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AWS expands on SageMaker capabilities with end-to-end features for machine learning
Nearly three years after it was first launched, Amazon Web Services’ SageMaker platform has gotten a significant upgrade in the form of new features, making it easier for developers to automate and scale each step of the process to build new automation and machine learning capabilities, the company said.
As machine learning moves into the mainstream, business units across organizations will find applications for automation, and AWS is trying to make the development of those bespoke applications easier for its customers.
“One of the best parts of having such a widely adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables,” said AWS vice president of machine learning, Swami Sivasubramanian. “Today, we are announcing a set of tools for Amazon SageMaker that makes it much easier for developers to build end-to-end machine learning pipelines to prepare, build, train, explain, inspect, monitor, debug and run custom machine learning models with greater visibility, explainability and automation at scale.”
Already companies like 3M, ADP, AstraZeneca, Avis, Bayer, Capital One, Cerner, Domino’s Pizza, Fidelity Investments, Lenovo, Lyft, T-Mobile and Thomson Reuters are using SageMaker tools in their own operations, according to AWS.
The company’s new products include Amazon SageMaker Data Wrangler, which the company said was providing a way to normalize data from disparate sources so the data is consistently easy to use. Data Wrangler can also ease the process of grouping disparate data sources into features to highlight certain types of data. The Data Wrangler tool contains more than 300 built-in data transformers that can help customers normalize, transform and combine features without having to write any code.
Amazon also unveiled the Feature Store, which allows customers to create repositories that make it easier to store, update, retrieve and share machine learning features for training and inference.
Another new tool that Amazon Web Services touted was Pipelines, its workflow management and automation toolkit. The Pipelines tech is designed to provide orchestration and automation features not dissimilar from traditional programming. Using pipelines, developers can define each step of an end-to-end machine learning workflow, the company said in a statement. Developers can use the tools to re-run an end-to-end workflow from SageMaker Studio using the same settings to get the same model every time, or they can re-run the workflow with new data to update their models.
To address the longstanding issues with data bias in artificial intelligence and machine learning models, Amazon launched SageMaker Clarify. First announced today, this tool allegedly provides bias detection across the machine learning workflow, so developers can build with an eye toward better transparency on how models were set up. There are open-source tools that can do these tests, Amazon acknowledged, but the tools are manual and require a lot of lifting from developers, according to the company.
Other products designed to simplify the machine learning application development process include SageMaker Debugger, which enables developers to train models faster by monitoring system resource utilization and alerting developers to potential bottlenecks; Distributed Training, which makes it possible to train large, complex, deep learning models faster than current approaches by automatically splitting data across multiple GPUs to accelerate training times; and SageMaker Edge Manager, a machine learning model management tool for edge devices, which allows developers to optimize, secure, monitor and manage models deployed on fleets of edge devices.
Last but not least, Amazon unveiled SageMaker JumpStart, which provides developers with a searchable interface to find algorithms and sample notebooks so they can get started on their machine learning journey. The company said it would give developers new to machine learning the option to select several pre-built machine learning solutions and deploy them into SageMaker environments.
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As it closes in on Arm, Nvidia announces UK supercomputer dedicated to medical research
As Nvidia continues to work through its deal to acquire Arm from SoftBank for $40 billion, the computing giant is making another big move to lay out its commitment to investing in U.K. technology. Today the company announced plans to develop Cambridge-1, a new £40 million AI supercomputer that will be used for research in the health industry in the country, the first supercomputer built by Nvidia specifically for external research access, it said.
Nvidia said it is already working with GSK, AstraZeneca, London hospitals Guy’s and St Thomas’ NHS Foundation Trust, King’s College London and Oxford Nanopore to use the Cambridge-1. The supercomputer is due to come online by the end of the year and will be the company’s second supercomputer in the country. The first is already in development at the company’s AI Center of Excellence in Cambridge, and the plan is to add more supercomputers over time.
The growing role of AI has underscored an interesting crossroads in medical research. On one hand, leading researchers all acknowledge the role it will be playing in their work. On the other, none of them (nor their institutions) have the resources to meet that demand on their own. That’s driving them all to get involved much more deeply with big tech companies like Google, Microsoft and, in this case, Nvidia, to carry out work.
