Using Tensorflow estimator API to not write Boilerplate codes

What we are doing here : predicting price of taxifare dataset. We are using tensorflow and the high level estimator API . As we know machine learning is a very buzz words now a days and we strongly believe that everyone should needs to know the nuts and and bolts of machine learning so without…

Azure Machine Learning

A workspace is a context for the experiments, data, compute targets, and other assets associated with a machine learning workload. Workspaces for Machine Learning Assets A workspace defines the boundary for a set of related machine learning assets. You can use workspaces to group machine learning assets based on projects, deployment environments (for example, test…

What Is Federated Learning?

Standard machine learning approaches require centralizing the training data on one machine or in a datacenter. And Google has built one of the most secure and robust cloud infrastructures for processing this data to make our services better. Now for models trained from user interaction with mobile devices, we’re introducing an additional approach: Federated Learning. Federated…

Predict prices using regression with ML.NET

You can follow along or download the source code here. Create a console application Create a .NET Core Console Application called “TaxiFarePrediction”. Create a directory named Data in your project to store the data set and model files. Install the Microsoft.ML NuGet Package:In Solution Explorer, right-click the project and select Manage NuGet Packages. Choose “nuget.org” as the Package source, select the Browse tab, search for Microsoft.ML,…

How Azure Machine Learning works: Architecture and concepts

How Azure Machine Learning works: Architecture and concepts Register for Upcoming event and send us a mail: Workflow The machine learning model workflow generally follows this sequence: Train Develop machine learning training scripts in Python or with the visual designer. Create and configure a compute target. Submit the scripts to the configured compute target to run in that environment….

Introduction Into Azure Machine Learning

Azure Machine Learning is a platform for operating machine learning workloads in the cloud. Built on the Microsoft Azure cloud platform, Azure Machine Learning enables you to manage: Scalable on-demand compute for machine learning workloads. Data storage and connectivity to ingest data from a wide range sources. Machine learning workflow orchestration to automate model training,…

Deploy a machine learning model with the designer

Create a real-time inference pipeline To deploy your pipeline, you must first convert the training pipeline into a real-time inference pipeline. This process removes training modules and adds web service inputs and outputs to handle requests. Create a real-time inference pipeline Above the pipeline canvas, select Create inference pipeline > Real-time inference pipeline.Your pipeline should now look like…

Predict automobile price with the designer: A regression problem

Create a new pipeline Azure Machine Learning pipelines organize multiple machine learning and data processing steps into a single resource. Pipelines let you organize, manage, and reuse complex machine learning workflows across projects and users. To create an Azure Machine Learning pipeline, you need an Azure Machine Learning workspace. In this section, you learn how…

MLOps: Model management, deployment and monitoring with Azure Machine Learning

learn how to use Azure Machine Learning to manage the lifecycle of your models. Azure Machine Learning uses a Machine Learning Operations (MLOps) approach. MLOps improves the quality and consistency of your machine learning solutions. Azure Machine Learning provides the following MLOps capabilities: Create reproducible ML pipelines. Pipelines allow you to define repeatable and reusable…