Operationalizing Enterprise Machine Learning: A Fireside Chat with
One aspect of machine learning (ML) adoption that we see time and time again as a point of friction for enterprise organizations is how to effectively operationalize and govern ML models, ensuring transparency, continuous learning, and continuous return on investment for the business. While corporate AI initiatives are fueling investments in new technology and skills profiles to support enterprise machine learning projects, organizations are finding they're not enough - they still face real difficulties when it comes to deploying and managing machine learning in production. Across the machine learning development lifecycle, from deciding whether a capability is feasible, what it will take, and whether it's worth developing, to the post-deployment phase where it can be difficult to understand where a model has gone wrong and what to do about it, expensive and even potentially dangerous pitfalls and barriers to success abound. The truth is that whether organizations are just starting their AI journey or have already seen reasonable success, the challenges of production ML requires both the right technology and the right organizational approaches that enable ongoing deployment, serving, and operation of models across the business.
It's no mystery that effectively productionalizing ML can be challenging. Based on a recent
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In this fireside chat, our experts will dive into pressing questions and discuss the best practices that enterprises are using to successfully develop and operationalize ML and AI solutions that drive transformational outcomes. This will include:
How to hire and retain the right talent?
How can businesses develop capabilities to quickly and effectively productionalize ML models?
What are the requirements for effectively governing and scaling ML models in production?
And what are the best practices for ML operations and governance that will accelerate time to value?
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Operationalizing Enterprise Machine Learning
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