CognitiveScale’s Platform Drives a Robust, Repeatable, and Enterprise Grade AI Engineering Approach to AI Solution development.
What is AI Engineering?
AI Engineering is a discipline for harnessing the people, systems and tools that it takes to deliver high-value, scalable, and trusted artificial intelligence (AI) for use in real-world contexts. Carnegie Mellon’s Software Engineering Institute considers the pillars of AI Engineering to be:
- Robust and Secure AI: AI application development, deployment, monitoring, maintenance, feedback, learning & value capture
- Scalable AI: Scaling AI applications across the enterprise for many use cases requires AI infrastructure, data, and models that can be reused across problem domains and deployments
- Human-centered AI: How AI systems are designed to align with humans, their behaviors, and their values – and built by teams across the enterprise (Data Science, ML Ops, Data Engineering, Subject Matter Experts, etc.)
Gap Between AI Technologies and Outcomes
There is a large gap between AI technology – the data science, data engineering, and machine learning (ML) software and infrastructure – and business outcomes – the value derived from AI-powered applications and decision intelligence. In order to realize value from AI applications, development teams must address several challenges:
- Access and availability to high quality data that is spread across the enterprise in data silos
- Coordination and collaboration across multiple development teams
- Contextual understanding of key entities (e.g. customers, products) to drive hyper-personalized insights and next best actions
- Optimizing AI applications to meet the desired business outcome at a system level rather than a model level
- Trust and governance across the lifecycle of AI development and deployment in order to mitigate the risk of bias, explainability, accuracy, and more
Enterprise AI is a team sport that requires various roles and functions to collaborate to deliver resilient, repeatable AI-powered solutions at scale. Data Scientists, Data Engineers, Application Developers, DevOps, DataOps, MLOps, Business SMEs, and Audit and Compliance teams need to collaborate to deliver scalable AI Solutions. Each of these collaborators use different tools and applications to play their part in application development.
Enterprises have invested in a variety of data, data science, application development, and cloud infrastructure tools and technologies over the years, yet are not getting the value they expect. AI application development is often focused on Data Science and ML Ops with a reliance on manually “wiring” AI into solutions for point-to-point isolated successes. AI Applications rarely leverage reusable assets, are often not easily repeatable, and are difficult and expensive to maintain.
AI application development teams are finding that development is taking longer than originally projected (6-8 months development time for applications, on average), requiring an increasing amount of time from high-level, high-cost developers and data scientists. These projects and applications are not realizing their original goals and financial returns, and AI Roadmaps are taking longer to deliver with fewer use cases and potential applications than originally planned. Oftentimes the business has moved to new initiatives by the time an application is ready for production. Simplifying the creation, orchestration, management, and validation of AI applications is imperative.
CognitiveScale’s AI Platform
CognitiveScale’s AI platform helps organizations industrialize AI Engineering principles to develop valuable, trusted AI applications that inject intelligence into key business processes and drive hyper-personalized insights that transform customer journeys. CognitiveScale empowers organizations to build, operationalize, and industrialize AI applications across the enterprise to drive trusted decision intelligence.
CognitiveScale’s platform accelerates the AI Application development life cycle by allowing enterprises to: connect and curate data and models that leverage existing data and data science investments; orchestrate the components and compose AI Applications from models, algorithms and rules engines (or a combination of multiple of these); drive personalized insights; learn via feedback loops that improve model and application performance; and, govern AI applications to ensure trusted, responsible use.
Data is the fuel for AI applications – not just the data that it takes to run models and algorithms, but the additional data required to run and improve AI applications, e.g. observed clickstream and activity data, and personalized insights based on model output, algorithms or rules engines. Cortex enables multiple connection types as well as curation of data per the requirements of models and decisioning systems. Our AI software platform and Profile of One technology can facilitate the aggregation and preparation of data across 140+ existing data connectors and enable easy creation of additional data connections via APIs, custom connections and more.
AI applications consist of models, algorithms, rules engines, data connectors, skills and more. CogtiveScale’s platform is uniquely capable of orchestrating components in order to accelerate the time to production and value. Monitoring and maintenance is then required to operationalize AI applications and optimize their performance and value.
A central component of CognitiveScale’s AI platform is what we call the Profile of One, a key capability that enables hyper-personalization. Profile of One collects declared and observed data from systems of record and systems of engagement, and uses this data to derive inferences and next best actions. This enables hyper-personalization at scale and provides a foundation for adding additional insights such as model output (risk scores, predictions, etc.).
CognitiveScale’s platform enables model and application performance improvement via feedback and learning capabilities. Feedback loops and learning improve model and application performance and can help to improve the overall value delivered by AI-powered applications.
CognitiveScale’s platform includes robust capabilities that ensure the creation of trusted, responsible AI Applications to meet the needs of compliance and risk management personnel in two key areas:
- Technical assessment of AI models and applications: Model assessment capabilities that evaluate and score models and applications for performance, bias, explainability, and more.
- Governance policies and systems: Ensure adherence to the policies, standards, accuracy requirements, bias and explainability thresholds, etc. as required by compliance officers, risk management, and regulators.
Conclusion: the Value of AI Engineering
Gartner estimates that organizations that establish AI Engineering best practices by 2025 will generate at least three times more value with their AI efforts than those that do not. An AI Engineering discipline will enable organizations to reduce the costs and complexity associated with one-off customized solutions (move from M/L Ops to AI Engineering). CognitiveScale’s Enterprise AI platform is accelerating speed to production of AI Applications which, in turn, enables more AI use cases and a more robust AI roadmap. CognitiveScale has demonstrated an approximately 35-40% reduction in time to value and associated costs.
Get in touch with CognitiveScale to learn more about how our AI platform can power your AI Engineering capability and enable your organization to deliver your most ambitious AI-powered initiatives with speed and at scale.