As you work to build your business into an AI-driven enterprise, you’ve probably encountered a few baffling pitfalls along the way. While you have hired top-notch data scientists to build models and invested in expensive data science tools, you still can’t get your AI projects off the ground. Why? What factors make it so hard to implement AI at scale?
In our ebook, How MLOps Can Help You Realize Your AI Dreams, we take a closer look at the real-world machine learning issues that may be holding back your AI projects, such as:
- Why data scientists are very particular about how they like to work and why you should respect their preferences
- What common stumbling blocks come up when the IT and data science teams work together
- How thinking about production model governance proactively can save you and your business legal and regulatory headaches down the road
- Why monitoring that is designed specifically for machine learning is so important
DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.
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