Is it Possible to Automate Automation?
Of late, data science has grabbed attention for good!
Organizations are increasingly motivated by the untapped potential automation offers to increase the number of AI projects and drastically reduce the time to market. One thing that even enterprises are not aware of is that automation designs can significantly increase the quality of solutions by training and retraining the models it is built on.
Automation has expanded to not only include any assortment of workflows supported by data science algorithms, but also entire data science itself. Automation as a utility is part of a greater revolution to democratize data science alongside SaaS solutions leverage machine learning, self-service analytics platforms, and visual solutions for business continuity.
Capability building to automate Data Science
Bots, the core component of automation deploy the following capabilities to harness maximum from Data Science:
• Natural Language Processing: NLP technologies work in tandem with deep learning tools enabling bots to understand applications written in different application frameworks and recognize workflows.
• Connectors: These are the mechanisms that allow bots to execute various steps required for their processes for instance data retrieval to work with varying operating systems.
• Computer Vision: Computer Vision enables bots to see just as human eyes would do. This lets bots to observe from a varied number of data sources, including apps and websites to get valuable data to support business use cases.
Marching ahead to the quest, IBM debuted Auto AI, an entirely new set of capabilities for its Watson Studio designed to automate critical yet time-consuming tasks associated with designing and optimizing AI in the enterprise. This liberates data scientists to execute more data science projections using automation to their advantage.
However, the current state of automation technology still encounters challenges around use cases that rely heavily on domain knowledge. With many technologies focusing on automating data science, it must be noted that though it is a good initiative to begin only a few of them answer a small fraction of automating the data science process.
Calling it too far out to put on a road map or science fiction would be a better term. To let this technology, define the progress fighting roadblocks given by actual business goals and constraints would require Artificial General Intelligence (defined as the hypothetical intelligence of a machine to understand and replicate any intellectual task that a human being can perform) and we have still a long way to go to harness its full potential.
Will Automation Kill Jobs?
The persistent question remains, would automation become strong enough to take away jobs? Experts Say No. Machine learning can only automate some part of the data scientist’s role. It is the job of the business enterprise to see what they want to automate and what should be handled by the manual workforce. This will lead data scientist’s handover the rule-based mundane, routine parts to the automation bots and instead focus on that which requires logical and intellectual shills which humans have to offer, for now.
1. Credit to automation, the role of data scientists is on way to democratization. With strong support from automation algorithms, software engineers, AI specialists, data engineers and business analysts, can work as one team along with data scientists.
2. The rise of Data Scientists, drag and drop data science tools will enable them to surpass data scientists in terms of the amount of intelligent analysis they produce and the consistent value derived from it.
In a crux, Automating the core aspect of data science is gradually becoming indispensable to digital transformation initiatives. Instead of a few data scientists training the models, Automation has taken data science to the next level by democratization and making data science available and accessible to all.