Top Healthcare Innovators Share AI Developments at GTC

Healthcare is under the microscope this year like never before. Hospitals are being asked to do more with less, and researchers are working around the clock to answer pressing questions.

NVIDIA’s GPU Technology Conference brings everything you need to know about the future of AI and HPC in healthcare together in one place.

Innovators across healthcare will come together at the event to share how they are using AI and GPUs to supercharge their medical devices and biomedical research.

Scores of on-demand talks and hands-on training sessions will focus on AI in medical imaging, genomics, drug discovery, medical instruments and smart hospitals.

And advancements powered by GPU acceleration in fields such as imaging, genomics and drug discovery, which are playing a vital role in COVID-19 research, will take center stage at the conference.

There are over 120 healthcare sessions taking place at GTC, which will feature amazing demos, hands-on training, breakthrough research and more from October 5-9.

Turning Months into Minutes for Drug Discovery

AI and HPC are improving speed, accuracy and scalability for drug discovery. Companies and researchers are turning to AI to enhance current methods in the field. Molecular simulation like docking, free energy pertubation (FEP) and molecular dynamics requires a huge amount of computing power. At every phase of drug discovery, researchers are incorporating AI methods to accelerate the process.

Here are some drug discovery sessions you won’t want to miss:

Architecting the Next Generation of Hospitals

AI can greatly improve hospital efficiency and prevent costs from ballooning. Autonomous robots can help with surgeries, deliver blankets to patients’ rooms and perform automatic check-ins. AI systems can search patient records, monitor blood pressure and oxygen saturation levels, flag thoracic radiology images that show pneumonia, take patient temperatures and notify staff immediately of changes.

Here are some sessions on smart hospitals you won’t want to miss:

Training AI for Medical Imaging

AI models are being developed at a rapid pace to optimize medical imaging analysis for both radiology and pathology. Get exposure to cutting-edge use cases for AI in medical imaging and how developers can use the NVIDIA Clara Imaging application framework to deploy their own AI applications.

Building robust AI requires massive amounts of data. In the past, hospitals and medical institutions have struggled to share and combine their local knowledge without compromising patient privacy, but federated learning is making this possible. The learning paradigm enables different clients to securely collaborate, train and contribute to a global model. Register for this session to learn more about federated learning and its use on AI COVID-19 model development from a panel of experts.

Must-see medical imaging sessions include:

Accelerating Genomic Analysis

Genomic data is foundational in making precision medicine a reality. As next-generation sequencing becomes more routine, large genomic datasets are becoming more prevalent. Transforming the sequencing data into genetic information is just the first step in a complicated, data-intensive workflow. With high performance computing, genomic analysis is being streamlined and accelerated to enable novel discoveries about the human genome.

Genomic sessions you won’t want to miss include:

The Best of MICCAI at GTC

This year’s GTC is also bringing to attendees the best of MICCAI, a conference focused on cutting-edge deep learning medical imaging research. Developers will have the opportunity to dive into the papers presented, connect with the researchers at a variety of networking opportunities, and watch on-demand trainings from the first ever MONAI Bootcamp hosted at MICCAI.

Game-Changing Healthcare Startups

Over 70 healthcare AI startups from the NVIDIA Inception program will showcase their latest breakthroughs at GTC. Get inspired by the AI- and HPC-powered technologies these startups are developing for personalized medicine and next-generation clinics.

Here are some Inception member-led talks not to miss:

Make New Connections, Share Ideas

GTC will have new ways to connect with fellow attendees who are blazing the trail for healthcare and biomedical innovation. Join a Dinner with Strangers conversation to network with peers on topics spanning drug discovery, medical imaging, genomics and intelligent instrument development. Or, book a Braindate to have a knowledge-sharing conversation on a topic of your choice with a small group or one-on-one.

Learn more about networking opportunities at GTC.

Brilliant Minds Never Turn Off

GTC will showcase the hard work and groundbreaking discoveries of developers, researchers, engineers, business leaders and technologists from around the world. Nowhere else can you access five days of continuous programming with regionally tailored content. This international event will unveil the future of healthcare technology, all in one place.

Check out the full healthcare session lineup at GTC, including talks from over 80 startups using AI to transform healthcare, and register for the event today.

The post Top Healthcare Innovators Share AI Developments at GTC appeared first on The Official NVIDIA Blog.

NVIDIA and Arm to Create World-Class AI Research Center in Cambridge

Artificial intelligence is the most powerful technology force of our time. 

It is the automation of automation, where software writes software. While AI began in the data center, it is moving quickly to the edge — to stores, warehouses, hospitals, streets, and airports, where smart sensors connected to AI computers can speed checkouts, direct forklifts, orchestrate traffic, and save power. In time, there will be trillions of these small autonomous computers powered by AI, connected by massively powerful cloud data centers in every corner of the world.

But in many ways, the field is just getting started. That’s why we are excited to be creating a world-class AI laboratory in Cambridge, at the Arm headquarters: a Hadron collider or Hubble telescope, if you like, for artificial intelligence.  

NVIDIA, together with Arm, is uniquely positioned to launch this effort. NVIDIA is the leader in AI computing, while Arm is present across a vast ecosystem of edge devices, with more than 180 billion units shipped. With this newly announced combination, we are creating the leading computing company for the age of AI. 

Arm is an incredible company and it employs some of the greatest engineering minds in the world. But we believe we can make Arm even more incredible and take it to even higher levels. We want to propel it — and the U.K. — to global AI leadership.

