Optimize and Seamlessly Deploy Your Models
Be the bridge between data engineering and data science by optimizing and implementing the models in the production environment.
Benefits For ML Engineers
Leverage a unified Operational AI platform that not only powers analytics and machine learning but also powers real-time applications. Optimize model performance by minimizing data movement and latency. Develop complete visibility into the model deployment process.
Key Splice Machine Benefits
Run Your Models at Optimum Level
A Machine Learning model needs to be constantly monitored in order to ensure that its performance is meeting the accuracy threshold. One of... the primary reasons contributing to model degradation and failure is the change in the data attributes. Splice Machine provides ML engineers the flexibility to continuously experiment with different data sets, transformations and parameters to identify the most effective model to put into production.
Effectively Manage the Model Lifecycle From From Test to Production
Use familiar languages including R, Python, PySpark, and Scala to fine-tune the model to make it more robust and ensure that it runs... effectively. Splice Machine’s in-database workbench supports the complete lifecycle of machine learning ranging from transactional updates to data wrangling to experimentation, and finally to deployment – all delivered as part of a real-time integrated platform.
Seamlessly Deploy Your Trained Model Into Production
Majority of the effort in implementing machine learning projects is not related to writing the code for the model but rather in developing... the application that will act on the prediction. Taking the trained model and making it work as part of the production environment is an integral part of the ML engineer responsibilities. With Splice Machine, the model deployment is greatly compressed where ML engineers can use MLflow to package models into Docker images, which are then deployable directly via Sagemaker for implementation.