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The tedious but necessary process of selecting, testing and tweaking machine learning models that power many of today’s 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 tool that uses AI to help scientists determine the best way to perform gene editing experiments.

“It was just not a good use of time,” said Fusi.

So, he set out to develop another AI capability that automatically does the data transformation, model selection and hyperparameter tuning part of AI development – and inadvertently created a new product.

Microsoft announced Monday at the Microsoft Ignite conference in Orlando, Florida, that the automated machine learning capability is being incorporated in the Azure Machine Learning service. The feature is available in preview.

Learning service reimagined

Automated machine learning is at the forefront of Microsoft’s push to make Azure Machine Learning an end-to-end solution for anyone who wants to build and train models that make predictions from data, and then deploy them anywhere – in the cloud, on premises or at the edge.

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.

“We 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,” said Eric Boyd, corporate vice president, AI Platform, who led the reimagining of the Azure Machine Learning service. “We have come back with a Python SDK that lights up a number of different features.”

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.

Recommender system

The 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’s customers, noted Boyd.

“There 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,” he said.

For machine learning experts, Boyd added that automated machine learning offers advantages as well.

“For trained, specialized data scientists, this is a shortcut. It automates a lot of the tedium in data science,” he said.

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.

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

“At the end, you have a very good pipeline. You don’t 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,” said Fusi, explaining that a user’s dataset remains on their local machine or in a virtual machine in Azure backed by Microsoft’s privacy policy.

From lab to product

Fusi described the research behind automated machine learning in an academic paper. 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.

Over the process of validating the technology, product testing and benchmarking with customers, the Azure team discovered several novel ways customers could use it.

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.

In other cases, data scientists are turning to automated machine learning after they’ve already selected and tuned a model as a way to validate their handcrafted solution. “We have found they often get a better model they hadn’t considered,” Veeraraghavan said.

For Fusi, the capability has eliminated the most tedious part of developing AI, freeing him to focus on other aspects such as feature engineering – the process of extracting useful relationships from data – and to get some rest.

“I can start an automated machine learning run, go home, sleep, and come back to work and see a good model,” he said.

Top image: Nicolo Fusi presents a graphic that shows models identified by automated machine learning. Photo by Dana J. Quigley for Microsoft.

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John Roach writes about Microsoft research and innovation. Follow him on Twitter.

In one part of the solution, they applied a machine teaching framework developed by Bonsai, which was acquired by Microsoft last summer, that allows subject matter experts with little background in data science or AI to tell the system what it wants the intelligent agent to do and what key information it needs to know to do that job successfully.

This Microsoft team works on combining this subject matter expertise with deep reinforcement learning — a branch of AI that enables models to learn from experiences much like a person does, rather than from meticulously labeled data.

The Bonsai platform performs much of the machine learning mechanics in the background — translating instructions into algorithms, creating neural networks and teaching the model the desired behavior. Using this approach, it produced an intelligent agent that, in a proof-of-concept test, learned how to optimally steer the drill using a simplified simulated 2D virtual well environment.

“What excites us about Bonsai is that it gives us a reinforcement learning platform that allows us to scale quickly and takes away the engineering effort involved in stitching together the open-source capabilities so our data scientists can focus on what they’re best at, which is figuring out what the model needs to do,” Jeavons said. “It’s early days still, but we’re extremely excited about the potential.”

Improving employee engagement

But Shell’s digital transformation isn’t just limited to its physical wells, pipelines and plants. It’s also changing the way employees working around the globe communicate with each other.

When Shell’s internal communications team started looking for ways to boost employee engagement and empower everyone across the organization to share information, they settled on a combination of intelligent tools offered as part of Microsoft Office365: Yammer, Stream and SharePoint Online.

Leaders started using Stream, an enterprise video service, to connect with employees more authentically and personally. Now, in addition to leadership communications, employees can easily find or create videos to promote safety, share best practices or analyze a successful project. Stream features like automatic closed captioning and deep search ensure communications are accessible and help employees quickly find the most useful content.

Those videos can be easily posted on SharePoint, a collaboration repository, and Yammer, a corporate social network that allows employees to have conversations with peers across the organization and give leaders insights into what employees are experiencing. More than three-quarters of Shell employees now use Yammer, with an average of 4,000 joining each month. The discussions help unify teams that are dispersed across the globe, solve problems together and foster open communication between groups that had little contact before.

For instance, employees working the night shift on a rig off the cost of Australia might use Yammer to alert the incoming crew to any issues they’ve experienced, and they can now ask if someone working at another location around the world might have a solution.

“These tools allow people to connect with each other, to learn from each other, to see opportunities quicker and build off of each other’s skills,” Sebregts said. “I lead a global organization, and in the past someone doing my type of job might travel around the world and hold a traditional town hall everywhere and once a quarter they would send an email with some thoughts. This is a new era of communication — it’s open, instantaneous, it’s modern, it’s fast, and I love it.”

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Jennifer Langston writes about Microsoft research and innovation. Follow her on Twitter.

In his office in suburban Beijing, Zhang proudly demonstrated the physical part of Airdoc’s system – a small desktop device that looks similar to a scanner a neighborhood optometrist might use for a routine eye exam.

You sit on a stool, lean forward, place your chin on a padded brace, and stare into the darkness of an eyepiece. The algorithm then takes over, precisely adjusting the angle of your head until a green cross comes into focus in the gaze of your right eye. A moment later there’s a bright, but not uncomfortable, flash of white light. The process is repeated for your left eye.

The machine has just taken high-resolution medical-grade images of both your retinas. It instantly sends them to the cloud where it takes 20 to 30 milliseconds (about the same time as an eye blink) of computation to analyze both.

Moments later an impressively detailed diagnostic dashboard is sent to your smartphone. It rates from low to medium to high your susceptibility to a long list of diseases. If there is a problem, it urges you to seek professional medical help.

Right now, it can search for 30 diseases. More machine learning will soon boost that number to 50, and eventually, it could go beyond 200.

Zhang regards his system as a gamechanger because of its potential to deliver at scale and relieve stretched medical resources. To date, it has scanned more than 1.12 million people, mostly in China, but also in the United States, India, Britain, and parts of Africa. “Airdoc users are all over the world.  We hope our deep learning technology can prevent all kinds of disease.”

China, with a population of 1.3 billion, only has about 1,100 eye doctors who are qualified to analyze retinal images. So, the challenge of providing adequate diagnostic services is truly massive – and perhaps no more so than for the epidemic of diabetes.

Authorities estimate as many as 114 million Chinese have diabetes – but only 30 percent of them know that. The other 70 percent are unaware and, without early detection, will eventually be struck down with serious maladies, like blindness, strokes and other potentially fatal conditions.

“Diabetic retinopathy, or DR, is one of the most common and serious complications of diabetes. Once patients feel symptoms, they are already in a severe stage of DR and will go blind without proper treatment,” says Dr. Rui Li Wei (pictured in top image) of Shanghai’s Changzheng Hospital, one of several major medical institutions that now routinely uses Airdoc’s technology as a quick, accurate, and simple diagnostic tool.