AI News, What’s new in Azure Machine Learning service

What’s new in Azure Machine Learning service

We are confident that they will dramatically boost productivity for data scientists and machine learning practitioners in building and deploying machine learning solutions at cloud scale.

Here is a cool example of transforming a column into 2 new columns by supplying a few examples: For more details, please visit this article.

You can also query the experiment runs to find the one that recorded the best metric as defined by you such as highest accuracy, lowest mean squared error, and more.

It is OK to tinker with small data sets locally on a laptop, but training a sophisticated model might require large-scale compute resources in the cloud.

It can automatically scale up to 4 nodes when jobs are submitted to it, or scale back down to 0 nodes when jobs are finished to save cost.

It is perfect for running many jobs in parallel, supporting features like intelligent hyperparameter tuning or batch scoring, or training large deep learning model in a distributed fashion.

Azure Machine Learning service supports executing your scripts in various compute targets, including on local computer, aforementioned remote VM, Spark cluster, or managed computer cluster.

You can ask the system to simply run your script in a Python environment you have already configured in the compute target, or ask the system to build a new conda environment based on dependencies specified to run your job.

Here is an example of a run configuration object that specifies a Docker image with system managed conda environment: The SDK also includes a high-level estimator pattern to wrap some of these configurations for TensorFlow and PyTorch-based execution to make it even easier to define the environments.

With the multitude of compute targets and execution environments support, it is critical to have a consistent way to access data files from your scripts.

Azure Machine Learning service includes advanced capabilities to automatically recommend machine learning pipeline that is composed of the best featurization steps and best algorithm, and the best hyperparameters, based on the target metric you set.

Here is an example of automatically finding the pipeline that produces the highest AUC_Weighted value for classifying the given training data set: Here is a printout of the run.

automated machine learning supports both classification and regression, and it includes features such as handling missing values, early termination by a stopping metric, blacklisting algorithms you don’t want to explore, and many more.

The Azure Machine Learning service delivers intelligent hyperparameter tuning capabilities by automatically scheduling parameter search jobs on the managed compute clusters, and through user-defined early termination policies to accelerate the process of finding the best parameter combination.

The above code will randomly search through the given parameter space, and launch up to 200 jobs on the computer target configured on the estimator object, and look for the one that returns the highest accuracy.

Whether you are using the native parameter server option that ships with TensorFlow, or an MPI-based approach leveraged by Hovorod framework combined with TensorFlow, or Horovod with PyTorch, or CNTK with MPI, you can train the network in parallel with ease.

Here is a simple example showing a sequential pipeline for data preparation, training and batch scoring: Using a pipeline can drastically reduce complexity by organizing multi-step workflows, improve manageability by tracking the entire workflow as a single experiment run, and increase usability by recording all intermediary tasks and data.

Simply supply the model file from your local computer or using a registered model in your workspace, add a training script and a package dependencies file.

You can choose to deploy the image to Azure Container Instance (ACI) service, a computing fabric to run a Docker container for dev/test scenarios, or deploy to Azure Kubernetes Cluster (AKS) service for scale-out and secure production environment.

With this extension, you can access all the cool features from experiment run submission to run tracking, from compute target provisioning to model management and deployment, all within a friendly user interface.

Microsoft Azure Cloud - Machine Learning - Train, Score & Evaluate - DIY-13-of-20

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Microsoft Azure Cloud - Machine Learning - Deploying Web Service - DIY-14-of-20

Bharati DW Consultancy cell: +1-562-646-6746 email: bharati.dwconsultancy@gmail.com website: ..

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