The 5 Steps To Build A Business’s Deep Learning Workflow
Deep learning, or using massive amounts of data to build intelligent models, is a hot topic. Many companies, now seeing the benefits of AI materialize, have decided they need to get started on deep learning or risk getting left behind.
How do you connect the big idea (businesses need to get started on deep learning) to the specific topic at hand (set up a workflow)? Here are the five main steps on setting up a deep learning workflow.
1) Identify Business Problems
Computers are great at optimizing models, but not so great at setting strategic goals. This is where humans add a lot of value right at the beginning!
- If you’re in a big organization, it’s important to have your executives and data scientists buy in on the goals. Together, you can look for areas or use cases that can benefit from deep learning in support of solving a pressive challenge or tapping into a new opportunity.
- After coming up with one to three pilot projects, put together goals that are as specific, relevant, attainable, measurable, and timely as possible. The reason to generate multiple ideas up front is to leverage the experience of the gathered team and decide on a strategy to pivot quickly, in case results are not forthcoming from the first pilot.
- Stay mindful of any regulatory aspect of your project, which is especially important with the implementation of GDPR. This not only affects what data you can collect, but also how long you can store it. For example, if you’re in insurance, industry policies require you to explain why your tools reject a client’s claim, so you might want to use a deep learning tool with decision-tree models (a traditional machine learning method) to help with explainability and interpretability of the decision.
2) Build a Data Strategy
Having the right data strategy has always been one of the most challenging steps. Data is the experience from which models learn. There are two ways we can gather data:
- Leverage publicly available datasets. There are many public data sets on Github and Kaggle you can use to get started. Some developers have mistrust of public data sets, with skepticism around the quality of such data.
- Build (and label) your own using available domain expertise or outsource that effort.
If quality and relevance of the data set is important to you,...
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