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.
This post is authored by Elad Ziklik, Principal Program Manager, Applied AI.
Today, data isn’t the barrier to innovation, usable data is. Real-world information is messy and carries valuable knowledge in ways that are not readily usable and require extensive time, resources, and data science expertise to process. With Knowledge Mining, it’s our mission to close the gap between data and knowledge.
We’re making it easier to uncover latent insights across all your content with:
Azure Search’s cognitive search capability (general availability) Form Recognizer (preview) Cognitive search and expansion into new scenarios
Announced at Microsoft Build 2018, Azure Search’s cognitive search capability uniquely helps developers apply a set of composable cognitive skills to extract knowledge from a wide range of content. Deep integration of cognitive skills within Azure Search enables the application of facial recognition, key phrase extraction, sentiment analysis, and other skills to content with a single click. This knowledge is organized and stored in a search index, enabling new experiences for exploring the data.
Cognitive search, now generally available, delivers:
Faster performance – Improved throughput capabilities with increased processing speeds up to 30 times faster than in preview. Completing previously hour-long tasks in only a couple of minutes.
This blog post was co-authored by Tina Coll, Senior Product Marketing Manager, Azure Cognitive Services.
Microsoft Build 2019 marks an important milestone for the evolution of Azure Cognitive Services with the introduction of new services and capabilities for developers. Azure empowers developers to make reinforcement learning real for businesses with the launch of Personalizer. Personalizer, along with Anomaly Detector and Content Moderator, is part of the new Decision category of Cognitive Services that provide recommendations to enable informed and efficient decision-making for users.
Available now in preview and general availability (GA):
Cognitive service APIs:
Personalizer – creates personalized user experiences Conversation transcription – transcribes in-person meetings in real-time Form Recognizer – automates data-entry Ink Recognizer – unlocks the potential of digital inked content
Container support for businesses AI models at the edge and closer to the data:
Cognitive Services span the categories of Vision, Speech, Language, Search, and Decision, offering the most comprehensive portfolio in the market for developers who want to embed the ability to see, hear, translate, decide and more into
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
For LaLiga, keeping fans entertained and engaged is a top priority. And when it comes to fans, the Spanish football league has them in droves, with approximately 1.6 billion social media followers around the world. So any time it introduces a new feature, forum, or app for fans, instant global popularity is almost guaranteed. And while this is great news for LaLiga, it also poses technical challenges—nobody wants systems crashing or going unresponsive when millions of people are trying out a fun new app.
When LaLiga chose to develop a personal digital assistant running on Microsoft Azure, its developers took careful steps to ensure optimal performance in the face of huge user volume in multiple languages across a variety of voice platforms. Specifically, the league used Azure to build a conversational AI solution capable of accommodating the quirks of languages and nicknames to deliver a great experience across multiple channels and handle a global volume of millions of users.
Along the way, some valuable lessons emerged for tackling a deployment of this scope and scale.
Accommodating the quirks of languages and nicknames
The LaLiga virtual assistant has launched for Google Assistant and Skype, and it will eventually support 11
Recommendation systems are used in a variety of industries, from retail to news and media. If you’ve ever used a streaming service or ecommerce site that has surfaced recommendations for you based on what you’ve previously watched or purchased, you’ve interacted with a recommendation system. With the availability of large amounts of data, many businesses are turning to recommendation systems as a critical revenue driver. However, finding the right recommender algorithms can be very time consuming for data scientists. This is why Microsoft has provided a GitHub repository with Python best practice examples to facilitate the building and evaluation of recommendation systems using Azure Machine Learning services.
What is a recommendation system?
There are two main types of recommendation systems: collaborative filtering and content-based filtering. Collaborative filtering (commonly used in e-commerce scenarios), identifies interactions between users and the items they rate in order to recommend new items they have not seen before. Content-based filtering (commonly used by streaming services) identifies features about users’ profiles or item descriptions to make recommendations for new content. These approaches can also be combined for a hybrid approach.
Recommender systems keep customers on a businesses’ site longer, they interact with more products/content, and it