We will create an open center of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key NVIDIA technologies are named. Here, leading scientists, engineers and researchers from the U.K. and around the world will come develop their ideas, collaborate and conduct their ground-breaking work in areas like healthcare, life sciences, self-driving cars and other fields. We want the U.K. to attract the best minds and talent from around the world. 

The center in Cambridge will include: 

  • An Arm/NVIDIA-based supercomputer. Expected to be one of the most powerful AI supercomputers in the world, this system will combine state-of-the art Arm CPUs, NVIDIA’s most advanced GPU technology, and NVIDIA Mellanox DPUs, along with high-performance computing and AI software from NVIDIA and our many partners. For reference, the world’s fastest supercomputer, Fugaku in Japan, is Arm-based, and NVIDIA’s own supercomputer Selene is the seventh most powerful system in the world.  
  • Research Fellowships and Partnerships. In this center, NVIDIA will expand research partnerships within the U.K., with academia and industry to conduct research covering leading-edge work in healthcare, autonomous vehicles, robotics, data science and more. NVIDIA already has successful research partnerships with King’s College and Oxford. 
  • AI Training. NVIDIA’s education wing, the Deep Learning Institute, has trained more than 250,000 students on both fundamental and applied AI. NVIDIA will create an institute in Cambridge, and make our curriculum available throughout the U.K. This will provide both young people and mid-career workers with new AI skills, creating job opportunities and preparing the next generation of U.K. developers for AI leadership. 
  • Startup Accelerator. Much of the leading-edge work in AI is done by startups. NVIDIA Inception, a startup accelerator program, has more than 6,000 members — with more than 400 based in the U.K. NVIDIA will further its investment in this area by providing U.K. startups with access to the Arm supercomputer, connections to researchers from NVIDIA and partners, technical training and marketing promotion to help them grow. 
  • Industry Collaboration. The NVIDIA AI research facility will be an open hub for industry collaboration, providing a uniquely powerful center of excellence in Britain. NVIDIA’s industry partnerships include GSK, Oxford Nanopore and other leaders in their fields. From helping to fight COVID-19 to finding new energy sources, NVIDIA is already working with industry across the U.K. today — but we can and will do more. 

We are ambitious. We can’t wait to build on the foundations created by the talented minds of NVIDIA and Arm to make Cambridge the next great AI center for the world. 

The post NVIDIA and Arm to Create World-Class AI Research Center in Cambridge appeared first on The Official NVIDIA Blog.

NVIDIA and Arm to Create World-Class AI Research Center in Cambridge

Artificial intelligence is the most powerful technology force of our time. 

It is the automation of automation, where software writes software. While AI began in the data center, it is moving quickly to the edge — to stores, warehouses, hospitals, streets, and airports, where smart sensors connected to AI computers can speed checkouts, direct forklifts, orchestrate traffic, and save power. In time, there will be trillions of these small autonomous computers powered by AI, connected by massively powerful cloud data centers in every corner of the world.

But in many ways, the field is just getting started. That’s why we are excited to be creating a world-class AI laboratory in Cambridge, at the Arm headquarters: a Hadron collider or Hubble telescope, if you like, for artificial intelligence.  

NVIDIA, together with Arm, is uniquely positioned to launch this effort. NVIDIA is the leader in AI computing, while Arm is present across a vast ecosystem of edge devices, with more than 180 billion units shipped. With this newly announced combination, we are creating the leading computing company for the age of AI. 

Arm is an incredible company and it employs some of the greatest engineering minds in the world. But we believe we can make Arm even more incredible and take it to even higher levels. We want to propel it — and the U.K. — to global AI leadership.

We will create an open center of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key NVIDIA technologies are named. Here, leading scientists, engineers and researchers from the U.K. and around the world will come develop their ideas, collaborate and conduct their ground-breaking work in areas like healthcare, life sciences, self-driving cars and other fields. We want the U.K. to attract the best minds and talent from around the world. 

The center in Cambridge will include: 

  • An Arm/NVIDIA-based supercomputer. Expected to be one of the most powerful AI supercomputers in the world, this system will combine state-of-the art Arm CPUs, NVIDIA’s most advanced GPU technology, and NVIDIA Mellanox DPUs, along with high-performance computing and AI software from NVIDIA and our many partners. For reference, the world’s fastest supercomputer, Fugaku in Japan, is Arm-based, and NVIDIA’s own supercomputer Selene is the seventh most powerful system in the world.  
  • Research Fellowships and Partnerships. In this center, NVIDIA will expand research partnerships within the U.K., with academia and industry to conduct research covering leading-edge work in healthcare, autonomous vehicles, robotics, data science and more. NVIDIA already has successful research partnerships with King’s College and Oxford. 
  • AI Training. NVIDIA’s education wing, the Deep Learning Institute, has trained more than 250,000 students on both fundamental and applied AI. NVIDIA will create an institute in Cambridge, and make our curriculum available throughout the U.K. This will provide both young people and mid-career workers with new AI skills, creating job opportunities and preparing the next generation of U.K. developers for AI leadership. 
  • Startup Accelerator. Much of the leading-edge work in AI is done by startups. NVIDIA Inception, a startup accelerator program, has more than 6,000 members — with more than 400 based in the U.K. NVIDIA will further its investment in this area by providing U.K. startups with access to the Arm supercomputer, connections to researchers from NVIDIA and partners, technical training and marketing promotion to help them grow. 
  • Industry Collaboration. The NVIDIA AI research facility will be an open hub for industry collaboration, providing a uniquely powerful center of excellence in Britain. NVIDIA’s industry partnerships include GSK, Oxford Nanopore and other leaders in their fields. From helping to fight COVID-19 to finding new energy sources, NVIDIA is already working with industry across the U.K. today — but we can and will do more. 

