End-to-end tools to productionize AI and data science.
Manage data pipelines seamlessly.
Detect model drift.
Collaborate in the platform.
Engage technical and end users.
Leverage AI while minimizing risk.
Create and deploy models with Jupyter.
No matter your use case, Domo’s seamless integration with Jupyter ensures that you can use the tools you’re familiar with to develop machine learning (ML) models and then produce relevant insights available to business users. With Jupyter Workspaces, you can:
Accelerate model development with AutoML.
Professional services to boost your team.
Sample use cases.
Here are just a few ways you can use Domo’s data science tools. If you don’t see what you’re looking for here, contact us to discuss your specific use case.
- Estimate the total number of months that an employee will work for the company.
- Understand the risk level of termination, considering management succession, last bonus, last base pay increase, total pay, and more.
- Intervene earlier to retain valuable employees.
- Select criteria to use for calculating engagement scores, such as purchase history, webstore usage, and payment behavior.
- Segment customers and personalize customer interactions based on engagement scores.
- Drive retention and upsell activities based on customer engagement.
- Choose data points to build a model that predicts which loans are potentially worrisome, using data such as number of months where loans are past due, credit score, and loan payoff balance.
- Arm customer service teams with information to intervene in high-risk scenarios, before it’s too late.
- Reduce the number of loans that go into default.
- Use forecasting to predict orders in future periods and adjust inventory levels accordingly, so you can meet customer demand while not carrying extra inventory.
- Leverage historical data and assumptions about the market and company performance to predict future customer ordering trends.
- Set alerts to notify your company when adjustments to inventory are needed based on your forecasting results.
- Decipher the emotional tone of a given text using a Natural Language Processing (NLP) algorithm and classify the text as positive, negative, or neutral.
- Identify which words or topics are commonly mentioned in your text data.
- Leverage insights to refine product offerings, improve customer service, increase brand reputation, and boost company performance.
- Identify underlying factors that contribute to whether a customer pays for an account.
- Provide teams with enough lead time to allow identification and preemptive resolution of billing issues.
- Use additional analytics for more accurate revenue forecasting.