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Managing end-to-end machine learning lifecycles



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

  • Promotes ML models to production faster and at scale
  • Faster time to market for products & services
  • ML model building and deployment can be done effectively with much less effort


  • Provides automated model pipeline management that reduces manual interventions, decreases time for deployment, and enables continuous delivery

Responsible AI

  • The Responsible AI integrated into PACE-ML focuses on developing robust, interpretable, explainable, bias-free, auditable, and privacy preserving AI. This makes the system trustworthy, thereby improving customer experience, reducing liability risk and ensuring regulatory compliance


  • Increases users' confidence in the system through model lineage, auditability and explainability


  • Tracks model, code and data changes and increase the ease of collaboration among teams


  • Monitors models in production
  • Responds to model performance issues faster
  • Identify data and model drifts


  • Allows users to identify biases or defects in the system so that they can be corrected

Cost of Development

  • Reduces cost of development due to automation & seamless integration. Provides access to Feature Store and Model Stores for effective collaboration across teams

Governance & Compliance

  • Reduces risks due to Model explainability, compliance and auditability



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:

  • Gap analysis with respect to MLOps best practices
  • Identification of high impact use-cases in the chosen data science practice
  • Solution creation based on the existing infrastructure, processes, and data landscape
  • Customized recommendations on frameworks and tooling for MLOps pipeline either on Cloud or on-prem
  • Reference architecture diagram for build, deploy and integration
  • Project plan and statement of work by phases on the full implementation

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.