Care and Feeding of Predictive Maintenance Solutions

This post is authored by John Ehrlinger, Data Scientist at Microsoft. Microsoft has recently launched Azure Machine Learning services (AML) to public preview. The updated services include a Workbench application plus command-line tools to assist in developing and managing machine learning solutions through the entire data science life cycle. An Experimentation Service handles the execution… Read more

Custom Vision Service: Code-Free Automated Machine Learning for Image Classification

Re-posted from the Microsoft Azure blog. Artificial Intelligence (AI) is a hugely disruptive force, one that is powering much of the digital transformation businesses are going through in recent times. At Microsoft, our mission is to bring AI to every developer and every organization on the planet, and to help businesses augment human ingenuity in… Read more

Deep Learning & Computer Vision in the Microsoft Azure Cloud

This is the first in a multi-part series by guest blogger Adrian Rosebrock. Adrian writes at PyImageSearch.com about computer vision and deep learning using Python, and he recently finished authoring a new book on deep learning for computer vision and image recognition. Introduction I had two goals when I set out to write my new… Read more

How to Run Large-Scale Educational Workshops in Deep Learning & Data Science

This post is authored by Gopi Kumar, Principal Program Manager, and Paul Shealy, Senior Software Engineer at Microsoft. With the rise of Artificial Intelligence, the need to rapidly train a large number of data scientists and AI developers has never been more urgent. Microsoft is always looking for efficient ways to educate employees and customers… Read more

Hello 2018 | Recap of “Top 10” Posts of 2017

As we ring in the new year, we’d like to kick things off in our usual fashion – with a quick recap of our most popular posts from the year just concluded. So here are our “Top 10” posts from 2017, sorted in increasing order of readership – enjoy! 10. Quick-Start Guide to the Data Science… Read more

ICYMI: Recent Microsoft AI Platform Updates, Including in ONNX, Deep Learning, Video Indexer & More

Four recent Microsoft posts about AI developments, just in case you missed it. 1. Getting Started with Microsoft AI – MSDN Article This MSDN article, co-authored by Joseph Sirosh and Wee Hyong Tok, provides a nice summary of all the capabilities offered by the Microsoft AI platform and how you can get started today. From… Read more

How We Share the Latest AI & ML Developments Within Microsoft

We recently concluded the Fall 2017 Machine Learning, AI & Data Science (MLADS) conference, Microsoft’s largest internal gathering of employees focused specifically on these areas. This latest edition was the eighth in a popular series that we launched back in 2014. Over 3,500 employees tuned into the sold-out conference, both in person in Redmond and… Read more

Build Great Conversational Bots Using Azure Bot Service & LUIS (Both Services Now Generally Available)

Re-posted from the Microsoft Azure blog. Conversational AI, or making human and computer interactions more natural, has been a goal of computer scientists for a long time. In support of that longstanding quest, we are excited to announce the general availability of two key Microsoft Azure services that streamline the creation of interactive conversational bots,… Read more

Calling All AI Innovators – Join the ‘Cloud AI Challenge’ for a Chance to Win $25,000

This post is authored by Vani Mandava, Director of Data Science at Microsoft Research. The AI revolution is poised to unleash unprecedented innovation and impact on our society. Several research and development groups across Microsoft have hit their stride in delivering world-changing impact through the power of AI. Working together, we are creating a comprehensive… Read more

Music Generation with Azure Machine Learning

This post is authored by Erika Menezes, Software Engineer at Microsoft. Using deep learning to learn feature representations from near-raw input has been shown to outperform traditional task-specific feature engineering in multiple domains in several situations, including in object recognition, speech recognition and text classification. With the recent advancements in neural networks, deep learning has… Read more