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NEW REPORT: Q&A with Mike Gualtieri, VP Principal Analyst at Forrester

Overview
WHAT IS AI ENGINEERING?
Drive Observability and Operationalization Across the full AI lifecycle

The Rise of AI Engineering

PROBLEM: Developing AI solutions requires an engineering approach that is resilient, open and repeatable to ensure necessary quality and agility is achieved. Until today these efforts are missing the foundation to address these challenges amid a sea of point tools and fast changing models and data.

STAGE 1

ML Ops Era

I/T focus on Data Science and ML Ops but manually “wire” AI into solutions for point-to-point isolated successes. Successes are not repeatable.

STAGE 2

AI Engineering Era

Business and I/T work together leveraging a platform deploying repeatable patterns for AI solution success and measurable business impact.

STAGE 3

Client-centric AI Era

Enterprises unleash sustained value and client trust by knowing and serving them better and through the right channel.


AI Engineering Definition & Value

Carnegie Mellon's Software Engineering Institute defines AI Engineering as ". . . an emergent discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts."

~ Carnegie Mellon SEI

Gartner says "A robust AI engineering strategy will facilitate the performance, scalability, interpretability and reliability of AI models while delivering the full value of AI investments.”

~ Gartner

Gartner predicts "By 2025, the 10% of enterprises who establish AI Engineering best practices will generate at least three times more value with their AI efforts than the 90% of enterprises who do not."

~ Gartner

Limitations of the ML Ops Era

Successful AI application development requires the collaboration of various personas across functional teams with an AI Engineering approach that addresses specific gaps in the AI lifecycle

Limitations of the ML Ops Era
Integration
How do you integrate insights into systems of record and engagement? (CRM, Apps, portals, etc)
Composition
How do you combine independent models and rules in complex workflows?
Personalization
How do you create hyper- personalized customer profiles at scale?
Trust & Governance
How do you provide a detailed explanation of decision workflows?
Measure & Monitor
How do you provide a detailed explanation of decision workflows?

CognitiveScale Closes the Gap

CognitiveScale's Cortex AI Platform provides visibility across the entire AI Engineering workflow with views and KPIs relevant and specific to each persona within the enterprise

Deployment of AI at Scale
Integration
Enable Developers to leverage a library of data connectors to integrate insights into disparate systems.
Composition
CognitiveScale's Cortex AI Platform is a collaborative platform for automating development and control of AI applications across multiple personas.
Personalization
CognitiveScale's Cortex AI Platform Profile-of-One enables personalized insights based on declared, observed and inferred attributes.
Trust & Governance
The Cortex AI Platform enables organizations to explain and prove compliance with applicable rules and regulations.
Measure & Monitor
Cortex-enabled operationalization of AI solutions includes both model and business observability.

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Q&A with Mike Gualtieri, VP Principal Analyst at Forrester

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