Vyasa software provides a novel AI-powered platform for organizations to integrate and analyze content across their entire enterprise data landscape. Recently acquired by Certara, Vyasa deep learning software enables healthcare and life science professionals to gain new value from their data. Its highly-scalable data fabric allows users to connect disparate data sources regardless of file type or storage location. Low-code applications built on top of the data fabric allow healthcare and life science teams to leverage deep learning to accelerate tasks such as early-stage research, clinical trial design, small-compound analytics, patient cohort curation, and more.

Address dark data with Vyasa, NVIDIA and World Wide Technology

Industry analysts report that 97% of enterprises use at least one type of cloud deployment, with the majority deploying hybrid or multi-cloud environments. 

While these environments deliver new flexibility to organizations, they can also deepen dark data issues which currently impact as much as 80% of organizational data. Dark data refers to data and workflows stored across multiple silos, but also the variety of formats the data is stored in such as spreadsheets, written documents, reports, PDFs, images, and more. 

As today’s data landscapes become increasingly diverse and distributed, how can organizations future-proof their environments to maintain productivity and collaboration? 

Intelligent data fabrics for deep learning

When deploying new AI solutions, organizations have traditionally developed custom environments requiring massive amounts of data for their workflows to operate. As a result, teams are burdened with managing multiple silos that fail to give organizations the agility and transparency they’re seeking in their modernization efforts. 

Vyasa has developed a novel approach to address this challenge. Known as an intelligent data fabric, Vyasa Layar enables organizations to apply deep learning across large sets of data regardless of storage location or file structure. As a result, customers can integrate and run analytics across their entire data landscape within a single platform. 

Vyasa Layar is delivered with pre-built deep learning models, and customers have the flexibility to integrate their own models into Layar to run custom workflows against their data fabric, leveraging a suite of low code application interfaces.

Certara’s Vyasa AI-powered platform accelerates life sciences research

Life sciences, pharmaceutical and other healthcare research firms can use the Vyasa AI software platform to accelerate research and increase speed-to-market. Specific benefits of Vyasa data fabric and deep learning capabilities allow organizations to: 

• Connect disparate cloud and on-premise data sources into a single platform without moving or replicating data.
• Identify relationships within data via dynamic knowledge graphs.
• Turn unstructured content into structured insights through smart spreadsheets.
• Track events and anomalies occurring within your data fabric with real-time dashboards.

Organizations leverage Vyasa to accelerate early-stage life science research, enhance clinical trial design & enrollment, conduct competitor intelligence, improve analyses around customer touchpoints, and gain a better understanding of patient and customer populations.

AI platform deployment considerations

Deploy where you want it

As a fully containerized solution, Vyasa Layar can be deployed on premises, as well as in public or private cloud environments. This provides a dynamic solution for organizations operating across multiple environments. 

Initial installation requirements include:

• RAM: 256GB
• CPU: 16+ cores
• GPU: 2x NVIDIA V100 32GB generation or later
• Disk: 1TB SSD

Want the advantages of the cloud while maintaining the data control and security of an on-premise environment? Vyasa’s architecture allows you to deploy an intelligent data fabric across cloud adjacent infrastructure.

Enterprise support and validated performance with NVIDIA VMI

NVIDIA Virtual Machine Image, or VMI, a part of NVIDIA AI Enterprise, dramatically cuts down deployment time by automatically installing Kubernetes, the latest GPU drivers, and other required software. It’s designed for hybrid and multi-cloud environments and enables customers to use the latest NVIDIA software and application frameworks for the development and deployment of AI, all of which is fully supported by NVIDIA through the NVIDIA AI Enterprise offering.

Vyasa Layar is fully validated for deployment via NVIDIA VMI, providing a seamless and accelerated experience for deployment in a self-managed Kubernetes cluster in your preferred cloud. 

Flexible AI for healthcare & life science analytics 

Vyasa, NVIDIA and World Wide Technology provide a flexible solution for healthcare and life science organizations to apply advanced AI analytics across their data. With an intelligent data fabric, organizations can enhance and accelerate

• Early-stage research
• Clinical trial analysis & recruitment
• Drug target discovery
• De novo compound generation
• Image analysis
• Genomic analysis
• Competitor & market intelligence

Evaluate and test data fabrics

Interested in testing Vyasa? Access the Vyasa data fabric lab within the Advanced Technology Center (ATC).

WWT, NVIDIA and Vyasa encourage anyone looking to evaluate this solution to leverage the environments the team has built and tested within AWS and within the Advanced Technology Center. Create an account on wwt.com to access both the ATC physical environment and the AWS instance via a web browser of their choice from anywhere in the world. 

