A closer look at MLOps and the Intel Extension for Scikit-learn within Azure Machine Learning


Operations (MLOps) is the emerging paradigm that governs the lifecycle of (ML) models and applications in production settings. In recent years, managed machine learning platforms such as Azure Machine Learning have undergone remarkable advancements, dramatically transforming the way businesses handle their ML workflows. Two aspects make Azure Machine Learning indispensable:

  1. It equips data scientists and engineers with powerful MLOps functionalities like model versioning, deployment management, and , thereby streamlining the development process.
  1. AzureML's user-friendly interface and drag-and-drop features enable even newcomers to harness its potential effortlessly.

But as with any technology, ML as a Service (MaaS) platforms come with challenges impacting production efficiency and cost containment, crucial to business ROI and adoption scale. Also, running open-source frameworks can be resource-intensive, causing increased costs, decreased productivity, and fragmentation among ML teams and services. This often compels data scientists to either upscale their cloud instances or tailor their workload to fit hardware cost constraints, both of which could lead to increased developer costs and delayed insights.

Bridging the Gap with Azure-Intel Partnership

To address these issues, Azure Machine Learning provides a comprehensive MLOps platform for building and scaling ML models. The platform eliminates bottlenecks like disjointed ML processes and leverages diverse hardware available on Azure.

The exciting collaboration between Intel and Microsoft brings Intel optimizations to the Azure Machine Learning platform, starting with the Intel® Extension for Scikit-learn*.

The Intel® Extension for Scikit-learn*

The Intel® Extension for Scikit-learn* is a Python* module that provides effortless acceleration for scikit-learn, a widely-used ML library. It enables users to scale applications for Intel® architecture, leading to performance gains and accuracy enhancements.

Harnessing the power of Intel® processors and the Intel® oneAPI Data Analytics Library (oneDAL), this extension accelerates complex ML tasks. The result is faster processing, leading to quicker model training, testing, and deployment, thereby making your ML workflow more efficient.

Integrating Azure and Intel for Enhanced Performance

When you integrate the Intel Extension for Scikit-learn with Azure Machine Learning, you leverage scikit-learn optimizations while utilizing AzureML's services and capabilities. You can continue developing and deploying your models with the added advantage of faster execution made possible by the Intel Extension.

Getting started with the Intel Extension in Azure Machine Learning is a breeze. Once you've created a new AzureML workspace instance, you can access the extension through the available Sklearn container(s) as part of the Microsoft curated environments. With just two lines of code, you can bring the power of Intel's optimization to your ML workload.

# Enable scikit-learn optimizations with these 2 simple lines:
from sklearnex import patch_sklearn

Looking Ahead

Incorporating the Intel Extension for Scikit-learn into Azure Machine Learning can yield significant performance improvements. Its wide range of optimized use cases and effortless implementation make it a potent tool. By adopting , data scientists can maximize their ML models' performance and leverage Intel's hardware and software technologies for faster, better results. Stay tuned for more Intel optimizations on Azure Machine Learning!

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This article was originally published by Microsoft's AI - Machine Learning Blog. You can find the original article here.