Name: Architecting Scalable AI Operations and Avoiding Key Bottlenecks
Start: 2022-11-22T17:00:00Z
End: 2022-11-22T17:00:46.000Z
Location: BrightTALK
Rating: 0

KEY TAKEAWAYS:
- The current Model Governance processes and tools need to be augmented to accommodate AI
- The modern enterprise cannot afford to wait to implement an AI Model Governance framework due to business, operational, and risk concerns
- A robust AI Model Governance framework should cover more than just Ethical Fairness, but account for business, data, IT, risk, and security concerns
- Getting started with an AI Model Governance framework can be achievable in months

A Mandate for Model Governance in the Age of AI

Complex questions around the responsible use of artificial intelligence (AI) continue to arise as its adoption unfolds: explain-ability, responsibility, fairness, and misuse are all poignant AI ethical challenges. 

However, enterprise AI usage faces yet greater scrutiny as the European Union (EU) and other regions rollout policies to regulate AI and reduce compliance risks. In this vein, which regulations should your company prepare for and how could they impact your current (or future) AI usage?

Ramesh Dontha of DigitalTransformationPro.com joins a panel of 3 industry experts to examine this issue in this BrightTALK Original webinar. Tune in to discover:
• What kinds of risks will AI pose to you as a company and as a consumer? 
• How to prepare for upcoming global and local AI regulations 
• What differentiates AI regulations for SMBs vs. large organizations?
• And much more

AI and Compliance: How to Prepare for Greater Regulations

Scaling AI for an enterprise means more than more data, more compute, bigger speeds and feeds.

Enterprise Architects need to accommodate multiple DSML tools, data systems and serving environments. In addition to the migration to cloud or multi-cloud, the dockerization journey, the increasing use of 3rd party AI models and 4th party AI. How do you implement enterprise standards for Operations and Governance, without stifling innovation and adding complexity?

Architecting Scalable AI Operations and Avoiding Key Bottlenecks

Usually when we think “governance”, we think ”slow”, “bottleneck”, and “anti-innovation”—all of which run counter to the desired outcomes of AI initiatives. 

However, the right AI governance approach goes beyond ethical AI, fairness and bias and also provides visibility to the business contribution of AI efforts and investments, freedom to innovate with the latest tools and techniques, and faster resolution of problems with less finger pointing and wasted efforts.

Tune into this webinar to uncover the intricacies of AI governance and learn how to: 
• Show the ROI of your AI initiatives to extend buy-in
• Automate the full model life cycle and establish seamless handoffs across teams
• Define and implement reusable governance templates
• Automatically ensure all AI governance policies (e.g. ethics and fairness) are enforced

The Surprising Truth about AI Governance

Scaling AI for an enterprise means more than more data, more compute, bigger speeds and feeds.
Enterprise Architects need to accommodate multiple DSML tools, data systems and serving environments. In addition to the migration to cloud or multi-cloud, the dockerization journey, the increasing use of 3rd party AI models and 4th party AI.

How do you implement enterprise standards for Operations and Governance, without stifling innovation and adding complexity? 

Tune into this on-demand session with ModelOp CTO Jim Olsen to gain experienced insight into how to architect scalable AI and navigate common—but significant—bottlenecks that inhibit success.

AI Architecture

Responsible AI

AlOps

Data Modelling

Models

Machine Learning

MLOps

Model Production

Model Portfolio

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Architecting Scalable AI Operations and Avoiding Key Bottlenecks

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