iTWire | September 27, 2023
Teradata today announced new enhancements to its leading AI/ML (artificial intelligence/machine learning) model management software in ClearScape Analytics (e.g., ModelOps) to meet the growing demand from organisations across the globe for advanced analytics and AI.
These new features – including “no code” capabilities, as well as robust new governance and AI “explainability” controls – enable businesses to accelerate, scale, and optimise AI/ML deployments to quickly generate business value from their AI investments.
Deploying AI models into production is notoriously challenging. A recent O'Reilly's survey on AI adoption in the enterprise found that only 26% of respondents currently have models deployed in production, with many companies stating they have yet to see a return on their AI investments.
This is compounded by the recent excitement around generative AI and the pressure many executives are under to implement it within their organisation, according to a recent survey by IDC, sponsored by Teradata.
ModelOps in ClearScape Analytics makes it easier than ever to operationalise AI investments by addressing many of the key challenges that arise when moving from model development to deployment in production: end-to-end model lifecycle management, automated deployment, governance for trusted AI, and model monitoring.
The governed ModelOps capability is designed to supply the framework to manage, deploy, monitor, and maintain analytic outcomes. It includes capabilities like auditing datasets, code tracking, model approval workflows, monitoring model performance, and alerting when models are not performing well.
We stand on the precipice of a new AI-driven era, which promises to usher in frontiers of creativity, productivity, and innovation. Teradata is uniquely positioned to help businesses take advantage of advanced analytics, AI, and especially generative AI, to solve the most complex challenges and create massive enterprise business value.
Teradata chief product officer Hillary Ashton
“We offer the most complete cloud analytics and data platform for AI. And with our enhanced ModelOps capabilities, we are enabling organisations to cost effectively operationalise and scale trusted AI through robust governance and automated lifecycle management, while encouraging rapid AI innovation via our open and connected ecosystem. Teradata is also the most cost-effective, with proven performance and flexibility to innovate faster, enrich customer experiences, and deliver value.”
New capabilities and enhancements to ModelOps include:
- Bring Your Own Model (BYOM), now with no code capabilities, allows users to deploy their own machine learning models without writing any code, simplifying the deployment journey with automated validation, deployment and monitoring
- Mitigation of regulatory risks with advanced model governance capabilities and robust explainability controls to ensure trusted AI
- Automatic monitoring of model performance and data drift with zero configuration alerts
Teradata customers are already using ModelOps to accelerate time-to-value for their AI investments
A major US healthcare institution uses ModelOps to speed up the deployment process and scale its AI/ML personalisation journey. The institution accelerated its deployment with a 3x increase in productivity to successfully deploy thirty AI/ML models that predict which of its patients are most likely to need an office visit to implement “Personalisation at Scale.”
A major European financial institution leveraged ModelOps to reduce AI model deployment time from five months to one week. The models are deployed at scale and integrated with operational data to deliver business value.
Big Data Management
WebWire | September 29, 2023
SAS, a leader in AI and analytics, is helping to revolutionize the use of clinical trial data so new medicines can be delivered to patients faster than before. After a thorough evaluation, SAS has been chosen by global biopharmaceutical company AstraZeneca to help increase efficiency and drive automation in the delivery of statistical analyses for clinical and post-approval submissions to regulatory authorities, via SAS’s cloud-based software and technologies.
SAS will support the redesign of clinical and patient data flow by delivering industry-leading analytics and AI, manage changing trial designs in a fast-evolving regulatory environment, enable data re-use, and help accelerate reporting and submission timelines. It will also deliver increased capacity, automation, interoperability, and flexibility to bring in and analyze diverse and novel patient data sources – such as those coming from wearables, sensors and precision medicine – as part of the submissions process.
This will be achieved by supporting the analysis and reporting phases with SAS® Life Science Analytics Framework and SAS® Viya®, a scalable and powerful cloud-based industry platform enabling swift decision-making regardless of data volumes or complexity using modern cloud technologies. This has the potential to provide significant productivity gains by driving faster time to market and reduced IT costs. The SAS and AstraZeneca partnership will enable teams across the organization to collaborate and increase clinical research innovation.
