Automation is taking over our lives and our future. Many things we do before, such as going to the bank to withdraw money or leaving the house to pay bills, are now almost laughable. People who have embraced automation do not even have to leave the spot they’re in to do these things. With a click or a tap, groceries arrive at the door and bill payments go through.
Experts perceive two possible futures in which automation changes everything. One involves more, better jobs after losing their old ones. The other involves robots taking over industries and taking away the jobs of people. These two possible scenarios are only possible because of the fact that automation is now an essential part of our daily lives.
Automation happens in data science and in machine learning.
Data Science Automation
Years ago, the extraction of information from large data sets is expensive. The company must build a data infrastructure that could cater to the needs of the data scientists. Highly skilled and specialized people should also be there to operate the tools. Presently, this usually doesn’t happen anymore. Because of automation in data science, cloud-based platforms make it much easier to maintain and develop large data infrastructures.
Data science has three main parts:
1. Modeling
2. Data processing
3. Deployment
Data processing is the most time consuming step. Modeling is more of a repetitive process. Deployment depends on the situation. The data scientist must try various algorithms until he or she reaches an optimal one. To come up with a more efficient process, innovative platforms were created.
These platforms make the following possible:
• They automate majority of the data processing
• They make overseeing developed data models and parameters easier
• They launch models and algorithms into production effortlessly
This automation doesn’t mean that the data scientists will all lose their jobs. They are still essential in building and interpreting models. The skills these experts have are difficult to find and expensive to acquire. When companies try o build projects on advanced analytics, the biggest challenge is hiring highly skilled data scientists.
Automated Machine Learning
This unending need for such professionals has prompted many companies to integrate automated machine learning into their data platforms. Because of this, there is only minimal intervention in entering data and getting resulting collection models.
Automated machine learning systems make it possible for predictive analytics open to a larger audience. These are powerful, helpful tools to help non-technical workers to solve simple problems on prediction.
Many tools have been created to automate the parts of data science workflow. Even so, the work of a data scientist cannot be fully automated. Automated data science and machine learning could never replace a data scientist’s ability to:
• Think of a means to create a solution
• Understand a company problem
• Listen to clients
Automation can help tool sets that non-technical people can use in working on analytical projects and presenting them to their clients.
