Machine-Learning Enabled Production Modeling Platform
Our client had data science efforts dispersed across the organization, each with different levels of expertise, their own data sets, processes, and preferred tooling. Their ability to leverage data and advanced analytics is central to their ability to operate and compete in this marketplace. They envisioned an enterprise data platform enabling data sharing and advanced analytics across the organization to address this. This created friction in understanding common data, connecting analytics across teams, quickly bringing models to market, and managing those models once in production. This friction materially slowed the critical innovation rate in this industry.
When less becomes more
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).
Modern Platform
The new platform enables their data scientists to prototype, deploy and monitor production models rapidly.
Advanced Algorithms
Less data engineering, less infrastructure, less software engineering and less management required.
Faster and High Quality
Data scientists are now able to track rapidly and pull value from Machine Learning.
Digital platform & ecosystem
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.
Electric vehicle automaker and automotive technology company
- Software Engineer
- Data Engineer
- Quality Engineer
- Data Scientist
- DevOps
- Machine Learning Engineering
- ML Ops
- Data Engineering
Let’s make it happen.
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