We are ambitious. We can’t wait to build on the foundations created by the talented minds of NVIDIA and Arm to make Cambridge the next great AI center for the world. 

The post NVIDIA and Arm to Create World-Class AI Research Center in Cambridge appeared first on The Official NVIDIA Blog.

Vision of AI: Startup Helps Diabetic Retinopathy Patients Retain Their Sight

Every year, 60,000 people go blind from diabetic retinopathy, a condition caused by damage to the blood vessels in the eye and a risk factor of high blood sugar levels.

Digital Diagnostics, a software-defined AI medical imaging company formerly known as IDx, is working to help those people retain their vision, using NVIDIA technology to do so.

The startup was founded a decade ago by Michael Abramoff, a retinal surgeon with a Ph.D. in computer science. While training as a surgeon, Abramoff often saw patients with diabetic retinopathy, or DR, that had progressed too far to be treated effectively, leading to permanent vision loss.

With the mission of increasing access to and quality of DR diagnosis, as well as decreasing its cost, Abramoff and his team have created an AI-based solution.

The company’s product, IDx-DR, takes images of the back of the eye, analyzes them and provides a diagnosis within minutes — referring the patient to a specialist for treatment if a more than mild case is detected.

The system is optimized on NVIDIA GPUs and its deep learning pipeline was built using the NVIDIA cuDNN library for high-performance GPU-accelerated operations. Training occurs using Amazon EC2 P3 instances featuring NVIDIA V100 Tensor Core GPUs and is based on images of DR cases confirmed by retinal specialists.

IDx-DR enables diagnostic tests to be completed in easily accessible settings like drugstores or primary care providers’ offices, rather than only at ophthalmology clinics, said John Bertrand, CEO at Digital Diagnostics.

“Moving care to locations the patient is already visiting improves access and avoids extra visits that overwhelm specialty physician schedules,” he said. “Patients avoid an extra copay and don’t have to take time off work for a second appointment.”

Autonomous, Not Just Assistive

“There are lots of good AI products specifically created to assist physicians and increase the detection rate of finding an abnormality,” said Bertrand. “But to allow physicians to practice to the top of their license, and reduce the costs of these low complexity tests, you need to use autonomous AI,” he said.

IDx-DR is the first FDA-cleared autonomous AI system — meaning that while the FDA has cleared many AI-based applications, IDx-DR was the first that doesn’t require physician oversight.

Clinical trials using IDx-DR consisted of machine operators who didn’t have prior experience taking retinal photographs, simulating the way the product would be used in the real world, according to Bertrand.

“Anyone with a high school diploma can perform the exam,” he said.

The platform has been deployed in more than 20 sites across the U.S., including Blessing Health System, in Illinois, where family medicine doctor Tim Beth said, “Digital Diagnostics has done well in developing an algorithm that can detect the possibility of early disease. We would be missing patients if we didn’t use IDx-DR.”

In addition to DR, Digital Diagnostics has created prototypes for products that diagnose glaucoma and age-related macular degeneration. The company is also looking to provide solutions for healthcare issues beyond eye-related conditions, including those related to the skin, nose and throat.

Stay up to date with the latest healthcare news from NVIDIA.

Digital Diagnostics is a Premier member of NVIDIA Inception, a program that supports AI startups with go-to-market support, expertise and technology.

The post Vision of AI: Startup Helps Diabetic Retinopathy Patients Retain Their Sight appeared first on The Official NVIDIA Blog.

The Great AI Bake-Off: Recommendation Systems on the Rise

If you want to create a world-class recommendation system, follow this recipe from a global team of experts: Blend a big helping of GPU-accelerated AI with a dash of old-fashioned cleverness.

The proof was in the pudding for a team from NVIDIA that won this year’s ACM RecSys Challenge. The competition is a highlight of an annual gathering of more than 500 experts who present the latest research in recommendation systems, the engines that deliver personalized suggestions for everything from restaurants to real estate.

At the Sept. 22-26 online event, the team will describe its dish, already available as open source code. They’re also sharing lessons learned with colleagues who build NVIDIA products like RAPIDS and Merlin, so customers can enjoy the fruits of their labor.

In an effort to bring more people to the table, NVIDIA will donate the contest’s $15,000 cash prize to Black in AI, a nonprofit dedicated to mentoring the next generation of Black specialists in machine learning.

GPU Server Doles Out Recommendations

This year’s contest, sponsored by Twitter, asked researchers to comb through a dataset of 146 million tweets to predict which ones a user would like, reply or retweet. The NVIDIA team’s work led a field of 34 competitors, thanks in part to a system with four NVIDIA V100 Tensor Core GPUs that cranked through hundreds of thousands of options.

Their numbers were eye-popping. GPU-accelerated software engineered in less than a minute features that required nearly an hour on a CPU, a 500x speedup. The four-GPU system trained the team’s AI models 120x faster than a CPU. And GPUs gave the group’s end-to-end solution a 280x speedup compared to an initial implementation on a CPU.

