Machine Learning Operations

Streamline your ML operations by using a structured canvas for effective architecture and infrastructure management.

Pricing not published

Guides ML practitioners through a structured and quality-assured development process.

Pricing not published

Streamline your ML development and deployment processes.

Pricing not published

Capabilities that manage the end-to-end ML lifecycle including experiment tracking, CI/CD integration, model versioning, deployment readiness, and workflow automation to operationalize models.

Products: CRISP-ML(Q) ML Lifecycle Process, MLOps Consulting Services

Frameworks and planning artifacts to define ML system architecture and infrastructure, stack components, data and code management, model management, and metadata strategies.

Products: MLOps Stack Canvas, MLOps Consulting Services

Capabilities for testing, scoring, certifying, and quantifying model risk with auditable scorecards, customizable evaluators, and test-data generation to meet regulatory and mission-risk requirements.

Products: CRISP-ML(Q) ML Lifecycle Process, MLOps Consulting Services, MLOps Stack Canvas

MLOps Stack Canvas

MLOps Stack Canvas is an architecture and infrastructure planning framework for ML systems. It is designed to be application- and industry-neutral and aligns with CRISP-ML(Q). The canvas describes eleven building blocks and organizes data and code management, model management, and metadata management to help teams design, implement, and govern ML-powered software.

Pricing not published

• Define ML Architecture
  Facilitates the specification of a systematic and adaptable architecture for managing ML operations.
• Track Model Lifecycle
  Enables effective oversight and management of model versions from development to production.
• Align with CRISP-ML(Q)
  Helps standardize practices during the ML development lifecycle, enhancing reproducibility and accountability.
• Govern Metadata
  Facilitates tracking and governance, ensuring easier project audits and compliance.
• Manage Data and Code
  Ensures streamlined data handling and code organization, vital for compliance and efficiency.
• Utilize Eleven Components
  Provides comprehensive guidance by outlining essential components crucial for effective ML system architecture.
• Organize Workflows
  Enhances clarity and efficiency by structuring ML workflows and supporting collaboration.

CRISP-ML(Q) ML Lifecycle Process

CRISP-ML(Q) is a cross-industry standard process model for machine learning development with quality assurance. It outlines six phases: Business and Data Understanding, Data Engineering, Machine Learning Model Engineering, Quality Assurance for ML Applications, Deployment, and Monitoring and Maintenance. The model emphasizes measurable requirements, risk awareness, and reproducibility across the lifecycle.

Pricing not published

• Provides Structured Phases
  The CRISP-ML(Q) model includes essential phases that ensure a comprehensive approach to machine learning development, promoting a structured and iterative workflow.
• Enhances Model Reliability
  Integrating quality assurance at each phase helps mitigate risks and ensures that machine learning applications meet performance and robustness standards.
• Supports Documentation and Transparency
  Promoting thorough documentation across all stages strengthens transparency and allows for reproducibility in ML applications, facilitating troubleshooting and audits.
• Mitigates Development Risks
  Specifying potential risks and governance needs throughout the ML lifecycle ensures compliance with legal and ethical standards while guiding project feasibility.
• Optimizes Model Performance
  Encourages iterative review and adjustment of models based on feedback and results, facilitating constant improvement of outcomes.
• Prepares Quality Data
  A dedicated phase for data engineering highlights the importance of high-quality data as a foundation for successful ML model training.

MLOps Consulting Services

MLOps consulting services help organizations design, deploy, and manage ML-powered software within a unified CI/CD-style workflow. Services cover data engineering, ML model engineering, deployment, monitoring, and governance, with emphasis on automated testing, reproducibility, and cross-team collaboration.

Pricing not published

• Automate Testing Processes
  Utilizing automated testing ensures quick identification of issues, facilitates reliable ML operations, and reduces the likelihood of deploying flawed models.
• Implement CI/CD Practices
  Enables seamless integration of machine learning models into software systems, ensuring that updates are automatically deployed and tested.
• Provide Complete Lifecycle Support
  Guides organizations through every aspect of ML project implementation, from initial data ingestion to ongoing monitoring of models in production.
• Enhance Team Collaboration
  Promotes synergetic interactions between diverse teams, fostering effective communication and streamlined project execution.
• Establish Governance Frameworks
  Ensures compliance with regulations and enhances model accountability through robust governance practices and documentation.