Distributed IT infrastructure across R&D applications, leading to high run cost & system complications.
Objective is to consolidate applications under one IT infrastructure to save costs.
- Developed a containerized solution using Kubernetes, to migrate parts of code that were incompatible with the new platform, resulting in highly scalable & flexible system.
Optimized the Machine Learning algorithm used by the system to train models, reducing run time to 1.5hr from 3hrs.
- Enhanced application UI, enabled the team to view the results of different ML models & compare results in a glance.
- Developed a critical module to view the application logs.
- Access & authorization controlled environment to allow users to add/discard data models, upload/download a new model, store and save the model in repository & use as needed.
- Activated session restoration capability resulting in 50% reduction in time taken to run the models on the system.
- Built Robust application to withstand server failure, network failure using latest cloud capabilities from Azure Cloud.
AT A GLANCE
- Incompatible code
- Complexity of containerization deployment for modules
- ML model optimization
- Legacy data migration
- Successful transition to cloud OTIF.
- Advanced ML algorithm for training Cost Savings worth 50K Euros/Year.
- Enhanced user experience.
- Slashed the run-time of application by 30% in the 1st month.
- Azure Cloud & Kubernetes
- Python, Django
- Azure DB
$ 0 K
Cost savings in 1st Year
New Features Deployed
$ 0 K
Savings In Run Cost
" With the help of AudvikLabs' cutting-edge cloud solution, our organization experienced a remarkable transformation of our legacy applications, resulting in unparalleled productivity and efficieny.