep your target variables apart from your features. Change any category fields to numbers and make your data even if you need to.

Tip: Breaking big files into smaller pieces can stop slowdowns and make batch inference work better.

Loading Data

You can bring data into Microsoft Fabric from many places. Use Spark data sources to load files from Unity Catalog tables, Parquet, CSV, JSON, or JDBC. For big datasets, batch loading and prefetching can make things faster. If you use TensorFlow, try the tf.data API for mapping and batching at the same time. For PyTorch, use the DataLoader class to pick batch sizes and worker numbers.

For real-time inference, put JSON data into Eventhouse with Kusto Query Language (KQL). This works for both simple and nested JSON and can handle big data loads. Use Eventstreams to catch and send streaming data, then save it in Eventhouse for quick checks. You can use this data in Power BI reports or Fabric Notebooks to see real-time results.

# Example: Load data from a Delta table into a Spark DataFrame
df = spark.read.format("delta").load("path/to/delta/table")

If you follow these steps, your data will be ready for fast and easy inference in Microsoft Fabric.

Deploy ML Models for Inference

When you put ML models into Microsoft Fabric, you can pick batch inference or real-time inference. Each one is good for different business needs. You can also send your models to Azure ML to make endpoints that can grow. This part will show you both ways and how to use MLflow API and Synapse ML for scoring.

Batch Inference

Batch inference lets you handle lots of data at one time. Use this when you do not need results right away. For example, you might want to score millions of records overnight. Batch endpoints run jobs in the background. You can start a job and check the results later.

Here is a table that shows how batch and real-time inference are different:

Use batch inference if:

• You have big datasets to process.

• Your model or pipeline takes a long time.

• You do not need answers right away.

• Your data is in Azure ML data assets or storage accounts.

• You want to use parallel processing to save time.

To set up batch inference in Fabric:

1. Register your model with MLflow in your notebook.

2. Export the model to Azure ML if you need bigger endpoints.

3. Make a batch endpoint in Azure ML. You can use Azure ML Studio or CLI.

4. Set where your input and output data will go.

5. Start a batch job with the Azure ML SDK or REST API.

You can also use Synapse ML for batch scoring. Synapse ML lets you score data with Spark. You can run predictions near your data using the Spark PREDICT function or Synapse SQL pools.

from synapse.ml.predict import MLflowModel
model = MLflowModel(model_uri="models:/my_model/1")
predictions = model.transform(input_df)

Tip: Batch inference can help you save money because resources can go down to zero when not needed.

Real-Time Inference

Real-time inference gives you answers right away. Use this when your app needs fast results, like chatbots or fraud checks. Real-time endpoints take HTTP requests and send back results in seconds.

Use real-time inference if:

• Your model must answer with low delay.

• Your input data fits in one HTTP request.

• You expect lots of requests every minute.

• Your model is a foundational or fine-tuned foundational model.

Real-time endpoints in Azure ML handle quick, low-delay requests. They can grow to handle more traffic but always keep resources ready for fast replies. You can put ML models as online endpoints and link them to your apps or dashboards.

How fast and how much data you can handle depends on:

• Model type and size

• Prompt and generation size

• Number of requests per minute

• Streaming options

To make things faster, you can:

• Use shorter prompts or make fewer tokens.

• Turn on streaming for quicker first answers.

• Change content filtering settings.

You can set up real-time inference by:

1. Sending your model from Fabric to Azure ML.

2. Making an online endpoint in Azure ML.

3. Putting your model on the endpoint.

4. Sending HTTP requests to the endpoint for answers.

Here is a simple way to call a real-time endpoint:

import requests

url = "https://<your-endpoint>.azurewebsites.net/score"
headers = {"Authorization": "Bearer <token>", "Content-Type": "application/json"}
data = {"input_data": [1.2, 3.4, 5.6]}
response = requests.post(url, headers=headers, json=data)
print(response.json())

Note: Real-time endpoints are best for apps that need answers right away and can keep resources running all the time.

Endpoint Setup

Setting up endpoints lets you use ML models for both batch and real-time inference at a big scale. Microsoft Fabric works with Azure ML, so you can send your trained models and use Azure ML’s managed compute clusters. This setup gives you strong, scalable deployment.

