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REPORT: Q&A with Mike Gualtieri, VP Principal Analyst at Forrester
Video
Friday, July 22, 2022

Now is the Time to Focus on AI Engineering

About the Webinar

CognitiveScale, CEO, and long-time IBM veteran Bob Picciano featuring Forrester’s, VP Principal Analyst, Mike Gualtieri are proud to present a fireside chat on the secret sauce to extend Data and ML Ops platform with four key innovations.

During this fireside chat, you will learn more about how the business case for AI is clear, yet businesses struggle when it comes to operationalizing and proving outcomes. For AI to have a transformative impact, it needs to be industrialized. That means putting aside the one-off solutions and establishing proven AI engineering practices — standardized building blocks, tools, and processes that make it easier to drive value faster, and in a way that scales.

AI is a team sport and AI engineering is a discipline for organizing their team, tools, and processes. ML Ops, information technology (IT), software development, business leaders, subject matter experts, and even compliance, audit, and risk management must all work together to build an AI engineering competence that drives enterprise AI initiatives. According to Carnegie Mellon’s Software Engineering Institute, the three pillars of AI engineering that will help drive value from AI applications are robust and secure AI, scalable AI, and human-centered AI.

CognitiveScale pioneered the concept of ‘AI Engineering', paving the way to industrialize scalable Enterprise AI development and deployment. 

Key Takeaways

  • How to extend ML Ops to speed delivery of repeatable AI Applications that are optimized to business goals.
  • The challenges of AI and machine learning, and the solutions for overcoming them.
  • How the business case for AI is clear, yet businesses struggle when it comes to operationalizing and proving outcomes; and how to overcome this.
  • How AI Engineering is a discipline for organizing a team, tools, and process to build scalability and generate 3x more value in your AI efforts.
  • How an effective end-to-end model can help integrate your data & models, orchestrate execution seamlessly, composes AI applications, and deploy on any cloud to optimize your business impact.
  • The promise of AI and the need for an Enterprise AI platform.
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