Ascend Ops™
Transform your credit operations with machine learning model deployment
Accelerate your model deployment process
Ascend Ops™ is our transformative model operations cloud solution. It allows you to register, test, deploy and monitor a full pipeline of custom features and models to existing or new production environment endpoints. Our innovative containerization process provides a private and secure environment for your intellectual property and eliminates recoding, which speeds testing, getting you to production in days instead of months and reduces risk.
Ascend Ops can help you bridge the gap between model development and production with:
- “No-recode” deployment
- Integrated user testing environment
- Performance and drift monitoring
- Model governance support
Fast
Register and migrate models to production environments quickly.
Flexible
Deploy in a wide range of languages and platforms with multiple delivery options.
Efficient
Code once and reuse anywhere while managing features and models all in one place.
Streamline your model deployment operations
Register, test, promote and manage models, faster
Ascend Ops Manager
Access everything you need to deploy, manage and monitor through our user-friendly, web-based application.
Model deployment as a managed service
Let our experts register, test and deploy features and models on your behalf.
Make machine learning model deployment easier
- Automated deployment enables custom features and models coded in any language or on any platform.
- Powerful run-time services source data, calculate features/models and serve both real-time and/or batch use cases.
- Elimination of recoding accelerates and streamlines testing.
- Proactive monitoring tracks performance and drift so you can make modifications as needed.
Model deployment is a critical part of the broader model operations workflow. The deployment step takes the model and makes it available for use in a live production environment, where it can be leveraged to deliver business value. For example, in the lending industry, new models may be deployed to help organizations enhance predictiveness in their decisioning strategies as business goals or market conditions change.
Typically, machine learning model deployment is preceded by rigorous and repeated testing and training on large data sets. Once a machine learning model’s performance has been validated, it can be integrated into a production environment. There are numerous ways to deploy machine learning models into production, including via API integration or software. Ascend Ops supports deploying machine learning models across cloud environments.
There are various types of cloud service deployment models depending on the cloud computing architecture, including public cloud, private cloud and hybrid cloud. Depending on your business requirements, each type offers different benefits and limitations when it comes to security, cost and scalability.
Once a model is deployed, its performance must be monitored for drift. This may involve tracking metrics such as accuracy, latency and throughput, as well as detecting and handling errors. Over time, the model may need to be updated as new data becomes available or as the underlying business requirements change, which involves retraining the model on new data and deploying the updated model to production.
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