“I’m still blown away when we pull off something like a 500x speedup in feature engineering,” said Even Oldridge, a Ph.D. in machine learning who in the past year quadrupled the size of his group that designs NVIDIA Merlin, a framework for recommendation systems.

Recommendation systems on GPUs
GPUs and frameworks such as UCX provided up to 500x speedups compared to CPUs.

Competition Sparks Ideas for Software Upgrades  

The competition spawned work on data transformations that could enhance future versions of NVTabular, a Merlin library that eases engineering new features with the spreadsheet-like tables that are the basis of recommendation systems.

“We won in part because we could prototype fast,” said Benedikt Schifferer, one of three specialists in recommendation systems on the team that won the prize.

Schifferer also credits two existing tools. DASK, an open-source scheduling tool, let the team split memory-hungry jobs across multiple GPUs. And cuDF, part of NVIDIA’s RAPIDS framework for accelerated data science, let the group run the equivalent of the popular Pandas library on GPUs.

“Searching for features in the data using Pandas on CPUs took hours for each new feature,” said Chris Deotte, one of a handful of data scientists on the team who have earned the title Kaggle grandmaster for their prowess in competitions.

“When we converted our code to RAPIDS, we could explore features in minutes. It was life changing, we could search hundreds of features and that eventually led to discoveries that won that competition,” said Deotte, one of only two grandmasters who hold that title in all four Kaggle categories.

More enhancements for recommendation systems are on the way. For example, customers can look forward to improvements in text handling on GPUs, a key data type for recommendation systems.

An Aha! Moment Fuels the Race

Deotte credits a colleague in Brazil, Gilberto Titericz, with an insight that drove the team forward.

“He tracked changes in Twitter followers over time which turned out to be a feature that really fueled our accuracy — it was incredibly effective,” Deotte said.

“I saw patterns changing over time, so I made several plots of them,” said Titericz, who ranked as the top Kaggle grandmaster worldwide for a couple years.

“When I saw a really great result, I thought I made a mistake, but I took a chance, submitted it and to my surprise it scored high on the leaderboard, so my intuition was right,” he added.

In the end, the team used a mix of complementary AI models designed by Titericz, Schifferer and a colleague in Japan, Kazuki Onodera, all based on XGBoost, an algorithm well suited for recommendation systems.

Several members of the team are part of an elite group of Kaggle grandmasters that NVIDIA founder and CEO Jensen Huang dubbed KGMON, a playful takeoff on Pokemon. The team won dozens of competitions in the last four years.

Recommenders Getting Traction in B2C

For many members, including team leader Jean-Francois Puget in southern France, it’s more than a 9-to-5 job.

“We spend nights and weekends in competitions, too, trying to be the best in the world,” said Puget, who earned his Ph.D. in machine learning two decades before deep learning took off commercially.

Now the technology is spreading fast.

This year’s ACM RecSys includes three dozen papers and talks from companies like Amazon and Netflix that helped establish the field with recommenders that help people find books and movies. Now, consumer companies of all stripes are getting into the act including IKEA and Etsy, which are presenting at ACM RecSys this year.

“For the last three or four years, it’s more focused on delivering a personalized experience, really understanding what users want,” said Schifferer. It’s a cycle where “customers’ choices influence the training data, so some companies retrain their AI models every four hours, and some say they continuously train,” he added.

That’s why the team works hard to create frameworks like Merlin to make recommendation systems run easily and fast at scale on GPUs. Other members of NVIDIA’s winning team were Christof Henkel (Germany), Jiwei Liu and Bojan Tunguz (U.S.), Gabriel De Souza Pereira Moreira (Brazil) and Ahmet Erdem (Netherlands).

To get tips on how to design recommendation systems from the winning team, tune in to an online tutorial here on Friday, Sept. 25.

The post The Great AI Bake-Off: Recommendation Systems on the Rise appeared first on The Official NVIDIA Blog.

Startup’s AI Platform Allows Contact-Free Hospital Interactions

Hands-free phone calls and touchless soap dispensers have been the norm for years. Next up, contact-free hospitals.

San Francisco-based startup Ouva has created a hospital intelligence platform that monitors patient safety, acts as a patient assistant and provides a sensory experience in waiting areas — without the need for anyone to touch anything.

The platform uses the NVIDIA Clara Guardian application framework so its optical sensors can take in, analyze and provide healthcare professionals with useful information, like whether a patient with high fall-risk is out of bed. The platform is optimized on NVIDIA GPUs and its edge deployments use the NVIDIA Jetson TX1 module.

Ouva is a member of NVIDIA Inception, a program that provides AI startups go-to-market support, expertise and technology. Inception partners also have access to NVIDIA’s technical team.

Dogan Demir, founder and CEO of Ouva, said, “The Inception program informs us of hardware capabilities that we didn’t even know about, which really speeds up our work.”

Patient Care Automation 

The Ouva platform automates patient monitoring, which is critical during the pandemic.

“To prevent the spread of COVID-19, we need to minimize contact between staff and patients,” said Demir. “With our solution, you don’t need to be in the same room as a patient to make sure that they’re okay.”

More and more hospitals use video monitoring to ensure patient safety, he said, but without intelligent video analytics, this can entail a single nurse trying to keep an eye on up to 100 video feeds at once to catch an issue in a patient’s room.

By detecting changes in patient movement and alerting workers of them in real time, the Ouva platform allows nurses to pay attention to the right patient at the right time.

