Big Data
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.
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Data Architecture
IQVIA | September 18, 2023
Snowflake recognizes IQVIA as a Global Healthcare Leader in the Measurement and Attribution category as part of its annual Modern Marketing Data Stack awards.
The Modern Marketing Data Stack report comprehensively analyzes data tools, applications, technologies, and processes in marketing data stacks.
Orchestrated Analytics GM Tanveer Nasir expressed his gratitude for the recognition and emphasized the company's commitment to improving brand performance and patient lives through data-driven insights and solutions.
Snowflake, a leading data cloud platform, has recognized IQVIA as a Global Healthcare Leader in the prestigious Measurement and Attribution category. This recognition comes as part of Snowflake's annual Modern Marketing Data Stack awards.
The Modern Marketing Data Stack report is the outcome of a comprehensive year-long analysis focusing on data tools, applications, technologies, and processes employed by organizations in their marketing data stacks. This exhaustive assessment, encompassing approximately 8,100 Snowflake customers, employs a weighted scoring algorithm to discern "marketplace leaders" across diverse data-driven business functions and technology categories.
The report underscores IQVIA's proficiency in aiding healthcare and life sciences organizations in the compliant utilization of extensive data resources. This enables swift and precise measurement and reporting, ultimately leading to actionable insights that facilitate informed decision-making and the formulation of effective sales and marketing strategies.
In recent years, life sciences firms have significantly increased their investments in business intelligence (BI) solutions to enhance their competitiveness and performance. However, this growth has also brought forth challenges, such as analytics failing to address essential business questions, the absence of a "single source of truth" for dependable insights, and the inability to prioritize personalized prescriptive insights.
IQVIA's Orchestrated Analytics platform has emerged as a preeminent solution in the industry due to its comprehensive consulting and change management approach. This approach guarantees that solutions align with specific business requirements, irrespective of the market while minimizing initial investment risks. Furthermore, the platform offers an array of self-service applications empowering business stakeholders to customize insights and extract reliable and actionable intelligence.
An exceptional feature of IQVIA's Orchestrated Analytics is its extensive library of algorithms, featuring over 200 algorithms and a multitude of Key Performance Indicators (KPIs) exceeding 400, all aimed at elevating commercial impact through personalized insights for each user. The platform's user-friendly interface is complemented by embedded smart assistants, ensuring effortless access to personalized intelligence across a spectrum of business intelligence tools.
IQVIA's global presence is another hallmark, with a team of over 86,000 experts operating in more than 100 countries. This expansive network accelerates the commercial impact of life sciences companies by furnishing market-relevant insights. In addition, Orchestrated Analytics is entrusted by seven out of the top ten pharmaceutical companies worldwide as they expand their brand portfolios.
Tanveer Nasir, General Manager, Orchestrated Analytics, commented
We are honored to be selected by Snowflake as the global leader in Measurement and Attribution in their Modern Marketing Data Stack report.
[Source: IQVIA]
He further explains that their insight recommendations and user adoption framework worked together effectively to enhance the sales force's efficiency and increase productivity. They had exhibited a significant ROI just by demonstrating Rx uplift for a top 10 pharmaceutical brand. Nasir conveyed that their commitment to enhancing brand performance and improving patients' lives worldwide by identifying the right customer at the right time through the correct channel and messaging continues to drive their passion.
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Big Data Management
IBM | September 08, 2023
IBM announces plans to enhance its Watsonx AI and data platform, with a focus on scaling AI impact for enterprises.
Key improvements include new generative AI models, integration of foundation models, and features like Tuning Studio and Synthetic Data Generator.
IBM emphasizes trust, transparency, and governance in training and plans to incorporate AI into its hybrid cloud solutions, although implementation difficulty and cost may be issues.
IBM reveals its plans to introduce new generative AI foundation models and enhancements to its Watsonx AI and data platform. The goal is to provide enterprises with the tools they need to scale and accelerate the impact of AI in their operations. These improvements include a technical preview for watsonx.governance, the addition of new generative AI data services to watsonx.data, and the integration of watsonx.ai foundation models into select software and infrastructure products.
Developers will have the opportunity to explore these capabilities and models at the IBM TechXchange Conference, scheduled to take place from September 11 to 14 in Las Vegas.
The upcoming AI models and features include:
1. Granite Series Models: IBM plans to launch its Granite series models, utilizing the ‘Decoder’ architecture, is essential for large language models (LLMs). These models will support various enterprise natural language processing (NLP) tasks, including summarization, content generation, and insight extraction, with planned availability in Q3 2023.
2. Third-Party Models: IBM is currently offering Meta's Llama 2-chat 70 billion parameter model and the StarCoder LLM for code generation within watsonx.ai on IBM Cloud.
IBM places a strong emphasis on trust and transparency in its training process for foundation models. They follow rigorous data collection procedures and include control points to ensure responsible deployments in terms of governance, risk assessment, privacy, bias mitigation, and compliance.
IBM also intends to introduce new features across the watsonx platform:
For Watsonx.ai:
Tuning Studio: IBM plans to release the Tuning Studio, featuring prompt tuning, allowing clients to adapt foundation models to their specific enterprise data and tasks. This is expected to be available in 3Q23.
Synthetic Data Generator: IBM has launched a synthetic data generator, enabling users to create artificial tabular data sets for AI model training, reducing risk and accelerating decision-making.
For Watsonx.data:
Generative AI: IBM aims to incorporate generative AI capabilities into watsonx.data to help users discover, augment, visualize, and refine data for AI through a self-service, natural language interface. This feature is planned for technical preview in 4Q 2023.
Vector Database Capability: IBM plans to integrate vector database capabilities into watsonx.data to support watsonx.ai retrieval and augmented generation use cases, also expected in the technical preview in 4Q 2023.
For Watsonx.governance:
Model Risk Governance for Generative AI: IBM is launching a tech preview for watsonx.governance, providing automated collection and documentation of foundation model details and model risk governance capabilities.
Dinesh Nirmal, Senior Vice President, Products, IBM Software, stated that IBM is dedicated to supporting clients throughout the AI lifecycle, from establishing foundational data strategies to model tuning and governance. Additionally, IBM will offer AI assistants to help clients scale AI's impact across various enterprise use cases, such as application modernization, customer care, and HR and talent management.
IBM also intends to integrate watsonx.ai innovations into its hybrid cloud software and infrastructure products, including intelligent IT automation and developer services. IBM's upgrades to the Watsonx AI and data platform offer promise but, come with potential drawbacks. Implementation complexity and the need for additional training may create a steep learning curve. The associated costs of advanced technology could be prohibitive for smaller organizations.
The introduction of generative AI and synthetic data raises data privacy and security concerns. Additionally, despite efforts for responsible AI, the risk of bias in models necessitates ongoing vigilance to avoid legal and ethical issues.
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