India's AI tech leaves something to be desired. Ironically, a sizable chunk of engineers working for tech companies like Google, Microsoft, Apple, Facebook and Amazon are Indians. The International Monetary Fund (IMF) has ranked India as the seventh-largest economy, down from the sixth position in 2020 and fifth in 2019. The relegation is chalked up to the pandemic crisis. Now, with the rising number of COVID-19 cases and deaths, India's future looks bleak.

Are you thinking of learning programming languages like C, Python or R to work on machine learning projects? AutoML could save you all the time and effort. Lately, Automated machine learning or AutoML has become a popular solution to build computer vision systems. The tech communities are awash with conversations around AutoML as to how it will change the way machine learning is done with limited or no coding knowledge. From autonomous vehicles to handwritten text recognition, face recognition, personalised recommendations, and diagnosing from x-ray images, computer vision is transforming industries globally.

The days of handcrafted algorithms aren't quite over, but it's hard to dismiss to impact that automated machine learning (AutoML) is having on the data science field. As companies look to imbue intelligence into their products and services, AutoML tools will lower the barrier of entry into data science and open the door for data-driven automation on vast scales. In the past few years, we've seen a surge of interest in AutoML tools, which automate a range of tasks in the data science workflow. While automated ML features may be found in a range of tools, the AutoML category has a fairly defined set of features, including: acquiring and prepping data; engineering features from the data; selecting the best algorithm; tuning the algorithm; and deployment and monitoring of production models. Forrester says just about every company will have a stand-alone AutoML tool.

The days of handcrafted algorithms aren't quite over, but it's hard to dismiss to impact that automated machine learning (AutoML) is having on the data science field. As companies look to imbue intelligence into their products and services, AutoML tools will lower the barrier of entry into data science and open the door for data-driven automation on vast scales. In the past few years, we've seen a surge of interest in AutoML tools, which automate a range of tasks in the data science workflow. While automated ML features may be found in a range of tools, the AutoML category has a fairly defined set of features, including: acquiring and prepping data; engineering features from the data; selecting the best algorithm; tuning the algorithm; and deployment and monitoring of production models. Forrester says just about every company will have a stand-alone AutoML tool.