{"id":50162,"date":"2018-09-24T15:52:28","date_gmt":"2018-09-24T15:52:28","guid":{"rendered":"https:\/\/news.microsoft.com\/?p=419223"},"modified":"2018-09-24T15:52:28","modified_gmt":"2018-09-24T15:52:28","slug":"microsoft-unveils-ai-capability-that-automates-ai-development","status":"publish","type":"post","link":"https:\/\/sickgaming.net\/blog\/2018\/09\/24\/microsoft-unveils-ai-capability-that-automates-ai-development\/","title":{"rendered":"Microsoft unveils AI capability that automates AI development"},"content":{"rendered":"

The tedious but necessary process of selecting, testing and tweaking machine learning models that power many of today\u2019s artificial intelligence systems was proving too time-consuming for Nicolo Fusi<\/a><\/span><\/span>.<\/span><\/p>\n

The final straw for the Microsoft researcher and machine learning expert came while fussing over model selection as he and his colleagues built CRISPR.ML<\/a><\/span><\/span>, a computational biology tool that uses AI to help scientists determine the best way to perform gene editing experiments.<\/span><\/p>\n

\u201cIt was just not a good use of time,\u201d said Fusi.<\/span><\/p>\n

So, he set out to develop another AI capability that automatically does the data transformation, model selection and hyperparameter tuning part of AI development \u2013 and inadvertently created a new product.<\/span><\/p>\n

Microsoft announced Monday at the Microsoft Ignite<\/a><\/span><\/span> conference in Orlando, Florida, that the automated machine learning capability is being incorporated\u00a0in the Azure Machine Learning<\/span><\/span><\/a> service. The feature is available in preview.<\/span><\/p>\n

Learning service reimagined<\/strong><\/span><\/h2>\n

Automated machine learning is at the forefront of Microsoft\u2019s push to make Azure Machine Learning an end-to-end solution<\/a> for anyone who wants to build and train models that make predictions from data, and then deploy them anywhere \u2013 in the cloud, on premises or at the edge.<\/span><\/p>\n

Microsoft also announced Monday that the Azure Machine Learning service now includes a software development kit, or SDK, for the Python programming language, which is popular among data scientists. The SDK integrates the Azure Machine Learning service with Python development environments including Visual Studio Code, PyCharm, Azure Databricks notebooks and Jupyter notebooks.<\/span><\/p>\n

\u201cWe heard users wanted to use any tool they wanted, they wanted to use any framework, and so we re-thought about how we should deliver Azure Machine Learning to those users,\u201d said Eric Boyd, corporate vice president, AI Platform, who led the reimagining of the Azure Machine Learning service. \u201cWe have come back with a Python SDK that lights up a number of different features.\u201d<\/span><\/p>\n

These features include distributed deep learning, which enables developers to build and train models faster with massive clusters of graphical processing units, or GPUs, and access to powerful field programmable gate arrays, or FPGAs, for high-speed image classification and recognition scenarios on Azure.<\/span><\/p>\n

\"Nicolo
From left, Microsoft\u2019s Paul Oka, Sharon Gillett, Nicolo Fusi, Evan Green, Gilbert Hendry, Francesco Paolo Casale and Rishit Sheth discuss the algorithm and different ways to choose the next machine learning pipeline. Photo by Dana J. Quigley for Microsoft. <\/em><\/span><\/figcaption><\/figure>\n

Recommender system<\/strong><\/span><\/h2>\n

T<\/strong>he automated model selection and tuning of so-called hyperparameters that govern the performance of machine learning models that are part of automated machine learning will make AI development available to a broader set of Microsoft\u2019s customers, noted Boyd.<\/span><\/p>\n

\u201cThere are a number of teams and companies that we work with that are now just going to make predictions based on the models that automated machine learning comes up with for them,\u201d he said.<\/span><\/p>\n

For machine learning experts, Boyd added that automated machine learning offers advantages as well.<\/span><\/p>\n

\u201cFor trained, specialized data scientists, this is a shortcut. It automates a lot of the tedium in data science,\u201d he said.<\/span><\/p>\n

Automated machine learning homes in on the best so-called machine learning pipelines for a given dataset in a similar way to how on-demand video streaming services recommend movies. New users of a streaming service watch and rate a few movies in exchange for recommendations on what to watch next. The recommendations get better the more the system learns what movies users rate highest.<\/span><\/p>\n

Likewise, automated machine learning runs a few models with hyperparameters tuned various ways on a user\u2019s new dataset to learn how accurate the pipeline\u2019s predictions are. That information informs the next set of recommendations, and so on and so forth for hundreds of iterations.<\/span><\/p>\n

\u201cAt the end, you have a very good pipeline. You don\u2019t have to do anything on top of it. And, the system never needs to see the data, which is attractive to a lot of people these days,\u201d said Fusi, explaining that a user\u2019s dataset remains on their local machine or in a virtual machine in Azure backed by Microsoft\u2019s privacy policy<\/span><\/span><\/a>.<\/span><\/p>\n

\"A
Nicolo Fusi, a Microsoft researcher and machine learning expert, developed the automated machine learning capability\u00a0for his own research purposes. Photo by Dana J. Quigley for Microsoft.<\/em><\/span><\/figcaption><\/figure>\n

From lab to product<\/strong><\/span><\/p>\n

Fusi described the research behind automated machine learning in an academic paper<\/span><\/a><\/span>. The Azure Machine Learning team saw an opportunity to incorporate the technology as a feature in the machine learning service, noted Venky Veeraraghavan, group program manager for the machine learning platform team.<\/span><\/p>\n

Over the process of validating the technology, product testing and benchmarking with customers, the Azure team discovered several novel ways customers could use it.<\/span><\/p>\n

For example, customers who have hundreds or thousands of pieces of equipment in different geographic locations, such as windmills on wind farms, could use automated machine learning to fine tune predictive models for each piece of equipment, which would otherwise prove cost and time prohibitive.<\/span><\/p>\n

In other cases, data scientists are turning to automated machine learning after they\u2019ve already selected and tuned a model as a way to validate their handcrafted solution. \u201cWe have found they often get a better model they hadn\u2019t considered,\u201d Veeraraghavan said.<\/span><\/p>\n

For Fusi, the capability has eliminated the most tedious part of developing AI, freeing him to focus on other aspects such as feature engineering \u2013 the process of extracting useful relationships from data \u2013 and to get some rest.<\/span><\/p>\n

\u201cI can start an automated machine learning run, go home, sleep, and come back to work and see a good model,\u201d he said.<\/span><\/p>\n

Top image: Nicolo Fusi presents a graphic that shows models identified by automated machine learning. Photo by Dana J. Quigley for Microsoft.<\/em><\/span><\/p>\n

Related:<\/strong><\/span><\/h2>\n

John Roach writes about Microsoft research and innovation. Follow him on Twitter<\/a><\/span><\/span>.<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

The tedious but necessary process of selecting, testing and tweaking machine learning models that power many of today\u2019s artificial intelligence systems was proving too time-consuming for Nicolo Fusi. The final straw for the Microsoft researcher and machine learning expert came while fussing over model selection as he and his colleagues built CRISPR.ML, a computational biology […]<\/p>\n","protected":false},"author":2,"featured_media":50163,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[49],"tags":[135,50],"class_list":["post-50162","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-microsoft-news","tag-artificial-intelligence","tag-recent-news"],"_links":{"self":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts\/50162","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/comments?post=50162"}],"version-history":[{"count":0,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts\/50162\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/media\/50163"}],"wp:attachment":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/media?parent=50162"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/categories?post=50162"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/tags?post=50162"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}