Your organization can also work with the WWT, NVIDIA and Vyasa teams to customize the solution before validating a custom configuration that is right for them or they can simply be granted access to a solution ready to be validated.

This article was originally published on the World Wide Technology blog.

Today’s data landscapes are more dispersed and diverse than ever before. For example, 97% of enterprises use at least one type of cloud deployment, with the majority deploying hybrid or multi-cloud environments. Further, industry analysts estimate that 80-90% of the new data being stored in these environments is unstructured. 

This scenario creates a number of complexities for organizations looking to harness this valuable data. First, this data needs to be identified and collected across multiple storage locations and once this process is complete the work has just begun. Then teams have to sift through and prepare the data – a process that can be a significant burden when considering the complexities of these data types such as:

• Published research
• Market analyses
• Quarterly reports
• Lab notes
• Emails 
• Images
• Presentations
• PDFs

Traditionally, analyzing this data requires manually searching through and extracting relevant data points – a task that can take days, weeks or months to complete depending on the size of the desired data set. For example, life science researchers undergoing systematic literature reviews anticipate each review to take months to complete, with each project costing roughly $140,000. 

A New Way Forward with Deep Learning 

Recent advancements in deep learning, known as large language models (LLMs), have presented a paradigm shift in how organizations can access, analyze and harness new value from this data. 

Unlike a traditional model that processes each term separately and outside the context of the sequence, LLMs use self-attention to build rich representations of each constituent in the data span, allowing these models to understand the relevance of the location of a term, the relation of one term to the next (even if far away from each other) and more. When trained on larger datasets, these models reach remarkable accuracy and recall for understanding unstructured data like large documents of natural language text.

LLMs are being applied to a variety of use cases to help address unstructured data challenges. For example, Vyasa’s Synapse smart spreadsheet technology applies LLMs to sets of unstructured documents and allows organizations to autonomously extract key data points into structured spreadsheets.

At Vyasa, we’ve helped life science consultancies leverage these models to improve productivity for systematic literature reviews, pharmaceutical companies improve early research, medical research centers enhance clinical trial enrollment, healthcare providers reduce time spent reporting and organizations improve call center transcript analysis. 

Watch our video below with SAP to learn about how we’re improving unstructured text analysis for a joint customer.

The healthcare industry’s expectations around deep learning and A.I. have become a reality as organizations undergo digital transformation and regain control of their data.

According to Gartner, healthcare technology leaders are prioritizing data integration, NLP and data annotation in their AI investments.

However, industry analysts also note that managing & preparing data remains the top time investment for deploying AI.

In this installment of our healthcare fireside chats, Steve Lazer, Global Healthcare & Life Sciences CTO at Dell Technologies joins us to discuss the evolution of AI in healthcare and where we’re seeing implementation in the market today.

Watch more below:

Organizations only access 20% of the data they have available to them. The other 80% is difficult to access due to silos, unstructured formats and redundant or obsolete content.

This scenario creates immense challenges for organizations looking to deploy A.I. In fact, IDC’s AI InfrastructureView Survey reports that managing & preparing data remains the top time investment for deploying A.I.

Experts from Vyasa, Dell Technologies & NVIDIA discussed how Vyasa’s Layar intelligent data fabric helps solve this challenge during a recent webinar.

Watch below to learn more about how data fabrics are driving healthcare analytics.

According to IDC’s AI InfrastructureView Survey, only one-third of companies surveyed claim to have reached mature AI adoption. The survey notes that infrastructure remains the most consequential, but least mature decision when deploying AI. 

In most cases, companies build custom environments for deploying AI, all of which require massive amounts of data for their workflows to operate. Unfortunately, this scenario poses challenges by deepening data silos and creating redundant or obsolete data as AI deployments become mature. Over time, this negatively impacts the quality of data teams have access to as well as the productivity and enhanced intelligence companies are looking to achieve through these projects.

A Paradigm Shift in Data Analytics

Innovations in data management and deep learning are addressing this issue and presenting a paradigm shift in how companies access and apply analytics to their data. Known as a data fabric, companies can now overcome silos by making data accessible and searchable in a single platform. 

At Vyasa, we’ve combined this approach with advanced deep learning to create our Layar intelligent data fabric. With Layar, companies can connect to disparate data sources regardless of storage location. Layar then applies transformer-based deep learning to the connected data sources, making the content easily searchable and accessible all in natural language. This enables Layar to expand its understanding of both structured and unstructured data, allowing companies to gain new insight and value across their data landscape. 