Follow these steps to set up endpoints:

1. Register your model in Fabric with MLflow.

2. Send the model to Azure ML. The MLflow metadata helps make sure it works well.

3. In Azure ML, pick to make a batch or online endpoint.

4. Set up compute resources and scaling.

5. Set up authentication and who can use it.

6. Test your endpoint with sample data.

Synapse ML also helps with deployment and scoring. You can use its API for batch, streaming, and serving. Synapse ML works with many ML frameworks and supports ONNX models. You can run predictions in Spark pools or SQL pools, close to your data.

Tip: Sending models to Azure ML lets you use elastic compute and RESTful APIs. This helps you grow your deployments for both interactive and batch jobs.

When you use ML models in Fabric and Azure ML, you get strong training and scalable inference. This way, you can handle both real-time and batch needs, so your machine learning is ready for any business job.

Operationalize and Monitor

Automation

You can use the Fabric AI Framework to automate machine learning in Microsoft Fabric. This framework puts AI and ML right into your data pipelines. You can build, train, and deploy models without leaving your workspace. The framework has prebuilt ML parts and notebooks you can use again. These tools help you work faster and stop you from writing the same code many times.

AutoML features help you choose the best algorithms and tune your models by themselves. You do not have to guess which settings are best. The framework also works with GitHub for version control. You can keep your notebooks, scripts, and pipeline setups all in one place. Use GitHub Actions or Azure DevOps to automate deployments and retraining. This setup keeps your models fresh and cuts down on manual work.

Tip: Store your data in Fabric’s Lakehouse and OneLake. This makes training and deploying models much easier.

Monitoring

You need to check your models after you deploy them. Watch important metrics like accuracy, latency, and resource use. This helps you find problems early. If your model starts making new predictions, you might have data or output drift. Set up alerts to catch these changes.

Automate retraining if performance drops or new data comes in. This keeps your models working well. Use tools like Azure ML’s InterpretML to explain your model’s choices. This is important if you need to show how your model works or follow rules.

• Watch accuracy, latency, and resource use

• Find data and output drift

• Automate retraining if performance drops

• Use explainability tools for audits

Integration

You can connect Microsoft Fabric to your MLOps pipelines. Fabric supports MLflow for tracking experiments, registering models, and deploying them. Use Synapse Data Science workloads to train models and make features with Spark notebooks. Register your models in the Model Registry to manage the full MLOps lifecycle.

You can use Azure Functions and Power BI connectors to add AI to your workflows. OneLake lets you get your data easily, so you do not need to move files. Spark gives you the power to train complex models fast.

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Now you know how to put ML models into Microsoft Fabric from beginning to end. Using MLOps together helps you try ideas faster, check your work better, and keep track of your models easily. You can use managed tools, pipelines you can use again, and strong safety features. To make your jobs bigger, try using one compute engine, SynapseML, and Fabric AI Functions. For what to do next, watch your models, use batch scoring, and make things better with Fabric Capacity Units.

FAQ

How do you choose between batch and real-time inference in Fabric?

Pick batch inference if you have a lot of data or jobs that run on a schedule. Use real-time inference when you need answers right away, like for chatbots or checking for fraud. Think about how fast you need results and how much data you have before you choose.

What should you do if your model deployment fails in Fabric?

First, make sure you have the right permissions and enough resources. Look at the error logs in Fabric Monitor to find problems. Check if your model format fits what Fabric needs. If there are dependency problems, update your environment or use the right libraries.

Can you update a deployed model without downtime?

Yes, you can add a new version of your model in the Model Registry. Put the new version on your endpoint. Fabric lets you update models while your apps keep working.

How do you monitor model performance after deployment?

You can watch things like accuracy, speed, and resource use in Fabric Monitor. Set alerts to catch changes in data or drops in how well your model works. Use automatic retraining to keep your model working well over time.

What data formats work best for inference in Fabric?

For batch inference, use Delta tables or Parquet files. For real-time inference, send data as JSON. Always make sure your input data matches what your model expects for the best results.