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Azure’s automated machine learning capabilities

A hugely popular topic in the data science field is Automated Machine Learning. It will be wrong to consider that Auto ML will replace the need for Data Scientist, rather it is just a tool to increase the productivity of data scientists and simplify the entire process. Azure’s Automated machine learning, also referred to as automated ML or Auto ML, is the process of automating the time consuming, iterative tasks of machine learning model development. It builds a set of machine learning models automatically and intelligently selecting models for training then recommending the best one thus accelerating the time it takes to get production-ready ML models with great ease and efficiency.  

As Quoted by Airbnb; “Automated Machine Learning – A Paradigm shift that accelerates data scientist productivity.” 

In our previous blog we discussed that Machine Learning is an application of artificial intelligence which empowers the systems to automatically learn and improve from experience without being programmed. But it still requires a lot of programming. AutoML aims to automate the entire ML workflow. 

This helps data scientists to automate part of their machine learning workflow and thus empowering them to spend more time focusing on other business objectives. It also helps business users who don’t have advanced data science and coding knowledge.  

How Auto ML works? 

While using Azure Machine Learning, you can design and run your automated ML training experiments with these few steps: 

  1. Identify the ML problem 
  2. Choose whether you want to use the Python SDK or the studio web experience 
  3. Specify the source and format of the labeled training data 
  4. Configure the compute target for model training 
  5. Configure the automated machine learning parameters 
  6. Submit the training run
  7. Review the results 

Azure Machine Learning offers two experiences when working with automated ML – Firstly, for code experienced customers, Azure Machine Learning Python SDK and secondly, for limited/no code experience customers, Azure Machine Learning studio at 

Capabilities of Auto ML: 

  • Automated machine learning no-code web interface: Azure introduced the automated machine learning web user interface enabling business domain experts to train models on their data, without writing a single line of code 
  • Time series forecasting: Time series forecasting is an important area of machine learning. Forecasting with automated machine learning improves the accuracy and performance of recommended models with time series data.  
  • Model transparencyNow you can understand all steps in the machine learning pipeline including automated featurization. You can also programmatically understand how your input data got pre-processed and featured, what kind of scaling and normalization was done and the exact machine learning algorithm and hyperparameter values for a chosen machine learning pipeline. 
  • ONNX Models: With ONNX support, users can build ONNX models using automated machine learning and integrate with C# applications, without recoding. With Azure Machine Learning, you can use automated ML to build a Python model and have it converted to the ONNX format. 
  • Enabling .NET developers using Visual Studio/VS CodeThe .NET automated machine learning API enables developers to leverage AutoML capabilities without needing to learn Python. Seamlessly integrate automated machine learning within your existing .NET project by using the API’s NuGet package.
  • Empowering data analysts in PowerBI: This integration allows PowerBI customers to use their data in PowerBI data-flows and leverage the power of automated machine learning capability of Azure Learning service to build models with a no-code experience and then deploy and use the models from PowerBI 
  • Automated machine learning in SQL Server:  With this new capability now you can leverage automated machine learning in Azure Machine Learning service to build, deploy, and use models. 
  • Automated machine learning in Spark: Now effortlessly process massive amounts of data and get all the benefits of a broad, open-source ecosystem with the global scale of Azure to run automated machine learning experiments with HDInsight. Automated machine learning running on Apache Spark in the HDInsight cluster, allows users to use compute capacity across these nodes to be able to run training jobs at scale, as well as running multiple training jobs in parallel. This allows users to run AutoML experiments while sharing the compute with their other big data workloads. 

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