Carbon Accounting Management Platform Benchmark…
Data, AI & GenAI Roadmap
Crafting customized roadmaps aligning data and AI with organizational objectives ensures a clear path forward.
Approach
Navigating the rapidly evolving landscape of data and artificial intelligence (AI) requires a clear roadmap that aligns with your organization's goals and capabilities. Our approach to crafting Data/AI/GenAI Roadmaps begins with a comprehensive assessment of your current data infrastructure, organizational objectives, and market trends. We work closely with your team to identify opportunities for leveraging data and AI technologies to drive innovation, enhance operational efficiency, and gain competitive advantage. Our roadmaps are not one-size-fits-all; they are customized to fit your unique needs and priorities, providing a clear path forward with actionable steps and milestones to guide your journey to data-driven excellence.
Use cases
We defined and executed a comprehensive MLOps roadmap, utilizing AWS services to streamline time-to-market for machine learning (ML) initiatives. Leveraging Amazon SageMaker ML Pipelines, we orchestrated the end-to-end ML workflow, enabling seamless integration of data preprocessing, model training, and deployment stages. Implementing CI/CD practices and versioning control ensured continuous integration and deployment, facilitating rapid iterations and updates.
Adopting an "Everything as Code" approach, we codified infrastructure, configuration, and deployment processes, enhancing reproducibility and scalability. The utilization of model registry and feature store capabilities provided centralized management of ML artifacts and feature engineering pipelines, promoting collaboration and usability across teams. We established a robust data lake architecture, enabling efficient data ingestion, governance, and access control.
We started with the analysis of the current AI system and the organization's level of maturity in AI adoption, followed by conducting interviews with key project leaders and regional managers to gather insights. Ideation sessions were then conducted to generate creative ideas. Subsequently, based on the collected information and insights, an AI Master Plan is drafted, which includes prioritizing use cases and defining the operating model for implementing AI initiatives within the company.