Christopher J Miller, VP Biometrics at AstraZeneca, said, This partnership with SAS supports the transformation of how we use clinical data to support our patient-centric approach and focus on getting medicines to patients faster than ever before. It will also allow us to introduce new ways of working and embrace new technologies and trial models to accelerate our portfolio.
Bryan Harris, SAS Executive Vice President and Chief Technology Officer, said, “I’m delighted that SAS is building on the strong relationship it has had with AstraZeneca over many years by being part of this transformation program. The work they do positively impacts the lives of millions of people around the world.
“This is exciting because we have solidified a great foundation between our companies, but we also recognize we are just scratching the surface. We pay attention to technology and the advancements in AI, and we thrive on thinking through how our technology blended with AstraZeneca’s expertise and insight can create new medical solutions for their customers.”
SAS is a global leader in AI and analytics software, including industry-specific solutions. SAS helps organizations transform data into trusted decisions faster by providing knowledge in the moments that matter. SAS gives you THE POWER TO KNOW®.
Big Data Management
Ocient | September 25, 2023
Ocient, a renowned hyperscale data analytics platform, has recently announced the release of its second annual industry report, titled "Beyond Big Data: Hyperscale Takes Flight." The 2023 report, which is the result of a survey conducted among 500 data and IT leaders responsible for managing data workloads exceeding 150 terabytes, sheds light on the escalating significance of hyperscale data management within enterprises. It also underscores the critical requisites of time, talent, and cutting-edge technology necessary for the effective harnessing of data at scale.
Building on the foundation of Ocient's inaugural Beyond Big Data survey, the 2023 report delves into the minds of IT decision-makers, seeking to unravel the challenges, investment priorities, and future prospects occupying the forefront of their agendas for 2023 and beyond. The report offers valuable year-over-year comparisons, drawing on trends identified in 2022 while also presenting fresh, timely insights that mirror the immediate concerns of enterprise leaders in the United States.
Among the pivotal insights featured in this year's report are the following:
Immediate Emphasis on Data Quality: Organizations committed to leveraging their hyperscale data for critical business decisions are placing paramount importance on ensuring the highest data quality standards.
Data Workload Growth as a Driving Force: Data and IT leaders are increasingly recognizing data warehousing and analytics as pivotal elements of their IT strategies, a sentiment vividly reflected in their budget allocations.
AI Readiness at the Forefront: Leaders are eager participants in the AI revolution, yet they grapple with concerns surrounding security, accuracy, and trust.
Innovation Hindered by Talent and Technology Gaps: Many leaders continue to struggle with the challenges of optimizing their toolsets and scaling their teams swiftly enough to meet the demands posed by hyperscale data volumes.
Stephen Catanzano, Senior Analyst, Enterprise Strategy Group, commented:
It's clear enterprises are investing in data analytics and warehousing, especially given their costs are being driven up so high with older systems that can't handle the data that's being pushed to them.
[Source – Business Wire]
Chris Gladwin, Co-Founder and CEO of Ocient, stated that data was not slowing down, and he emphasized that the results of the 2023 Beyond Big Data report confirmed the significance of hyperscale data workloads for enterprises across various industries. He also noted that data volumes were on the rise, as was the importance of comprehending one's data. Nevertheless, the challenges related to data quality, the proliferation of tools, and staffing constraints persisted and were impeding progress in the industry.
Furthermore, Gladwin mentioned that the frontier beyond big data had arrived and that Ocient's annual report illustrated the challenges and opportunities that were shaping the enterprise data strategies of the future.
Ocient is a pioneering hyperscale data analytics solutions company dedicated to empowering organizations to unlock substantial value through the analysis of trillions of data records, achieving performance levels and cost efficiencies previously deemed unattainable. The company is entrusted by leading organizations around the globe to leverage the expertise of its industry professionals in crafting and implementing sophisticated solutions. These solutions not only enable the rapid exploration of new revenue avenues but also streamline operational processes and enhance security measures, all while managing five to 10 times more data and significantly reducing storage requirements by up to 80%.