Cloud Data Science

Defining Cloud Data Science

In simple terms, cloud data science means hosted services on the internet, which supports a wide range of data science work. Traditionally, data scientists used to work on data or do deployments from physical environments. Before starting their actual data science work, they would need to think and work on questions like, "What hardware do I need?" "Will my existing infrastructure support this new data science problem?" "What are the steps and skills I need to install and support this infrastructure?" Now, with the introduction of cloud computing, data scientists can seek almost all services, as per their requirements, from the cloud.

This is a big boost for the data science community and businesses that are seeking data science solutions. From cleaning data, to building models, or productionizing solutions, companies don't need to worry about what hardware should they buy, how to maintain the infrastructure for a model, or how many service providers they need. Additionally, they don't need to have a separate team for storing and maintaining data or doing deployments of machine learning models. Instead, they can seek the required services from the web.

Benefits of Cloud Data Science Over Traditional Data Science

Cost Saving
Only pay for what you use, which leads to major cost savings for a business. For example, let's say you need GPUs to run a model for a week. Instead of investing in expensive hardware, which you might not even need after one week of use, you can scale up your data science with easily accessible services on cloud and save money on infrastructure setup.
Flexible
Your requirements for computing resources may change as your business grows. One day, you may need a language support, and the next day, you may need to spin up high memory machines. You can choose the type of service you need from a variety of computing services provided on the cloud.
Customizable
Easy interfaces allow you to easily customize your resources for your workflow requirements. For example, you can customize your machines ranging from small CPUs to large GPUs, depending on how powerful you want your hardware to be.
Collaboration
Cloud Data Science makes collaboration between team members seamless. Data scientists can easily share their work with colleagues. This eliminates situations where code can't run for other teammates, or code becomes stale, and other critical issues.
Security
Along with a robust infrastructure, data scientists can rely on the cloud as a safe place for preprocessing their data and developing decision-driven models. Cloud providers add multi-layered security and monitor their infrastructure closely. Your data is off-premise which means that, unlike legacy systems, the risk of human interface is minimal.