The Ouva platform alerts nurses to changes in patient movement.

“The platform minimizes the time that nurses may be in the dark about how a patient is doing,” said Demir. “This in turn reduces the need for patients to be transferred to the ICU due to situations that could’ve been prevented, like a fall or brain injury digression due to a seizure.”

According to Ouva’s research, the average hospitalization cost for a fall injury is $35,000, with an additional $43,000 estimated per person with a pressure injury like an ulcer from the hospital bed. This means that by preventing falls and monitoring a patient’s position changes, Ouva could help save $4 million per year for a 100-bed facility.

Ouva’s system also performs personal protective equipment checks and skin temperature screenings, as well as flags contaminated areas for cleaning, which can reduce a nurse’s hours and contact with patients.

Radboud University Medical Center in the Netherlands recently integrated Ouva’s platform for 10 of its neurology wards.

“Similar solutions typically require contact with the patient’s body, which creates an infection and maintenance risk,” said Dr. Harry van Goor from the facility. “The Ouva solution centrally monitors patient safety, room hygiene and bed turnover in real time while preserving patients’ privacy.”

Patient Assistant and Sensory Experience

The platform can also guide patients through a complex hospital facility by providing answers to voice-activated questions about building directions. Medical City Hospital in Dallas was the first to pick up this voice assistant solution for their Heart and Spine facilities at the start of COVID-19.

In waiting areas, patients can participate in Ouva’s touch-free sensory experience by gesturing at 60-foot video screens that wrap around walls, featuring images of gardens, beaches and other interactive locations.

The goal of the sensory experience, made possible by NVIDIA GPUs, is to reduce waiting room anxiety and improve patient health outcomes, according to Demir.

“The amount of pain that a patient feels during treatment can be based on their perception of the care environment,” said Demir. “We work with physical and occupational therapists to design interactive gestures that allow people to move their bodies in ways that both improve their health and their perception of the hospital environment.”

Watch Ouva’s sensory experience in action:

Stay up to date with the latest healthcare news from NVIDIA and check out our COVID-19 research hub.

The post Startup’s AI Platform Allows Contact-Free Hospital Interactions appeared first on The Official NVIDIA Blog.

DIY with AI: GTC to Host NVIDIA Deep Learning Institute Courses for Anyone, Anywhere

The NVIDIA Deep Learning Institute is launching three new courses, which can be taken for the first time ever at the GPU Technology Conference next month. 

The new instructor-led workshops cover fundamentals of deep learning, recommender systems and Transformer-based applications. Anyone connected online can join for a nominal fee, and participants will have access to a fully configured, GPU-accelerated server in the cloud. 

DLI instructor-led trainings consist of hands-on remote learning taught by NVIDIA-certified experts in virtual classrooms. Participants can interact with their instructors and peers in real time. They can whiteboard ideas, tackle interactive coding challenges and earn a DLI certificate of subject competency to support their professional growth.

DLI at GTC is offered globally, with several courses available in Korean, Japanese and Simplified Chinese for attendees in their respective time zones.

New DLI workshops launching at GTC include:

  • Fundamentals of Deep Learning — Build the confidence to take on a deep learning project by learning how to train a model, work with common data types and model architectures, use transfer learning between models, and more.
  • Building Intelligent Recommender Systems — Create different types of recommender systems: content-based, collaborative filtering, hybrid, and more. Learn how to use the open-source cuDF library, Apache Arrow, alternating least squares, CuPy and TensorFlow 2 to do so.
  • Building Transformer-Based Natural Language Processing Applications — Learn about NLP topics like Word2Vec and recurrent neural network-based embeddings, as well as Transformer architecture features and how to improve them. Use pre-trained NLP models for text classification, named-entity recognition and question answering, and deploy refined models for live applications.

Other DLI offerings at GTC will include:

  • Fundamentals of Accelerated Computing with CUDA Python — Dive into how to use Numba to compile NVIDIA CUDA kernels from NumPy universal functions, as well as create and launch custom CUDA kernels, while applying key GPU memory management techniques.
  • Applications of AI for Predictive Maintenance — Leverage predictive maintenance and identify anomalies to manage failures and avoid costly unplanned downtimes, use time-series data to predict outcomes using machine learning classification models with XGBoost, and more.
  • Fundamentals of Accelerated Data Science with RAPIDS — Learn how to use cuDF and Dask to ingest and manipulate large datasets directly on the GPU, applying GPU-accelerated machine learning algorithms including XGBoost, cuGRAPH and cuML to perform data analysis at massive scale.
  • Fundamentals of Accelerated Computing with CUDA C/C++ — Find out how to accelerate CPU-only applications to run their latent parallelism on GPUs, using techniques like essential CUDA memory management to optimize accelerated applications.
  • Fundamentals of Deep Learning for Multi-GPUs — Scale deep learning training to multiple GPUs, significantly shortening the time required to train lots of data and making solving complex problems with deep learning feasible.
  • Applications of AI for Anomaly Detection — Discover how to implement multiple AI-based solutions to identify network intrusions, using accelerated XGBoost, deep learning-based autoencoders and generative adversarial networks.

With more than 2 million registered NVIDIA developers working on technological breakthroughs to solve the world’s toughest problems, the demand for deep learning expertise is greater than ever. The full DLI course catalog includes a variety of topics for anyone interested in learning more about AI, accelerated computing and data science.

