In this first course of the AI Product Management Specialization offered by Duke University's Pratt School of Engineering, you will build a foundational understanding of what machine learning is, how it works and when and why it is applied. To successfully manage an AI team or product and work collaboratively with data scientists, software engineers, and customers you need to understand the basics of machine learning technology. This course provides a non-coding introduction to machine learning, with focus on the process of developing models, ML model evaluation and interpretation, and the intuition behind common ML and deep learning algorithms. The course will conclude with a hands-on project in which you will have a chance to train and optimize a machine learning model on a simple real-world problem.

Payrailz, a digital payments company offering smarter, more engaging payment experiences to banks and credit unions across the United States, announced the appointment of Kavita Singh as its Vice President of AI Product Management. In this role, Singh joins the Payrailz executive management team, reporting to CEO Fran Duggan, where she will use her extensive background in financial services product management and artificial intelligence to further build out and develop Payrailz' AI product initiatives and features. Singh will also be responsible for further advancing the company's strategic "smarter payments vision" and oversee the go-to-market approaches for Payrailz' AI initiatives and experiences. "I am excited to continue my work in payments and to become an integral part of the Payrailz team as we work to make a real difference in the industry we serve. The use of AI and machine learning to enhance the payments experience is the wave of the future and I am impressed by what I've seen coming from Payrailz," said Singh.

We are witnessing a data labeling market explosion: labeling platforms have hit prime time. S&P Global released an October 11 report entitled *Avoiding Garbage in Machine Learning* in which it termed unlabeled data "garbage data" to highlight the importance of labeling in AI. The Economist recently noted that while spending on AI is growing from $38bn this year to $98bn in 2023, only 1 in 5 companies interested in AI has deployed machine learning models because of a shortage of labeled data. This is why "the market for data-labeling services may triple to $5bn by 2023." It is difficult not to notice the abundance of labeling startups being funded of late that are chasing after this market.