A BIG thank you to Amarita Natt, Sr. Economist at Econ One Research for writing this post and being part of DSSe, an initiative focused on elevating women in Data.
Guest post by Mısra Turp, Business Owner at So you want to be a data scientist. Changing jobs is stressful. But it doesn’t have to be. By following some simple tips, you can make sure you are on the right track and will be happy in your new position.
It’s no news that the tech industry is far from being gender-balanced. According to research from recruiting firm Burtch Works, not even 1 out of 5 data professionals are women. What is even more worrying is that the industry is still predominantly represented by white males while women of color.
Female-focused communities are a great place for women working in AI to meet, inspire, learn from each other and eventually work towards a more diverse tech industry.
With the recent advancements in machine learning (ML), organizations can use algorithms to positively influence every stage of the business growth cycle - from customer acquisition, to activation, retention and referral.
Retail and eCommerce companies are embracing AI and machine learning (ML) technologies in order to gain valuable insights, automate marketing, forecast demand and much more.
The days of walking miles up and down shopping streets to find an appropriate dress are long gone. Online services like Stitch Fix leverage data and algorithms to help customers find the clothes they love. Combining AI with the experience of expert stylists they can predict the style of shoppers.
ESG (Environmental, Social and Governance) investing is a great umbrella term to describe responsible investing (RI), a way to evaluate companies beyond financial factors. In other words, ESG is a way of measuring the impact of companies on the environment and society, thereby facilitating ethical.
MLOps—short for “machine learning operations”—has become a buzzword in recent years, and for good reason. Algorithmia’s 2021 enterprise trends in machine learning report found that while organizations have dramatically increased their investments in machine learning (ML), the time required to.