How to monitor your Azure infrastructure with Filebeat and Elastic Observability
Overview The ability to access the internal state of your application ecosystem is critical to optimizing your applications and the experience of your users.
Menghua Xiao articles
Accelerate and simplify Scikit-learn model inference with ONNX Runtime
Scikit-learn is one of the most useful libraries for general machine learning in Python. To minimize the cost of deployment and avoid discrepancies, deploying scikit-learn models to production usually leverages Docker containers and pickle, the object serialization module of the Python standard library.
Introducing the Cluster API Provider for Azure (CAPZ) for Kubernetes cluster management
Managing Kubernetes clusters is hard. Managing Kubernetes clusters at scale across a variety of infrastructures is—well—even harder. The Kubernetes community project Cluster API (CAPI) enables users to manage fleets of clusters across multiple infrastructure providers.
ONNX Runtime scenario highlight: Vespa.ai integration
Since its open source debut two years ago, ONNX Runtime has seen strong growth with performance improvements, expanded platform and device compatibility, hardware accelerator support, an extension to training acceleration, and more.
Adding RoBERTa NLP to the ONNX model zoo for natural language predictions
In summer 2019, I worked as a high school intern for the ONNX AI team at Microsoft and loved working on various projects with the team, including the BERT text classification model. However, due to Covid-19, the Microsoft Internship Program for high school students was canceled in the summer of 2020.
Cloudera Data Platform’s integration with Azure delivers enterprise security and governance
Modern analytics and the resulting business insights unlock new opportunities to optimize company performance and open new revenue streams. Since these initiatives also heighten the need for greater security and governance of company data, Identity and Access Management (IAM) needs to be a foundational component of any corporate security plan that covers company data.
.NET for Apache Spark hits v1.0
About two years ago, we heard an increasing demand from the .NET community for an easier way to build big data applications with .NET, outside of needing to learn Scala or Python. Thus, in a collaboration between Azure Data and .NET teams, we started the .NET for Apache® Spark™ open source project.
How to optimize Azure Machine Learning for IoT production usage
Introduction Over the last few years IoT devices, machine learning (ML), and artificial intelligence (AI) have become very popular and now a lot of companies are moving forward to use them in production. All cloud providers, including Microsoft Azure, provide services on how to deploy developed ML algorithms to edge devices.
How to deploy Elastic Cloud on Microsoft Azure
From startups to the global 2000, Elastic powers search solutions for thousands of companies worldwide to find documents, monitor infrastructure, protect against security threats, and more. With Elastic Cloud managed services on Azure, you have the power of Elastic Enterprise Search, Elastic Observability, and Elastic Security.
Announcing Akri, an open source project for building a connected edge with Kubernetes
Overview One of the hallmarks of “the edge” in computing is the array of sensors, controllers, and microcontroller unit (MCU) class devices that produce data and perform actions. For Kubernetes to be a versatile edge computing solution, a cluster needs to easily find these leaf devices.
Introducing ONNX Runtime mobile – a reduced size, high performance package for edge devices
ONNX Runtime is an open source project that is designed to accelerate machine learning across a wide range of frameworks, operating systems, and hardware platforms. Today, we are excited to announce ONNX Runtime release v1.5 as part of our AI at Scale initiative.
Participate in the Java EE cloud migration patterns survey
The Java on Microsoft Azure team has been strengthening its commitment and outreach to Java EE users. This effort includes additional technical guidance, tools, scripts, workshops, and more to better support migrations to Virtual Machines, Kubernetes, OpenShift, and managed service (PaaS) offerings.