Building, deploying, and monitoring ML models in production can be complex and time-consuming. HARMAN MLOps Professional Service enables enterprises to automate their ML pipelines, streamlining the management of model selection, versioning, auditability, explainability, re-usability, validation, deployment, and monitoring. Built on MLOps principles, this service helps organizations improve the quality and reliability of ML solutions in production.

**Assessments & Workshops **

HARMAN MLOps Assessments and Workshops provide a structured analysis of enterprises' data science practices to identify potential use cases and toolchains for ML model deployment using MLOps principles. During the workshops, HARMAN AI experts, SMEs, architects, and data scientists collaborate with client stakeholders to understand their current ML model building, deployment, and monitoring practices. The engagement includes activities such as stakeholder interviews, assessment and user journey workshops, framework and tool identification, gap analysis, solution recommendations, and report-outs. The outcomes of these exercises include prioritized use cases, recommended solution approaches, frameworks, tooling, reference architecture, and required infrastructure.

Implementation & Deployment

The Implementation and Deployment phase, a 2-3 month engagement, utilizes HARMAN's expertise and customizations to address specific client challenges. Leveraging its comprehensive framework, HARMAN MLOps streamlines the development, deployment, and management of machine learning solutions in production. Key capabilities include:

1. Integrated annotation: Support for multiple data types
2. Rapid experimentation: Orchestrated and automated workflows
3. Experimental-operational symmetry: Ensuring model efficacy in production
4. Modularized code: Reusable modules for scalability
5. Pipeline deployment: Seamless CI/CD integration
6. Feature Store: Attribute availability and reusability
7. Metadata management: Comprehensive scrutiny of metadata
8. Drift detection: Monitoring and statistical detection of data drifts
9. Model/pipeline performance monitoring: Continuous monitoring of performance and business KPIs

By leveraging HARMAN MLOps Professional Service, organizations can overcome the challenges associated with ML model development, deployment, and monitoring. This comprehensive solution enables enterprises to automate their ML pipelines, improve collaboration, and ultimately achieve better business outcomes through reliable, efficient, and high-quality machine learning solutions.