As a result, companies can scale AI deployments by applying deep learning models across silos via the Layar data fabric.

Accelerated Analytics with Vyasa, Oracle and NVIDIA

Innovative cloud infrastructure from Oracle leveraging NVIDIA accelerated computing provides a high-performance environment for deploying a Layar data fabric.

As an Independent Software Vendor partner in the NVIDIA Partner Network (NPN)  ISV Partner, Vyasa accelerates these capabilities with the NVIDIA AI Enterprise software suite, which includes essential AI workflow processing engines from NVIDIA, including transformer-based deep learning models like NeMo Megatron, NVIDIA Triton Inference Server, NVIDIA RAPIDS data science libraries and more.

Deep Learning Hits its Stride in Healthcare 

Healthcare technology leaders are prioritizing data integration, NLP and data annotation in their AI investments, according to recent reports from Gartner. The Layar data fabric provides a suite of low-code application interfaces that meets many of these priorities, enabling healthcare and life science teams to:

• Accelerate early research and systematic literature review
• Enhance clinical trial assessment and enrollment
• Complement small compound analysis 
• Improve target discovery
• Better analyze genomics content
• Streamline analyses of biomedical images

With Layar, users have experienced a 97% improvement in query accuracy for clinical trial protocol assessment and a 25% increase in productivity when compared to manual literature review.

Vyasa, Oracle and NVIDIA at SC22 

Interested in learning more about Vyasa’s deep learning analytics for healthcare & life science? Visit our demo in the Oracle & NVIDIA booth #2406 at SC22 from November 14-17. 

Access a free trial of Vyasa here. 

New application interface provides advanced visualization for identifying trends and anomalies across your data 

BOSTON, September 27, 2022 — Vyasa, an innovative provider of highly scalable deep learning A.I. analytics software for healthcare, life sciences and business applications, today introduces its latest application interface, Signal. Featuring an intuitive design, Signal enables users to monitor trends and identify anomalies in their Layar data fabric through highly-visual charts and graphs, delivered in a single dashboard.  

According to industry analysts, between 80-90% of organizational data created today is unstructured. While rich in insight, the complexity of this data makes it increasingly difficult to analyze efficiently, requiring many to rely on manual processes to find the information they need. Vyasa leverages proprietary deep learning models to accelerate this process, allowing users to search and identify insights from their data via its Layar intelligent data fabric. Signal is the latest application interface developed by Vyasa to make this data easy to analyze in low code. 

Users of Signal are able to:  

• Visualize term usage and frequency in your data via line, bar and pie charts. 
• Monitor terms and concepts of interest across multiple cloud and on-premise data sources without having to move or replicate files. 
• Discover common concepts that relate to your terms of interest. 
• Identify emerging terms and trends with named entity recognition. 
• Receive real-time alerts to events occurring in your data. 

• Filter charts and graphs to receive dynamic results for comparisons and reporting. 
• Query across multiple data sources and file types. 

“Today’s organizations face significant data accessibility challenges – from content being stored across multiple silos to managing various structured and unstructured file formats to outdated processes for collecting and analyzing data that are time and resource intensive,” said Vyasa Founder & CEO, Dr. Christopher Bouton. “At Vyasa we’re developing cutting-edge deep learning technologies to address this issue and Signal is the latest example of that. Now users can quickly access the insights they need in charts and graphs which are familiar and easy to analyze without having to worry about finding the data or having the technical acumen to develop a code or query to collect the information they’re seeking.” 

Signal is being used across a variety of industries to help users to monitor trends, identify needs in the market, discover emerging technologies, streamline competitor intelligence and more. It joins Vyasa’s suite of deep learning applications powered by its Layar data fabric, including Axon dynamic knowledge graph capability, Cortex data fabric creation and management, Synapse “smart table” technology, and Retina image management and analysis. Vyasa can be deployed on-premise or in the cloud as a fully containerized application. 

Access a free trial of Vyasa’s suite of deep learning applications at https://vyasa.com/download/.  
For more information, please visit https://vyasa.com/ or contact [email protected]   

About Vyasa: 

Founded in 2016, Vyasa Analytics provides highly-scalable deep learning software and analytics for healthcare, life sciences and business applications. Vyasa’s technologies enable organizations to integrate and access data across disparate silos, regardless of location or structure. With Vyasa’s collection of deep learning applications, users can ask complex questions across large-scale data sets and gain critical insights to make faster, more accurate, business decisions. For more information, please visit vyasa.com  

According to industry research, 85% of clinical trials end with less than expected enrollment, while 19% are terminated or fail to meet accrual goals at all. Considering the millions of dollars and significant time invested into these trials, the ability to identify and enroll viable candidates is key to successful trial completion and driving innovation around cutting-edge therapeutics. 

