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.
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.
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.