AI Engineering: Playbook for Scalable Enterprise AI
What is AI Engineering?
AI Engineering is the playbook to streamline the AI development lifecycle to drive greater value, faster, and at scale. It enables seamless, end-to-end orchestration within your ecosystem of stakeholders to maximize value derived from your AI Applications.
What are the Challenges to Scale AI within your organization?
There is friction across the AI Application Development lifecycle - from data engineering, data science, and ML Ops to Software Development, Architects, Subject Matter Experts, Business Managers, and even auditors - when it comes to delivering robust AI applications. Integrating data and models, development of an AI system that leverages multiple models and business logic, deployment of the AI system across the enterprise, and the continuous optimization of the AI system to achieve business goals with trust and transparency, requires coordination and collaboration across all stakeholders, the absence of which causes one-off, point to point solutions that are not repeatable.
The AI Engineering Solution
Data Challenges: How do you integrate disparate data across the enterprise - internal systems of record and data lakes and external data sources to support batch, real-time, and streaming requirements?
AI Application Composition: How do you orchestrate all of the components of an AI application (mod- els, rules engines, data connections, skills, and more) to deliver actionable insights into your applications?
AI Powered Personalization: How do you continuously learn about key entities (customers, products, pol- icies etc.) through declared, observed behaviors, and inferences to drive personalized engagement?
AI Development Lifecycle: How do you develop an AI playbook that helps disparate teams across data, data science, IT, business, and compliance, manage activities required for building, productionalizing and operationalizing a wide range of AI applications for use across the enterprise?
AI Application Optimization: How do you optimize your AI Systems to business goals and KPI’s based on continuous feedback and learning?
Scale and Acceleration of AI: Can AI application development teams across the enterprise achieve repeat- ability and scale across AI applications across multiple use cases, to accelerate a robust AI roadmap?
Cloud Agnostic: Can AI application development, data aggregation and deployment operate on any cloud, on internal systems (“on prem”), and hybrid deployments?
Responsible AI: How do you continuously evaluate an AI System (ML and non-ML models) for bias, fair- ness, explainability and robustness, to help insure responsible use of AI applications?
Contact us to explore how we can help your organization rapidly productionalize intelligence that delights customers and produces incredible business outcomes.