Mphasis Stelligent MLOps Automation Services
ML Life Cycle Automation
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:
Faster Cycle Time
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.
Increase Collaboration
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.
Improve Governance & Control
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.
Scalability and Quality
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.
Lower Costs
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.
Learn more about what Mphasis can do for you in AI/ML on AWS
Mphasis has a broad and deep set of
offerings for enterprises seeking to
deploy AI/ML at scale on AWS,
as well as
PACE-ML,
our framework for automating machine learning pipelines.
Contact us to learn more.