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 Forrester survey, 53% of global data and analytics decision makers say they have implemented, are in the process of implementing, or are expanding or upgrading their implementation of some form of artificial intelligence—but while these numbers are growing rapidly, many of the investments being made are not where you would expect. It’s predicted that by 2024, 75% of enterprises will invest in employee retraining and development to address new skill needs and ways of working resulting from AI adoption. While the promise of ML and AI for business remains massive, this is indicative of the existing knowledge gap with effectively putting ML to work for your business. While this may seem daunting, the good news is that all the pieces of the AI puzzle—everything from the right platform, tools, and skills—is ready to be discovered and implemented today.
Join us December 10, 2019, to learn how to operationalize enterprise ML from the experts.
We at Cloudera feel that productionalizing ML, and the challenges faced by many organizations in doing so, are critical to the success of any enterprise looking to accelerate their ML journey. On December 10th, you’ll have the unique opportunity to join Cloudera Chief Architect Doug Cutting, Forrester senior analyst Dr. Kjell Carlsson, and Senior Product Manager Alex Breshears in an open discussion about how to tackle these challenges head on.
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?
Details
Please RSVP and we hope to see you there!
Operationalizing Enterprise Machine Learning
Hosted by Cloudera and featuring Forrester Senior Analyst Kjell Carlsson.
December 10th, 2019 – 10:00am PT | 1:00pm ET
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