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As the demand for machine learning continues to grow, so will the need for engineers and data scientists. Nobody wants to talk about the obstacles you can face when developing ML models.
Here are some common challenges you may encounter during your project when starting to develop an ML model.
1. Develop enough models
We’ve worked with several companies, including Uber, and the biggest challenge for their machine learning team is building models that are good enough to deliver business value. We’ve heard that nearly 80% of ML models built never make it into production because they’re worthless.
The problem isn’t deployment, it’s providing a use case and the reasoning behind it.
2. Identify use cases
When it comes to use cases, there needs to be a common ground between owners and data scientists in your organization.
Bring the necessary departments together for a meeting to discuss the owner’s needs and team capabilities. Identify the business case and ensure that ML engineers and data scientists are involved in the process.
3. Lack of predictability
Often we don’t know what will happen and whether the model will succeed when deployed. So, rather than identifying a single problem, adopt a portfolio approach in which your ML team considers multiple projects at once.
If you are facing similar challenges, please join us for an upcoming webinar where we discuss these challenges and Building a successful ML teamIn this webinar, learn about the main challenges facing ML practitioners in the field and the tools and processes for deploying ML at scale.
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