More businesses are moving their data assets to the cloud. The data science models may need to be migrated to the cloud as well.
Businesses across the world are opting to put at least some of their data assets on the cloud. Many cloud providers have provisions for businesses to have a hybrid option of on-premise as well as cloud based applications. Increasingly, more and more of our customers are opting to build the initial data science models on premise but prefer the production models to be on the cloud. This has enabled them to automate more than 75% of the regular maintenance tasks and helped them maintain more models with without increasing the headcount of their data science team.
At FOYI, we help businesses to make the most of their cloud infrastructure. We do this by building automations and data pipelines on the cloud.
Types of Projects
Lift & Shift
The least impact option to move the existing models to the cloud is lift and shift.
We help businesses by firstly understanding the existing steps of data preprocessing, training and finally predicting. We then rewrite these steps in the cloud native process.
This helps the business in minimizing the impact on its operations during the transition to the cloud.
Enhance & Shift
The recommended option for cloud migration is the enhance and shift approach.
We help businesses by firstly understanding all the existing steps. Secondly, we rewrite these steps in the cloud native process. Finally, we work on improving the model performance iteratively.
This helps the business in leveraging the cloud migration project to also improve the model performance.
The option that takes the longest time to deliver is the end-to-end rebuild of the model on the cloud.
We help businesses by firstly understanding the project objective and, the consumers of the outcomes. Secondly, we work with the data team to understand the data. Finally, we build and deploy the model iteratively.
This helps the business build cloud native models. This is less of cloud migration and more of could build.