Alongside the supercomputer news, Nvidia is making a second announcement in the area of healthcare in the U.K.: it has inked a partnership with GSK, which has established an AI hub in London, to build AI-based computational processes that will be used in drug vaccine and discovery — an especially timely piece of news, given that we are in a global health pandemic and all drug makers and researchers are on the hunt to understand more about, and build vaccines for, COVID-19.
The news is coinciding with Nvidia’s industry event, the GPU Technology Conference.
“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Jensen Huang, founder and CEO of Nvidia, in his keynote at the event. “The Cambridge-1 supercomputer will serve as a hub of innovation for the U.K., and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”
The company plans to dedicate Cambridge-1 resources in four areas, it said: industry research, in particular joint research on projects that exceed the resources of any single institution; university granted compute time; health-focused AI startups; and education for future AI practitioners. It’s already building specific applications in areas, like the drug discovery work it’s doing with GSK, that will be run on the machine.
The Cambridge-1 will be built on Nvidia’s DGX SuperPOD system, which can process 400 petaflops of AI performance and 8 petaflops of Linpack performance. Nvidia said this will rank it as the 29th fastest supercomputer in the world.
“Number 29” doesn’t sound very groundbreaking, but there are other reasons why the announcement is significant.
For starters, it underscores how the supercomputing market — while still not a mass-market enterprise — is increasingly developing more focus around specific areas of research and industries. In this case, it underscores how health research has become more complex, and how applications of artificial intelligence have both spurred that complexity but, in the case of building stronger computing power, also provides a better route — some might say one of the only viable routes in the most complex of cases — to medical breakthroughs and discoveries.
It’s also notable that the effort is being forged in the U.K. Nvidia’s deal to buy Arm has seen some resistance in the market — with one group leading a campaign to stop the sale and take Arm independent — but this latest announcement underscores that the company is already involved pretty deeply in the U.K. market, bolstering Nvidia’s case to double down even further. (Yes, chip reference designs and building supercomputers are different enterprises, but the argument for Nvidia is one of commitment and presence.)
“AI and machine learning are like a new microscope that will help scientists to see things that they couldn’t see otherwise,” said Dr. Hal Barron, chief scientific officer and president, R&D, GSK, in a statement. “NVIDIA’s investment in computing, combined with the power of deep learning, will enable solutions to some of the life sciences industry’s greatest challenges and help us continue to deliver transformational medicines and vaccines to patients. Together with GSK’s new AI lab in London, I am delighted that these advanced technologies will now be available to help the U.K.’s outstanding scientists.”
“The use of big data, supercomputing and artificial intelligence have the potential to transform research and development; from target identification through clinical research and all the way to the launch of new medicines,” added James Weatherall, PhD, head of Data Science and AI, AstraZeneca, in his statement.
“Recent advances in AI have seen increasingly powerful models being used for complex tasks such as image recognition and natural language understanding,” said Sebastien Ourselin, head, School of Biomedical Engineering & Imaging Sciences at King’s College London. “These models have achieved previously unimaginable performance by using an unprecedented scale of computational power, amassing millions of GPU hours per model. Through this partnership, for the first time, such a scale of computational power will be available to healthcare research – it will be truly transformational for patient health and treatment pathways.”
Dr. Ian Abbs, chief executive & chief medical director of Guy’s and St Thomas’ NHS Foundation Trust Officer, said: “If AI is to be deployed at scale for patient care, then accuracy, robustness and safety are of paramount importance. We need to ensure AI researchers have access to the largest and most comprehensive datasets that the NHS has to offer, our clinical expertise, and the required computational infrastructure to make sense of the data. This approach is not only necessary, but also the only ethical way to deliver AI in healthcare – more advanced AI means better care for our patients.”
“Compact AI has enabled real-time sequencing in the palm of your hand, and AI supercomputers are enabling new scientific discoveries in large-scale genomic data sets,” added Gordon Sanghera, CEO, Oxford Nanopore Technologies. “These complementary innovations in data analysis support a wealth of impactful science in the U.K., and critically, support our goal of bringing genomic analysis to anyone, anywhere.”
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Lack of transparency in healthcare startups risks another Theranos implosion
Are more Theranos -style scandals looming for investors in healthcare startups?
A team of researchers associated with the Meta-Research Innovation Center at Stanford thinks so. They’ve published a paper warning investors in life sciences startups that a systemic lack of transparency exists in their portfolio companies — creating the possibility for more multi-billion-dollar implosions and scandals like the one that toppled Theranos and its charismatic founder, Elizabeth Holmes.