Clinical Trials’ Data Problem

One of the major challenges associated with clinical trial enrollment is the vast amounts of data required to identify relevant candidates. This data is stored across multiple silos and formats making it difficult to quickly access and analyze. In fact, over half of eligibility criteria for clinical trials is unstructured, and that’s not considering the other critical data points that help influence candidate identification such as patient records, clinician notes, genomics files, images and more. 

Traditionally, trial sponsors have relied on manual processes to identify appropriate candidates – searching through massive databases and multiple resources before gleaning out potentially relevant participants. These processes are time and labor-intensive; in many cases, only a fraction of potential leads are found. 

Tackling Your Data with Deep Learning

Deep learning is revolutionizing how the healthcare and life sciences industries can search and access data. Known as transformer-based deep learning, these advanced models use a novel approach called self-attention to train on large sets of data. Unlike traditional deep learning models that process each term separately and without context, self-attention allows transformers to build rich representations of the data to understand the relevance of the location of a term, the relation of one term to the next (even if far away from each other) and more. When trained on larger datasets, these models reach remarkable accuracy and recall for understanding the context within unstructured data like large documents of natural language text.

At Vyasa, we leverage this novel approach to deep learning as part of our Layar intelligent data fabric. By applying deep learning to disparate data sources, Layar is able to make the contents of those sources searchable and accessible in a single platform, or data fabric. We then take this a step further by providing a suite of low code application interfaces to help users simultaneously explore and gain access to key insights in their data regardless of its format or location it’s stored in. 

Successful Candidate Identification with Vyasa  

While data fabrics provide benefits across healthcare and life science, they hold significant power when applied to research tasks including clinical trial design and candidate recruitment. 

For example, healthcare organizations are turning to Vyasa to enhance their understanding of their patient population and improve clinical trial enrollment as a whole. By connecting their data silos into a single data fabric they can have all of the relevant information they need in a single platform without having to collect, move or replicate data. The addition of deep learning on top of this data enables them to uncover new insights with peak accuracy and speed. As a result, these organizations can leverage Vyasa Layar to create a unified data platform to search and identify relationships between clinical trials and their patient population to improve trial enrollment and viability. 

Working with Dell Technologies, we can provide this solution on innovative compute and storage hardware, allowing organizations to access and operate these capabilities in a familiar server environment.

Smarter Trial Analysis – HETT 2022

Vyasa will be presenting our Layar intelligent data fabric and clinical trial analysis capabilities at Dell Technologies stand #E30 from September 27-28 at HETT 2022. 

Interested in meeting? Contact us here. 

Access a 7-day free trial of Vyasa Layar here. 

The industry estimates that nearly 80% of organizational data is unstructured.

While rich in insight, unstructured data is incredibly challenging to search and analyze due to the complexity of content such as scientific research, scanned documents, clinician notes, pathology reports and more.

Fortunately, advancements in deep learning are creating a paradigm shift in how we can approach this data through transformer-based models.

Unlike a traditional deep learning model that processes each term separately and outside the context of the sequence, transformers use self-attention to build rich representations of each constituent in the data span, allowing these models to understand the relevance of the location of a term, the relation of one term to the next (even if far away from each other) and more. When trained on larger datasets, these models reach remarkable accuracy and recall for understanding unstructured data like large documents of natural language text.

In the next installation of our Healthcare Fireside Chats, we’re joined by Dell Technologies‘ Global & Federal Healthcare CTO, Michael Giannopoulos. We’ll discuss the rise of unstructured content in healthcare and how Vyasa is applying transformers to improve how the industry can access and gain insight from this valuable data.

Watch more below:

On average, hospitals create up to 50 petabytes of data on an annual basis. Unfortunately, as much as 97% of this information goes unused.

In our new series with Dell Technologies, we’ll unpack the biggest data challenges facing the healthcare industry – from data silos to research to understanding patient need to magnet reporting.

In our first episode, we’re joined by Kevin Crosby, Dell Technologies’ healthcare field director where we discuss the impact of data silos in healthcare and the innovations addressing this issue.

Watch more below.

Industry estimates are that nearly 80% of organizational data is dark and siloed. Vyasa helps organizations overcome this challenge with its intelligent data fabric, Layar. With Layar, organizations can logically connect disparate data silos without moving or replicating files. Unified data can then be queried through a suite of low code interfaces which accelerate data exploration and insight discovery with the power of deep learning.