PACE-ML – MPHASIS MLOPS PLATFORM
PACE-ML empowers data scientists, data engineers, developers, and IT operations to collaborate, build, deploy, and monitor ML models efficiently in production environments at scale.
PACE-ML is Mphasis Platform for end-to-end machine learning development and deployment using MLOps principles. It is a combination of Mphasis proprietary tools and methodologies along with the best in-class third-party as well as open-source tools. It is an end-to-end platform to automate multiple stages in the ML pipeline. The objective of the platform is to accelerate the life cycle of machine learning development, deployment, and monitoring of ML algorithms. PACE-ML uses workflows, collaboration platforms and monitoring tools for improving efficiency and streamlining model selection, reproducibility, versioning, auditability, explainability, packaging, re-usability, validation, deployment & monitoring.
PACE-ML is built on MLOps principles to facilitate a set of practices and activities which enable data scientists, data engineers, developers, and IT operations to collaborate and manage production pipelines of machine learning applications and services. PACE-ML enables organizations to improve the quality & reliability of machine learning solutions in production and helps automate, scale, and monitor them.
Efficiency, Speed & Time to Market
Cost of Development
Governance & Compliance
MLOps Assessment & Workshop
Mphasis PACE-ML Assessment helps enterprises perform structured analysis of their data science practice to identify potential use-cases and toolchains for deployment of ML models using MLOps principles. The typical duration of the assessment and workshop exercise is one to two weeks. Mphasis PACE-ML offers MLOps assessments and workshops led by our data scientists, solution architects and SMEs. The deliverables of this exercise will be an assessment report indicating:
The roadmap for the engagement includes the following key activities - Stakeholder interviews, Assessment and user journey workshop, Identification of frameworks and tooling, Validation of gap analysis, understanding and assumptions, Recommendation of solution, assumptions, and impact and report-out.
Mphasis PACE-ML: Implementation & Deployment
PACE-ML offers a single platform to run machine learning experiments, test & deploy them in production, and further manage and manage them with ease. It also ensures that all production machine learning systems work under a robust framework across the organization, leveraging and sharing the burden of production model management with the entire team. Mphasis PACE-ML implementation is led by our PACE-ML SMEs, architects, and data scientists. The deliverables of this exercise will be a prototype MLOps pipeline setup on the cloud/on-prem which will highlight key elements of versioning, experiment tracking, standardized frameworks, automated ML pipelines, and ML monitoring dashboards.
The roadmap for the implementation includes the following key activities - Requirements gathering, business and data understanding, setting up of standardized frameworks and checklists for collaboration, Versioning and experiment tracking setup, Automated Feature Engineering, Model development and evaluation frameworks, Automated ML pipeline development, Monitoring dashboard, implementation roadmap for enabling ML models at scale.