Get a glimpse of the DLI experience:

Workshops have limited seating, with the early bird deadline on Sep 25. Register now.

The post DIY with AI: GTC to Host NVIDIA Deep Learning Institute Courses for Anyone, Anywhere appeared first on The Official NVIDIA Blog.

What Is MLOps?

MLOps may sound like the name of a shaggy, one-eyed monster, but it’s actually an acronym that spells success in enterprise AI.

A shorthand for machine learning operations, MLOps is a set of best practices for businesses to run AI successfully.

MLOps is a relatively new field because commercial use of AI is itself fairly new.

MLOps: Taking Enterprise AI Mainstream

The Big Bang of AI sounded in 2012 when a researcher won an image-recognition contest using deep learning. The ripples expanded quickly.

Today, AI translates web pages and automatically routes customer service calls. It’s helping hospitals read X-rays, banks calculate credit risks and retailers stock shelves to optimize sales.

In short, machine learning, one part of the broad field of AI, is set to become as mainstream as software applications. That’s why the process of running ML needs to be as buttoned down as the job of running IT systems.

Machine Learning Layered on DevOps

MLOps is modeled on the existing discipline of DevOps, the modern practice of efficiently writing, deploying and running enterprise applications. DevOps got its start a decade ago as a way warring tribes of software developers (the Devs) and IT operations teams (the Ops) could collaborate.

MLOps adds to the team the data scientists, who curate datasets and build AI models that analyze them. It also includes ML engineers, who run those datasets through the models in disciplined, automated ways.

MLOps combine machine learning, applications development and IT operations. Source: Neal Analytics

It’s a big challenge in raw performance as well as management rigor. Datasets are massive and growing, and they can change in real time. AI models require careful tracking through cycles of experiments, tuning and retraining.

So, MLOps needs a powerful AI infrastructure that can scale as companies grow. For this foundation, many companies use NVIDIA DGX systems, CUDA-X and other software components available on NVIDIA’s software hub, NGC.

Lifecycle Tracking for Data Scientists

With an AI infrastructure in place, an enterprise data center can layer on the following elements of an MLOps software stack:

  • Data sources and the datasets created from them
  • A repository of AI models tagged with their histories and attributes
  • An automated ML pipeline that manages datasets, models and experiments through their lifecycles
  • Software containers, typically based on Kubernetes, to simplify running these jobs

It’s a heady set of related jobs to weave into one process.

Data scientists need the freedom to cut and paste datasets together from external sources and internal data lakes. Yet their work and those datasets need to be carefully labeled and tracked.

Likewise, they need to experiment and iterate to craft great models well torqued to the task at hand. So they need flexible sandboxes and rock-solid repositories.

And they need ways to work with the ML engineers who run the datasets and models through prototypes, testing and production. It’s a process that requires automation and attention to detail so models can be easily interpreted and reproduced.

Today, these capabilities are becoming available as part of cloud-computing services. Companies that see machine learning as strategic are creating their own AI centers of excellence using MLOps services or tools from a growing set of vendors.

Gartner on ML pipeline
Gartner’s view of the machine-learning pipeline

Data Science in Production at Scale

In the early days, companies such as Airbnb, Facebook, Google, NVIDIA and Uber had to build these capabilities themselves.

“We tried to use open source code as much as possible, but in many cases there was no solution for what we wanted to do at scale,” said Nicolas Koumchatzky, a director of AI infrastructure at NVIDIA.

“When I first heard the term MLOps, I realized that’s what we’re building now and what I was building before at Twitter,” he added.

Koumchatzky’s team at NVIDIA developed MagLev, the MLOps software that hosts NVIDIA DRIVE, our platform for creating and testing autonomous vehicles. As part of its foundation for MLOps, it uses the NVIDIA Container Runtime and Apollo, a set of components developed at NVIDIA to manage and monitor Kubernetes containers running across huge clusters.

Laying the Foundation for MLOps at NVIDIA

Koumchatzky’s team runs its jobs on NVIDIA’s internal AI infrastructure based on GPU clusters called DGX PODs.  Before the jobs start, the infrastructure crew checks whether they are using best practices.

First, “everything must run in a container — that spares an unbelievable amount of pain later looking for the libraries and runtimes an AI application needs,” said Michael Houston, whose team builds NVIDIA’s AI systems including Selene, a DGX SuperPOD recently ranked the most powerful industrial computer in the U.S.

Among the team’s other checkpoints, jobs must:

  • Launch containers with an approved mechanism
  • Prove the job can run across multiple GPU nodes
  • Show performance data to identify potential bottlenecks
  • Show profiling data to ensure the software has been debugged

The maturity of MLOps practices used in business today varies widely, according to Edwin Webster, a data scientist who started the MLOps consulting practice a year ago for Neal Analytics and wrote an article defining MLOps. At some companies, data scientists still squirrel away models on their personal laptops, others turn to big cloud-service providers for a soup-to-nuts service, he said.

Two MLOps Success Stories

Webster shared success stories from two of his clients.

One involves a large retailer that used MLOps capabilities in a public cloud service to create an AI service that reduced waste 8-9 percent with daily forecasts of when to restock shelves with perishable goods. A budding team of data scientists at the retailer created datasets and built models; the cloud service packed key elements into containers, then ran and managed the AI jobs.

