THE NEED FOR DEVOPS IN DATA SCIENCE AND ITS APPLICATIONS
DevOps is a strategic concept that combines software development practice with the operations part of the software deployment in order to improve the process & provide continuous integration and delivery services.
Most of the cloud providers have dedicated services around them to have a seamless integration & service to the end customers. Some of the common providers are Amazon Web Services, Microsoft Azure and Google Cloud. These cloud service providers also support machine learning, image processing, GPU Computing & high volume data analysis.
Typical DevOps Structure
Advantages:
- Better Operational execution
- Increase in the flexibility of deployment
- Effective Collaboration working
- Cost-Effective MaintenanceÂ
- Lesser Capital deployment
- Streamlined Development & Deployment process
Disadvantages:
- Requires a cultural change in an organization
- Cross-Skilling expenditure
- Outsourcing becomes a little difficult For more info DevOps Training
The need for DevOps in Data science
As we are seeing, the entire data analytics industry has evolved over the last 5 years, hence the need for cost-effective & easy management of development practices has been an attentive topic. With more collaborative teams across the globe, it is essential for an organization to have a structured process around development for the end-users.
From a data science perspective, we see that there are more independent freelancers, consultants, remote teams who are working on various problems & challenges. There has to be a structured way of development, building the code, testing & deployment to the final stage.
Data science solutions are not going to be just a piece of code to work with. In order for the end-user to consume, the model has to work with a front-end application as well as the backend mechanism. As we see, there are 3 different development teams that are going to be integrated at a single point to run the business & provide benefits for the customer.
Application Flowchart
Apart from the above advantages levered from the cloud, we also have an efficient process of enabling the logging mechanism, cost management, building dashboards, deriving insights. Some of the services one can use on top of the existing requirements are:
- CloudWatch- Captures logs of the application runs
- IAM – For Security & User management
- Quicksight – Visualization of Scores & Metrics
- Cost Management- Keep control on Budgets & Spendings
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Cost Management in Data science Cloud Solutions
Most of the advanced algorithms such as CNN, GAN have a higher usage of computing & needs a lot of memory. With a regular infrastructure, it becomes a constraint & difficult for developers to run executions. In one of my previous experiences where we built Generative to come up with an artificial sample of images, it was very difficult to run in our computing environment.Â
The advent of cloud has enabled us to use more powerful infrastructure machines that have GPU support & can handle a large volume of data processing. Applications that depend on high-resolution images, audio and video data can be processed faster & building the required architecture, design and execution become easier. Purchasing such a powerful infrastructure is not cost-effective unless we use them on a regular basis & see value out of it. Most of the startups, SME & Mid-level organizations heavily rely on cloud solutions.