Improve the automation, governance, and quality of production ML through Continuous Delivery (CD) pipelines and Self-Service Portals
As enterprises seek to move from ML testing to production, it is essential to address key challenges around deployment, model reproducibility, diagnostics, governance, collaboration, scalability, handoffs and meeting key metrics. Mphasis Stelligent adapts their proven ‘automate everything’ approach to meet these challenges. Our methods enable data scientists and production teams to jointly extract key benefits for hastening a typical ML lifecycle:
Reduces process friction of model creation, iteration, and deployment. Removes typical bottlenecks around data collection, testing, resource management, monitoring, and model analysis. Increases ability to experiment with new algorithms, modeling techniques, and parameter configurations.
Eliminates typical handoff challenges between data scientist, developers, and business stakeholders with an automated and coordinated process. Facilitates instant feedback, collaboration, and design cycles for continuous collaboration on exploratory data analysis and model development.
Addresses tracking, monitoring and versioning challenges – as well as workflow audit for compliance. Integrates and codifies controls to ensure that all models and configuration meet your defined requirements for security and governance.
Effortlessly scales ability to deploy and train new models with automated model training, evaluation, and validation pipelines. Horizontally scales tuning of implemented algorithms, accelerating discovery of optimal parameters for your models.Enforces quality at every stage of development through codified validation tests.
Reduces manual tasks allowing you to increase your development capacity and reduce errors in the ML lifecycle. Optimizes resource utilization during orchestration of model testing and analysis.
The Mphasis Stelligent MLOps program has proven capabilities to improve the speed and accuracy of model completion and transfer practices and processes. Leveraging automation, model democratization, and greater collaboration among data scientists, engineers, and other technologists, the Mphasis Stelligent MLOps program enables organizations to improve the time-to-value of their models.
Full implementation, includes integration with our DevSecOps solutions, incorporating security into every step of model development. MLOps CI/CD becomes an extension of your model release process increasing quality and decreasing the release cycle time.
For the Corporate Data Office (CDO) and Business Teams
The MLOps framework shortens the cycle time required to extract value from models with high quality. This allows agility for application of new models to business questions giving you a competitive advantage. Key information becomes available in minutes while others take weeks or months to understand their data.
For the Data Scientist and Team
The MLOps framework eases release to production by automating transmission of models and data sets to execution environments and operational teams. Short feedback cycles mean data scientists can iterate quickly and experiment without fear of lost time or effort. Teams can focus on bringing value to models with continuous development, iteration, design, testing and release cycles. Releases of validated models take minutes and constructive feedback arrives fast and frequent.
For the Operations Engineer
An MLOps framework allows you to focus more on serving the needs of your data science clients while minimizing manual operations tasks. Publish codified, validated, and compliant model execution environments along with secure access to data sources. Provide self-service mechanisms to empower developers and data scientists with on-demand, fully automated testing and experimental environments.
Want to learn more?
MLOps Workshops Available
The Stelligent MLOps workshop provides the framework for data and analytics teams to implement a MLOps practice. For each topic, an assessment is made of the current posture and steps to achieve full MLOps practice are identified. Best practices are reviewed and incorporated into the enablement plan. We formulate an action plan to accelerate achieving implementation of MLOps framework.
Please contact us to schedule a MLOps Workshop for your data scientist and production teams.