Another involves a PC maker that developed software using AI to predict when its laptops would need maintenance so it could automatically install software updates. Using established MLOps practices and internal specialists, the OEM wrote and tested its AI models on a fleet of 3,000 notebooks. The PC maker now provides the software to its largest customers.

Many, but not all, Fortune 100 companies are embracing MLOps, said Shubhangi Vashisth, a senior principal analyst following the area at Gartner. “It’s gaining steam, but it’s not mainstream,” she said.

Vashisth co-authored a white paper that lays out three steps for getting started in MLOps: Align stakeholders on the goals, create an organizational structure that defines who owns what, then define responsibilities and roles — Gartner lists a dozen of them.

Gartner on MLOps which it here calls the machine learning development lifecycle
Gartner refers to the overall MLOps process as the machine learning development lifecycle (MLDLC).

Beware Buzzwords: AIOps, DLOps, DataOps, and More

Don’t get lost in a forest of buzzwords that have grown up along this avenue. The industry has clearly coalesced its energy around MLOps.

By contrast, AIOps is a narrower practice of using machine learning to automate IT functions. One part of AIOps is IT operations analytics, or ITOA. Its job is to examine the data AIOps generate to figure out how to improve IT practices.

Similarly, some have coined the terms DataOps and ModelOps to refer to the people and processes for creating and managing datasets and AI models, respectively. Those are two important pieces of the overall MLOps puzzle.

Interestingly, every month thousands of people search for the meaning of DLOps. They may imagine DLOps are IT operations for deep learning. But the industry uses the term MLOps, not DLOps, because deep learning is a part of the broader field of machine learning.

Despite the many queries, you’d be hard pressed to find anything online about DLOps. By contrast, household names like Google and Microsoft as well as up-and-coming companies like Iguazio and Paperspace have posted detailed white papers on MLOps.

MLOps: An Expanding Software and Services Smorgasbord

Those who prefer to let someone else handle their MLOps have plenty of options.

Major cloud-service providers like Alibaba, AWS and Oracle are among several that offer end-to-end services accessible from the comfort of your keyboard.

For users who spread their work across multiple clouds, DataBricks’ MLFlow supports MLOps services that work with multiple providers and multiple programming languages, including Python, R and SQL. Other cloud-agnostic alternatives include open source software such as Polyaxon and KubeFlow.

Companies that believe AI is a strategic resource they want behind their firewall can choose from a growing list of third-party providers of MLOps software. Compared to open-source code, these tools typically add valuable features and are easier to put into use.

NVIDIA certified products from six of them as part of its DGX-Ready Software program-:

  • Allegro AI
  • cnvrg.io
  • Core Scientific
  • Domino Data Lab
  • Iguazio
  • Paperspace

All six vendors provide software to manage datasets and models that can work with Kubernetes and NGC.

It’s still early days for off-the-shelf MLOps software.

Gartner tracks about a dozen vendors offering MLOps tools including ModelOp and ParallelM now part of DataRobot, said analyst Vashisth. Beware offerings that don’t cover the entire process, she warns. They force users to import and export data between programs users must stitch together themselves, a tedious and error-prone process.

The edge of the network, especially for partially connected or unconnected nodes, is another underserved area for MLOps so far, said Webster of Neal Analytics.

Koumchatzky, of NVIDIA, puts tools for curating and managing datasets at the top of his wish list for the community.

“It can be hard to label, merge or slice datasets or view parts of them, but there is a growing MLOps ecosystem to address this. NVIDIA has developed these internally, but I think it is still undervalued in the industry.” he said.

Long term, MLOps needs the equivalent of IDEs, the integrated software development environments like Microsoft Visual Studio that apps developers depend on. Meanwhile Koumchatzky and his team craft their own tools to visualize and debug AI models.

The good news is there are plenty of products for getting started in MLOps.

In addition to software from its partners, NVIDIA provides a suite of mainly open-source tools for managing an AI infrastructure based on its DGX systems, and that’s the foundation for MLOps. These software tools include:

Many are available on NGC and other open source repositories. Pulling these ingredients into a recipe for success, NVIDIA provides a reference architecture for creating GPU clusters called DGX PODs.

In the end, each team needs to find the mix of MLOps products and practices that best fits its use cases. They all share a goal of creating an automated way to run AI smoothly as a daily part of a company’s digital life.

 

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Speed Reader: Startup Primer Helps Analysts Make Every Second Count

Expected to read upwards of 200,000 words daily from hundreds, if not thousands, of documents, financial analysts are asked to perform the impossible.

Primer is using AI to apply the equivalent of compression technology to this mountain of data to help make work easier for them as well as analysts across a range of other industries.

The five-year-old company, based in San Francisco, has built a natural language processing and machine learning platform that essentially does all the reading and collating for analysts in a tiny fraction of the time it would normally take them.

Whatever a given analyst might be monitoring, whether it’s a natural disaster, credit default or geo-political event, Primer slashes hours of human research into a few seconds of analysis.

The software combs through massive amounts of content, highlights pertinent information such as quotes and facts, and assembles them into related lists. It distills vast topics into the essentials in seconds.

“We train the models to mimic that human behavior,” said Barry Dauber, vice president of commercial sales at Primer. “It’s really a powerful analyst platform that uses natural language processing and machine learning to surface and summarize information at scale.”

The Power of 1,000 Analysts

Using Primer’s platform running on NVIDIA GPUs is akin to giving an analyst a virtual staff that delivers near-instantaneous results. The software can analyze and report on tens of thousands of documents from financial reports, internal proprietary content, social media, 30,000-40,000 news sources and elsewhere.