Indeed, one of the study’s authors, Dr. John Ioannidis, the co-director of the Meta-Research Innovation Center at Stanford and director of the University’s PhD program in Epidemiology and Clinical Research, was among the first people to identify the risks associated with Theranos and its “stealth research.”
Now Dr. Ioannidis and his co-authors, Ioana A. Cristea and Eli M. Cahan, have published a study surveying the publicly available research from the largest privately held companies in the healthcare space, and found them lacking.
Most of the highest-valued startups in healthcare have not published any significant scientific literature, the study found. Nearly half of the publications from companies worth more than $1 billion came from only two startups — 23andMe and Adaptive Biotechnologies, according to the paper.
“Many years ago I was the first person to say that Theranos had a problem,” says Ioannidis. “The problem that I had then was that Theranos did not have any peer-reviewed evidence to show.”
In an interview and in their paper, Ioannidis and Cahan warn that investors have overlooked systemic problems created by the lack of transparency among healthcare startups.
They write:
It would be tempting to dismiss the Theranos case as just one rotten apple. However, we worry that the focus on fraud puts aside a more fundamental concern. Fraud is making waves in the news, but stealth research may have a more detrimental impact.
According to the study’s findings, more than half of the healthcare startups that are worth more than $1 billion have published no highly cited papers at all. For companies that were acquired or are publicly traded that number is around 40 percent.
In all, healthcare startups that are currently valued at more than $1 billion published 425 Pubmed papers. And of those papers only 34 (8 percent, including two reviews) were highly cited. For companies with valuations of more than $1 billion that had been acquired or are publicly traded on stock exchanges, the researchers counted 413 papers, of which 47 (11 percent, including nine reviews) were highly cited.
Digging deeper into some of the companies that had high valuations but little or no published research revealed scores of operational and technological issues for the researchers.
For instance, StemCentrx, which was bought for $10.2 billion in 2016 by AbbVie, had published 16 papers — and only one highly cited paper. Since the acquisition, the Food and Drug Administration had imposed a delay on the readout of the company’s phase II trial for its Rova T targeted antibody drug for cancer treatment. In December, a Phase III trial for Rova T as a second-line treatment for patients with advanced small cell lung cancer was halted because the treatment wasn’t working, according to a report in Targeted Oncology.
Acerta Pharma, another healthcare-focused startup focused on cancer treatments, was bought by AstraZeneca for $7.3 billion. That company published nine articles and had one highly cited paper for a very early study of a potential treatment for relapsed chronic lymphocytic leukemia. Acerta received accelerated approval for a drug called acalabrutinib, which treats a rare form of lymphoma called mantle cell lymphoma. Two years ago, AstraZeneca had to retract data and admit that Acerta falsified preclinical data for its drug.
Then there’s Intarcia, the developer of a device for diabetes treatment that’s worth $5.5 billion. That company had its device rejected by the FDA and was forced to lay off staff and halt a couple of later-stage trials. It had only published six papers — none of them very highly cited.
Ultimately, the researchers concluded that highly valued healthcare startups don’t contribute to published research and that the valuation of these companies by investors is divorced from any externally validated data.
For the researchers (and for investors) this should present a problem.
“Many unicorns may be overvalued [21] and subject to unrealistic scientific expectations,” the study’s authors write. And they reject the argument that simply applying for — and receiving — patents is enough to prove that a technology in the healthcare space has been thoroughly vetted. “[Patents] do not offer the same level of documentation as peer-reviewed articles. For example, Theranos had over 100 patents [1], but these were unable to supplant the vacuum in their evidence,” the researchers wrote.
Even if companies want to protect their technology, there are still ways for them to be more transparent about the results or benefits of their technology. The authors acknowledge that publishing isn’t the primary mission of startups. They can, however publish a few high-value articles, secure their technology through patents and then work with researchers, universities or hospitals to validate the technology and have those organizations publish results of the tests, the authors argue.
As the authors conclude:
Start-ups are key purveyors of innovation and disruption. Consequently, holding them to a minimal standard of evaluation from the scientific community is crucial. Participation in peer review, with all its limitations, is the best way we have to uphold this standard. We are not arguing that start-ups should divert excessive resources to having peer-reviewed papers. However, when their products are destined to affect patient health, they should neither be solely doing marketing. Confidential data sharing with potential investors or regulators cannot replace more open scrutiny by the scientific community.
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