Last week, our CEO, Diego Oppenheimer, and CEO of ArthurAI, Adam Wenchel, hosted a webinar on the state of enterprise machine learning in 2020. The webinar was moderated by Algorithmia VP of Engineering, Ken Toole.
Diego and Adam leverage their knowledge of AI and machine learning and offer their enterprise experience making these technologies available for companies to automate their business operations. This is a great opportunity to learn from industry leaders what they see happening in the AI/ML space and what is likely ahead for companies deciding to incorporate AI and ML into their workflows.
The talk started with a look at our 2020 state of enterprise machine learning report, which published in December. The report focused on seven key findings:
- the role of the data scientist and the rise of data science arsenals at companies to prepare for data value extraction via machine learning models.
- the most common challenges to developing mature machine learning programs are deployment, versioning, and aligning stakeholders within an organization.
- investment in AI/ML in the enterprise is growing swiftly with several industries leading the charge.
- most companies are spending more than 8 days, and some times up to a year, deploying a single model.
- the majority of companies undertaking ML initiatives are in relatively early stages (ie. developing use cases, building models, or working on deployment).
- there is a discrepancy in determining what ML success looks like across industries and roles within an organization.
- business use cases for machine learning vary, but the most common ones are for gaining customer insight and for reducing costs.
One of the topics of discussion surrounded how DevOps, engineering, and data science teams are organizing around machine learning. Diego and Adam both mention the blending of roles and the morphing of resources across business units. About this change, Adam said:
Having to change the way groups are organized in order to be successful is something we see over and over again.” — Adam Wenchel, CEO ArthurAI
Model deployment challenges
A topic that Algorithmia cares about deeply is the time to deployment for machine learning models in the enterprise. We talk to a massive number of companies that say they spend between 8 and 90 days deploying, and an alarming number of companies who spend more than 90 days, and we think that’s unnecessary and a waste of valuable resources.
Time to deployment is where we see a giant gap; the fact that it could potentially take 90 days, or even more in some cases, to deploy a single model is scary because the cost balloons during that time and it’s unacceptable to the C-suite.” – Diego Oppenheimer, CEO Algorithmia
Listen to the full story
The webinar covers many trends in the AI/ML space, and it’s a great opportunity to hear from three leaders in enterprise machine learning. Watch the full webinar here and if you’d like a copy of the slides, click below.