Electric vehicle automaker and automotive technology company
Dialexa stepped in and implemented AWS® Sagemaker® to sit on top of a managed EKS cluster as the core for an ecosystem approach to MLOps. This allowed teams to efficiently leverage different components (notebooks, shared data assets, and infrastructure) of the platform.
With this ecosystem approach, we enabled teams to choose how to best utilize the platform to their advantage, given the unique problems they were trying to solve. Marrying this with a service-centric organization to support those teams in their efforts was critical in driving adoption. A marketing customer propensity model was also built as a means of validating and demonstrating the value of the solution.
The result… a modern platform that enables their data scientists to prototype, deploy, and monitor production models rapidly, reducing the required effort to build and get value from ML and advanced algorithms. In the end, this meant less data engineering (prep for the models because of access to enterprise sources that already passed a quality control process), less infrastructure (access to a shared platform that would automatically scale up or down based on demand), less software engineering (pipelines for data and templates for API endpoints) and less management (automated drift detection and notification).
The new platform enables their data scientists to prototype, deploy and monitor production models rapidly.
Less data engineering, less infrastructure, less software engineering and less management required.
Data scientists are now able to track rapidly and pull value from Machine Learning.
The path to your next great product, invention or software application starts here. The first step is starting the conversation. Simply fill out this form, and we’ll reach out immediately.
"*" indicates required fields