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.
Business Wire | October 23, 2023
NICE (Nasdaq: NICE) today announced that NEVA Discover, its process analytics and task mining offering, has been recognized as a ‘Leader’ in Everest Group's Task Mining Products PEAK Matrix Assessment 2023 out of 19 task mining providers evaluated. NEVA Discover utilizes its rich desktop data collection and desktop analytics to pinpoint areas for employee improvement and optimize performance. Leaders were recognized for strong growth momentum in the task mining market and the ability to continue to differentiate by offering innovative features.
NEVA Discover offers a scientific approach to scaling task mining capabilities, allowing CX organizations to take employees’ performances to a new level and ground business decisions on rich processes and interaction data. Utilizing NEVA Discover’s desktop analytics, organizations can drive employee performance improvements by developing and engaging employees with proactive, personalized coaching using actionable data. The report noted that NEVA Discover “helps users to discover best practices by combining the captured data with additional metrics, such as interaction data, employee data, and interaction outcomes, leveraging the power of the CXone Platform.” The report also noted that clients appreciated NEVA Discover’s ease of use as well as key areas of strength including the power of its task discovery and handling capabilities as well as quick adaptation to the product.
“NICE has reinforced its position as a Leader on Everest Group’s Task Mining Products PEAK Matrix 2023, underpinned by its strong vision, depth and breadth of product functionalities, focus on product support, and integration with its automation capabilities,” said Amardeep Modi, Vice President at Everest Group. “Discovery capabilities, ease of maintenance, and data security are some of the key strengths indicated by its clients.”
Barry Cooper, President, CX Division, NICE, said, We are pleased to be recognized as a Leader in this assessment, demonstrating NICE’s excellence in Task Mining. As CX organizations continue to struggle with employee retention, NICE’s NEVA Discover enables organizations to provide objective, targeted coaching to empower employees and make them even more effective. With our ongoing investments in Enlighten AI, NICE will continue its momentum as a market leader in Task Mining.
This recognition adds to NICE’s past accomplishments in this space. NICE was recognized as a ‘Leader’ in Everest Group's Task Mining Products PEAK Matrix Assessment 2022. NICE was also named a ‘Leader’ in Everest Group’s Robotic Process Automation (RPA) PEAK Matrix Assessment 2022.
Salesforce | September 14, 2023
Salesforce introduces the groundbreaking Einstein 1 Platform, built on a robust metadata framework.
The Einstein 1 Data Cloud supports large-scale data and high-speed automation, unifying customer data, enterprise content, and more.
The latest iteration of Einstein includes Einstein Copilot and Einstein Copilot Studio.
On September 12, 2023, Salesforce unveiled the Einstein 1 Platform, introducing significant enhancements to the Salesforce Data Cloud and Einstein AI capabilities. The platform is built on Salesforce's underlying metadata framework. Einstein 1 is a reliable AI platform for customer-centric companies that empowers organizations to securely connect diverse datasets, enabling the creation of AI-driven applications using low-code development and the delivery of entirely novel CRM experiences.
Salesforce's original metadata framework plays a crucial role in helping companies organize and comprehend data across various Salesforce applications. This is like establishing a common language to facilitate communication among different applications built on the core platform. It then maps data from disparate systems to the Salesforce metadata framework, thus creating a unified view of enterprise data. This approach allows organizations to tailor user experiences and leverage data for various purposes using low-code platform services, including Einstein for AI predictions and content generation, Flow for automation, and Lightning for user interfaces. Importantly, these customizations are readily accessible to other core applications within the organization, eliminating the need for costly and fragile integration code.
In today's business landscape, customer data is exceedingly fragmented. On average, companies employ a staggering 1,061 different applications, yet only 29% of them are integrated. The complexity of enterprise data systems has increased, and previous computing revolutions, such as cloud computing, social media, and mobile technologies, have generated isolated pockets of customer data.
Furthermore, Salesforce ensures automatic upgrades three times a year, with the metadata framework safeguarding integrations, customizations, and security models from disruptions. This enables organizations to seamlessly incorporate, expand, and evolve their use of Salesforce as the platform evolves.
The Einstein 1 Data Cloud, which supports large-scale data and high-speed automation, paves the way for a new era of data-driven AI applications. This real-time hyperscale data engine combines and harmonizes customer data, enterprise content, telemetry data, Slack conversations, and other structured and unstructured data, culminating in a unified customer view. Currently, the platform is already processing a staggering 30 trillion transactions per month and connecting and unifying 100 billion records daily. The Data Cloud is now natively integrated with the Einstein 1 Platform, and this integration unlocks previously isolated data sources, enabling the creation of comprehensive customer profiles and the delivery of entirely fresh CRM experiences.
The Einstein 1 Platform has been expanded to support thousands of metadata-enabled objects per customer, each able to manage trillions of rows. Furthermore, Marketing Cloud and Commerce Cloud, which joined Salesforce's Customer 360 portfolio through acquisitions, have been reengineered onto the Einstein 1 Platform.
Now, massive volumes of data from external systems can be seamlessly integrated into the platform and transformed into actionable Salesforce objects. Automation at scale is achieved by triggering flows in response to changes in any object, even events from IoT devices or AI predictions, at a rate of up to 20,000 events per second. These flows can interact with any enterprise system, including legacy systems, through MuleSoft.
Analytics also benefit from this scalability, as Salesforce provides a range of insights and analytics solutions, including reports and dashboards, Tableau, CRM analytics, and Marketing Cloud reports. With the Einstein 1 Platform's common metadata schema and access model, these solutions can operate on the same data at scale, delivering valuable insights for various use cases.
Salesforce has additionally made Data Cloud accessible at no cost to every customer with Enterprise Edition or higher. This allows customers to commence data ingestion, harmonization, and exploration, leveraging Data Cloud and Tableau to extend the influence of their data across all business segments and kickstart their AI journey.
Salesforce's latest iteration of Einstein introduces a conversational AI assistant to every CRM application and customer experience. This includes:
Einstein Copilot: This is an out-of-the-box conversational AI assistant integrated into every Salesforce application's user experience. Einstein Copilot enhances productivity by assisting users within their workflow, enabling natural language inquiries, and providing pertinent, trustworthy responses grounded in proprietary company data from the Data Cloud. Furthermore, Einstein Copilot proactively takes action and offers additional options beyond the user's query.
Einstein Copilot Studio: This feature enables companies to create a new generation of AI-powered apps with custom prompts, skills, and AI models. This can help accelerate sales processes, streamline customer service, auto-generate websites based on personalized browsing history, or transform natural language prompts into code. Einstein Copilot Studio offers configurability to make Einstein Copilot available across consumer-facing channels such as websites and messaging platforms like Slack, WhatsApp, or SMS.
Both Einstein Copilot and Einstein Copilot Studio operate within the secure Einstein Trust Layer, an AI architecture seamlessly integrated into the Einstein 1 Platform. This architecture ensures that teams can leverage generative AI while maintaining stringent data privacy and security standards.
The metadata framework within the Einstein 1 Platform expedites AI adoption by providing a flexible, dynamic, and context-rich environment for machine learning algorithms. Metadata describes the structure, relationships, and behaviors of data within the system, allowing AI models to better grasp the context of customer interactions, business processes, and interaction outcomes. This understanding enables fine-tuning of large language models over time, delivering continually improved results.