Enabling AI as a service for enterprises to drive efficient business management
Concentrate on extracting value from data for a fraction of the cost and time that actually takes.
We helped them with:
- The development of a data platform that allows individuals and businesses to experiment with AI for a variety of reasons without a huge upfront investment and with less risk.
- Automating the entire AI/ML Pipeline to enable data scientists to stay focused on deriving value from the data.
Solution scope included:
- Built on open source: Used Kubeflow, SageMaker and Kubernetes for developing, orchestrating, deploying, and running scalable and portable MLOps pipelines.
- Embedded ML/DL/AI: Train models with incorporated open source AI learning system libraries to scale knowledge.
- Elastic deploy options: Build, manage, secure, and scale storage and compute resources quickly and easily in public, private, or your own data centres.
- Data Analytics in AWS using AWS glue, Redshift, Kinesis etc.
- Visualization using PowerBI, Tableau and AWS QuickSight.
- Increase capacity and output (end to end process and automation)
- Enables data scientists to carry out data and analytics tasks while encompassing visualization, interactive exploration, deployment, performance engineering data preparation, and data access.
- Improved transparency, explainability and reproducibility.
- It’s simple to set up new business or operational logic.
- The selection, training, and deployment of models are all automated.
- New models can be created, tested, and deployed quickly by data scientists.