How ModelOp Gives You Control
IT teams face many challenges: how to deploy, monitor and govern all the machine learning and AI workloads their company is building including the variety of open source options like R, Python and Tensorflow. This can be a daunting task for IT professionals, as they must support deployment as part of core business operations.
Use any framework, language or data
R, Python, Scikit, Tensorflow, C, Matlab, Scala. ModelOp supports them. Apply your model to real-time data arriving in Kafka or REST or run it in batch against a database or file system.
Centralize Model Artifacts & Metadata
Track Key Metrics
Model utilization, data skew, accuracy, latency, overall model productionization time, SLA and other metrics are captured in one place in a consistent format.
Customizable Model Lifecycle
Define workflows to manage the entire lifecycle of a model from development to testing, to production, to continuous improvement and eventual retirement. ModelOp provides an out-of-the-box integration with github, docker, kubernetes, hdfs and sql databases.
How Your Work Will Be Transformed
As an IT professional charged with enabling ML and AI in your organization, you are fundamentally concerned with reducing the impedance between model development and putting a model “into business”. ModelOp Center provides out-of-the-box capabilities to support this effort as well as the ability to build new capabilities and assets needed for your organization. Every new model doesn’t need to be a new IT effort!
How ModelOp Is Unique
ModelOp takes a fundamentally different approach to enabling Model Operations for large enterprises. Built with the critical capabilities required for IT operations teams to support data scientists and the business in getting models into business quickly, consistently, and reliably. By leveraging a set of simple but powerful abstractions, which enable long term, uniform deployment success for the enterprise. Fundamentally designed from the core to drive automation for an enterprise’s data science investments.
Models should follow a uniform deployment process regardless of source
Data schemas should be defined by data science teams and supported by data engineering
Data streams enable a model to effortlessly move from batch to on-demand applications.
As is often the case, base architectures are critical to ensure both flexibility and scalability for the IT teams.