This blog was co-authored by Jordan Edwards, Senior Program Manager, Azure Machine Learning
This year at Microsoft Build 2019, we announced a slew of new releases as part of Azure Machine Learning service which focused on MLOps. These capabilities help you automate and manage the end-to-end machine learning lifecycle.
Historically, Azure Machine Learning service’s management plane has been via its Python SDK. To make our service more accessible to IT and app development customers unfamiliar with Python, we have delivered an extension to the Azure CLI focused on interacting with Azure Machine Learning.
While it’s not a replacement for the Azure Machine Learning service Python SDK, it is a complimentary tool that is optimized to handle highly parameterized tasks which suit themselves well to automation. With this new CLI, you can easily perform a variety of automated tasks against the machine learning workspace, including:
Datastore management Compute target management Experiment submission and job management Model registration and deployment
Combining these commands enables you to train, register their model, package it, and deploy your model as an API. To help you quickly get started with MLOps, we have also released a predefined template in Azure Pipelines. This template allows you
In October 2018 we announced the public preview of Azure Monitor for Virtual Machines (VMs). At that time, we included support for monitoring your virtual machine scale sets from the at scale view under Azure Monitor.
Today we are announcing the public preview of monitoring your Windows and Linux VM scale sets from within the scale set resource blade. This update includes several enhancements:
In-blade monitoring for your scale set with “Top N”, aggregate, and list views across the entire scale set. Drill down experience to identify issues on a particular scale set instance. Updated mapping UI to display the entire dependency diagram across your scale set while supporting drill down maps for a single instance. UI based enablement of monitoring from the scale set resource blade. Updated examples for enabling monitoring using Azure Resource Manager templates. Use of policy to enable monitoring for your scale set. Performance
The performance views are powered using log analytics queries, offering “Top N”, aggregate, and list views to quickly find outliers or issues in your scale set based on guest level metrics for CPU, available memory, bytes sent and received, and logical disk space used.
These views will help you quickly determine if a
Last month, Microsoft released Azure Blockchain Service making it easy for anyone to quickly setup and manage a blockchain network and providing a foundation for developers to build a new class of multi-party blockchain applications in the cloud.
To enable end-to-end development of these new apps, we’ve collaborated with teams from Visual Studio Code to Azure Logic Apps and Microsoft Flow to Azure DevOps, to deliver a high-quality experience that integrates Microsoft tools developers trust and open-source tools they love.
As we looked at the open source projects for Ethereum-based blockchains, we saw Truffle addressing core needs of developers looking to create, compile, test, and manage smart contract code. We kicked off our relationship in 2018 by co-authoring guidance for using Truffle for consortium DevOps and incorporating Truffle-based tooling in our Azure Blockchain Development Kit for Ethereum.
This week, we doubled down on our relationship by announcing an official partnership between our organizations to bring Truffle blockchain tools for developer experience and DevOps to Microsoft Azure. This will manifest not just in Visual Studio and Azure DevOps, but also upcoming tools from Truffle such as Truffle Teams. Through this partnership, developers working in Truffle environments will have access to Azure
Azure Deployment Manager is a new set of features for Azure Resource Manager that greatly expands your deployment capabilities. If you have a complex service that needs to be deployed to several regions, if you’d like greater control over when your resources are deployed in relation to one another, or if you’d like to limit your customer’s exposure to bad updates by catching them while in progress, then Deployment Manager is for you. Deployment Manager allows you to perform staged rollouts of resources, meaning they are deployed region by region in an ordered fashion.
During Microsoft Build 2019, we announced that Deployment Manager now supports integrated health checks. This means that as your rollout proceeds, Deployment Manager will integrate with your existing service health monitor, and if during deployment unacceptable health signals are reported from your service, the deployment will automatically stop and allow you to troubleshoot.
In order to make health integration as easy as possible, we’ve been working with some of the top service health monitoring companies to provide you with a simple copy/paste solution to integrate health checks with your deployments. If you’re not already using a health monitor, these are great solutions to start with:
This blog post was authored by Jordan Edwards, Senior Program Manager, Microsoft Azure.
At Microsoft Build 2019 we announced MLOps capabilities in Azure Machine Learning service. MLOps, also known as DevOps for machine learning, is the practice of collaboration and communication between data scientists and DevOps professionals to help manage the production of the machine learning (ML) lifecycle.
Azure Machine Learning service’s MLOps capabilities provide customers with asset management and orchestration services, enabling effective ML lifecycle management. With this announcement, Azure is reaffirming its commitment to help customers safely bring their machine learning models to production and solve their business’s key problems faster and more accurately than ever before.
Here is a quick look at some of the new features:
Azure Machine Learning Command Line Interface (CLI)
Azure Machine Learning’s management plane has historically been via the Python SDK. With the new Azure Machine Learning CLI, you can easily perform a variety of automated tasks against the ML workspace including:
Compute target management Experiment submission Model registration and deployment Management capabilities
Azure Machine Learning service introduced new capabilities to help manage the code, data, and environments used in your ML lifecycle.
Azure Functions constantly innovates so that you can achieve more with serverless applications, enabling developers to overcome common serverless challenges through a productive, event-driven programming model. Some releases we made in the last few weeks are good examples of this, including:
The Azure Functions premium plan, enables a whole new range of low latency and networking scenarios. The preview of PowerShell support in Azure Functions, provides a way to tackle cloud automation scenarios which is a common challenge to IT pros and SREs all around the globe.
The new releases and improvements do not stop there, and today we are pleased to present several advancements intended to provide a better end-to-end experience when building serverless applications. Keep reading below to learn more about the following:
A new way to host Azure Functions in Kubernetes environments Stateful entities with Durable Functions (in preview) Less cluttered .NET applications with dependency injection Streamlined deployment with Azure DevOps Improved integration with Azure API Management (in preview) Bring Azure Functions to Kubernetes with KEDA
There’s no better way to leverage the serverless advantages than using a fully managed service in the cloud like Azure Functions. But some applications might need to run on disconnected environments,
With the exponential rise of data, we are undergoing a technology transformation, as organizations realize the need for insights driven decisions. Artificial intelligence (AI) and machine learning (ML) technologies can help harness this data to drive real business outcomes across industries. Azure AI and Azure Machine Learning service are leading customers to the world of ubiquitous insights and enabling intelligent applications such as product recommendations in retail, load forecasting in energy production, image processing in healthcare to predictive maintenance in manufacturing and many more.
Microsoft Build 2019 represents a major milestone in the growth and expansion of Azure Machine Learning with new announcements powering the entire machine learning lifecycle.
Boost productivity for developers and data scientists across skill levels with integrated zero-code and code-first authoring experiences as well as automated machine learning advancements for building high-quality models easily. Enterprise-grade capabilities to deploy, manage, and monitor models with MLOps (DevOps for machine learning). Hardware accelerated models for unparalleled scale and cost performance and model interpretability for transparency in model predictions. Open-source capabilities that provide choice and flexibility to customers with MLflow implementation, ONNX runtime support for TensorRT and Intel nGraph, and the new Azure Open Datasets service that delivers curated open