“Every time an analyst wants to know something about Syria, we cluster together documents about Syria, in real time,” said Ethan Chan, engineering manager and staff machine learning engineer at Primer. “The goal is to reduce the amount of effort an analyst has to expend to process more information.”

Primer has done just that to the relief of its customers, which includes financial services firms, government agencies and an array of Fortune 500 companies.

As powerful as Primer’s natural language processing algorithms are, up until two years ago they required 20 minutes to deliver results because of the complexity of the document clustering they were asking CPUs to support.

“The clustering was the bottleneck,” said Chan. “Because we have to compare every document with every other document, we’re looking at nearly a trillion flops for a million documents.”

GPUs Slash Analysis Times

Primer’s team added GPUs to the clustering process in 2018 after joining NVIDIA Inception — an accelerator program for AI startups — and quickly slashed those analysis times to mere seconds.

Primer’s GPU work unfolds in the cloud, where it makes equally generous use of AWS, Google Cloud and Microsoft Azure. For prototyping and training of its NLP algorithms such as Named Entity Recognition and Headline Generation (on public, open-source news datasets), Primer uses instances with NVIDIA V100 Tensor Core GPUs.

Model serving and clustering happens on instances with NVIDIA T4 GPUs, which can be dialed up and down based on clustering needs. The company also uses a wrapper called CuPy, which allows for CUDA-powered acceleration of GPUs on Python.

But what Chan believes is Primer’s most innovative use of GPUs is in acceleration of its clustering algorithms.

“Grouping documents together is not something anyone else is doing,” he said, adding that Primer’s success in this area further establishes that “you can use NVIDIA for new use cases and new markets.”

Flexible Delivery Model

With the cloud-based SaaS model, customers can increase or decrease their analysis speed, depending on how much they want to spend on GPUs.

Primer’s offering can also be deployed in a customer’s data center. There, the models can be trained on a customer’s IP and clustering can be performed on premises. This is an important consideration for those working in highly regulated or sensitive markets.

Analysts in finance and national security are currently Primer’s primary users, however, the company could help anyone tasked with combing through mounds of data actually make decisions instead of preparing to make decisions.

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Pixel Perfect: V7 Labs Automates Image Annotation for Deep Learning Models

Cells under a microscope, grapes on a vine and species in a forest are just a few of the things that AI can identify using the image annotation platform created by startup V7 Labs.

Whether a user wants AI to detect and label images showing equipment in an operating room or livestock on a farm, the London-based company offers V7 Darwin, an AI-powered web platform with a trained model that already knows what almost any object looks like, according to Alberto Rizzoli, co-founder of V7 Labs.

It’s a boon for small businesses and other users that are new to AI or want to reduce the costs of training deep learning models with custom data. Users can load their data onto the platform, which then segments objects and annotates them. It also allows for training and deploying models.

V7 Darwin is trained on several million images and optimized on NVIDIA GPUs. The startup is also exploring the use of NVIDIA Clara Guardian, which includes NVIDIA DeepStream SDK intelligent video analytics framework on edge AI embedded systems. So far, it’s piloted laboratory perception, quality inspection, and livestock monitoring projects, using the NVIDIA Jetson AGX Xavier and Jetson TX2 modules for the edge deployment of trained models.

V7 Labs is a member of NVIDIA Inception, a program that provides AI startups with go-to-market support, expertise and technology assistance.

Pixel-Perfect Object Classification

“For AI to learn to see something, you need to give it examples,” said Rizzoli. “And to have it accurately identify an object based on an image, you need to make sure the training sample captures 100 percent of the object’s pixels.”

Annotating and labeling an object based on such a level of “pixel-perfect” granular detail takes just two-and-a-half seconds for V7 Darwin — up to 50x faster than a human, depending on the complexity of the image, said Rizzoli.

Saving time and costs around image annotation is especially important in the context of healthcare, he said. Healthcare professionals must look at hundreds of thousands of X-ray or CT scans and annotate abnormalities, Rizzoli said, but this can be automated.

For example, during the COVID-19 pandemic, V7 Labs worked with the U.K.’s National Health Service and Italy’s San Matteo Hospital to develop a model that detects the severity of pneumonia in a chest X-ray and predicts whether a patient will need to enter an intensive care unit.

The company also published an open dataset with over 6,500 X-ray images showing pneumonia, 500 cases of which were caused by COVID-19.

V7 Darwin can be used in a laboratory setting, helping to detect protocol errors and automatically log experiments.

Application Across Industries

Companies in a wide variety of industries beyond healthcare can benefit from V7’s technology.

“Our goal is to capture all of computer vision and make it remarkably easy to use” said Rizzoli. “We believe that if we can identify a cell under a microscope, we can also identify, say, a house from a satellite. And if we can identify a doctor performing an operation or a lab technician performing an experiment, we can also identify a sculptor or a person preparing a cake.”

Global uses of the platform include assessing the damage of natural disasters, observing the growth of human and animal embryos, detecting caries in dental X-rays, creating autonomous machines to evaluate safety protocols in manufacturing, and allowing farming robots to count their harvests.

Stay up to date with the latest healthcare news from NVIDIA, and explore how AI, accelerated computing, and GPU technology contribute to the worldwide battle against the novel coronavirus on our COVID-19 research hub.

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