Select the Best Use Case
Now that you understand more about deep learning and the development cycle, it’s time to investigate how you can use Einstein Vision and Einstein Language to solve your own business problems.
Here’s a list of considerations when selecting a successful use case.
- Select the right business problem. During the process of identifying a good use case, there are various factors to consider. First, find business problems in your organization that fit the technologies available in Einstein Vision or Einstein Language. Look for tasks that can be automated and are a good match for these services.
Be sure that the problem you solve isn’t too big or complex. This way you can achieve success more quickly. And consider whether solving the problem has value to your organization.
- Select a use case for which you have data. Data is central to successful deep learning projects. When you identify a high-value business problem, confirm that you have enough data to solve the problem using deep learning.
The quality of the data is also important. For Einstein Vision, do you have a variety of images of what you want to identify? For Einstein Language, do you have enough sentiment or intent text data for each label?
- Understand the effort required for data processing (labeling or formatting data). After you identify that you have enough high-quality data, be prepared for the time and effort required to process the data.
Whether you use Einstein Vision or Einstein Language, the data must be sorted and labeled. For object detection, parts of images must be labeled with bounding box data. If text data is in long paragraphs, you need to process it so that individual phrases can be extracted and labeled.
- Define the business success metrics. When you implement deep learning, your goal isn’t to use AI; the goal is to solve a business problem. To ensure that your solution is working, define what success looks like from the business perspective.
For example, if you’re implementing a deep learning project to route incoming support cases, define up front how many cases you want to route correctly and in what timeframe. If your solution correctly routes a case, identify the time and cost savings. Understand the impact that the solution has on your business.
- Bring in the right expertise. To build and implement deep learning solutions requires people in your organization that understand AI and deep learning. You can use the Einstein Platform Services managed services to quickly build solutions. But it’s still necessary to have people that understand the deep learning life cycle and technologies. Be sure that you have people that can frame the problem, collect the right data, label and process the data, understand the model, interpret the model metrics, and understand how to use feedback to improve the model.
- Blog post: How to Choose Your First AI Project