Within the corporate sphere, the fog of uncertainty and the expectation of ROI shrouds AI adoption, obscuring the path with ambiguity regarding use cases, resource requirements, and strategic prioritization. OmniData offers clear direction and expert guidance to navigate the complexities of AI adoption and value creation, particularly with Machine Learning Operations (MLOps). Our MLOps consulting services for Microsoft Azure are designed to help customers get started with or extend their use of Azure by providing the expertise, capabilities, and know-how they may lack in-house or need to augment.
OmniData's Azure-focused MLOps engagements typically include several key components:
- Strategic Consultation: We provide strategic consultation to help refine AI/ML strategies and develop a detailed architecture roadmap tailored for Azure.
- Azure MLOps Framework Development: The engagement includes the creation of an MLOps framework specifically designed for Azure to support continuous integration and deployment (CI/CD) of ML models.
- Ongoing Support and Consultation: OmniData offers ongoing support and consultation services to address existing AI/ML priorities and collaborate on new initiatives, ensuring seamless integration with Azure services.
EXAMPLE IMPLEMENTATION AGENDA:
- Project Kickoff: Initial meeting with the OmniData team and project sponsor to outline the project scope and objectives, focusing on Azure capabilities.
- Azure Infrastructure Deployment: Setting up the necessary Azure infrastructure and ingesting data into raw structures.
- Data and System Analysis: Conducting a thorough analysis of data and systems to design an effective data model optimized for Azure.
- Model Production: Building and deploying ML models into Azure production environments.
- User Acceptance Testing: Ensuring that the deployed models meet the required standards and specifications within the Azure ecosystem.
- Ongoing Enhancements: Regularly reviewing and enhancing the deployed models and systems to ensure optimal performance on Azure.
OUTCOMES
Customers can expect several tangible outcomes from an Azure MLOps engagement with OmniData:
- Engineering Changes: Implementation of a robust data architecture that addresses data latency, scalability, and migration issues within Azure.
- Technical Artifacts: Development of comprehensive technical artifacts, including detailed architecture roadmaps, data models, and deployment pipelines tailored for Azure.
By engaging with OmniData, customers can expect to enhance their AI/ML capabilities, achieve their strategic goals, and drive innovation using Microsoft Azure.
This is a 40-hour service engagement. Actual outcomes and pricing will vary based on project scope.