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The demand for machine learning is only going to increase, thus the need for engineers and data scientists will follow suit. No one wants to talk about the potential roadblocks you’ll encounter when developing ML models.
As you begin developing your ML models, here are the common challenges you might encounter during your project.
1. Developing A Good Enough Model
We’ve worked with several companies, including Uber, and the biggest challenge with their machine learning team is building a model that’s good enough that will provide business value. We hear that nearly 80% of ML models built, don’t make it production because it doesn’t provide value.
Deploying isn’t the issue, it’s providing a use case and reasoning to back it up.
2. Identifying The Use Case
Speaking of use cases, there needs to be common ground between the owners and data scientists in the organization.
Gather the necessary departments into a meeting and discuss the needs of the owners and capabilities of the team. Identify the business case and make sure the ML engineers and data scientists are part of the process.
3. Lack Of Predictability
We often don’t know what will happen and if models will be successful upon deployment. So instead of pinpointing one problem, take a portfolio approach where the ML team explores multiple projects at once.
If you’re encountering similar challenges, join us for our upcoming webinar as we talk about these challenges and building successful ML teams. In this webinar, you’ll learn the key challenges faced by ML practitioners in the field and the tools and processes for deploying ML at scale.
Original post appears on comet.com.
Source: https://www.kdnuggets.com/2022/09/comet-tackle-3-common-machine-learning-challenges.html
