The current approach towards machine learning and data science involves numerous mundane and time-consuming activities including cleansing, pre-processing, and selecting appropriate models. This makes data-science a resource-intensive activity which largely engages in repetitive activities. A direct fallout of this approach is delay in gaining insights and translating these insights into actions. In addition, scarcity of qualified data science experts further makes matters worse.
AutoML, the Catalyst for Change
All these challenges can be effectively resolved with automated machine learning, hailed as the catalyst for a fundamental change in the way machine learning and data science is approached. With an ability to use pre-built algorithms, businesses can leverage the baked-in expertise of data science without the need for internal coding. It is akin to an off-the-shelf solution that enables businesses to self-service their needs. Developers can tweak the pre-built algorithms according to their unique business needs. While AutoML does eliminate the need for coding and building machine learning models from scratch, it does require developers to have adequate data handling skills.
AutoML is driven by best practices from top data scientists and makes data science accessible to organizations of all sizes. This makes it easier for organizations to pull out relevant information from an ocean of data they possess fairly quickly. This frees up human resources to focus on more strategic business activities. In fact, Gartner has recently predicted that self-service analytics will empower human resources to actually produce more analysis when compared with professional data scientists—a great proposition from a human resource perspective.
Tech giants including Google, Amazon, and Microsoft are offering numerous tools to enable beginners get started with building machine learning models on their own. Apart from these large corporations there are other companies as well as start-ups that allow users to upload their data, select a few target variables, and hundreds of models are automatically generated. Users can choose the best fit for further analytics needs. Open source machine learning libraries are also available that provide developers with the building blocks to build their own algorithms.
With so much effort underway to democratize access to analytics powered by machine learning, its adoption will speed up in the near future.
The article has been written by Neetu Katyal, content and marketing consultant
She can be reached here.