Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process. They are building feature engineering pipelines as part of MLOps life cycle to transform data from various data sources and materialize the same for future consumption. Without feature stores, different teams across various business groups would maintain the above process independently, which can lead to conflicting and duplicated features in the system. Data scientists find it hard to search for and reuse existing features and it is painful to maintain version control. Furthermore, feature correctness violations related to online (inferencing) - offline (training) skews and data leakage are common. Although the machine learning community has extensively discussed the need for feature stores and their purpose [10, 11], this paper aims to capture the core architectural components that make up a managed feature store and to share the design learning in building such a system.

Real-time AI/ML use cases such as fraud prevention and recommendations are on the rise, and feature stores play a key role in deploying them successfully to production. According to popular open source feature store Feast, one of the most common questions users ask in their community Slack is: how scalable / performant is Feast? This is because the most important characteristic of a feature store for real-time AI/ML is the feature serving speed from the online store to the ML model for online predictions or scoring. Successful feature stores can meet stringent latency requirements (measured in milliseconds), consistently (think p99) and at scale (up to 100Ks of queries per second and even million of queries per second, and with gigabytes to terabytes sized datasets) while at the same time maintaining a low total cost of ownership and high accuracy. As we will see in this post, the choice of online feature store as well as the architecture of the feature store play important roles in determining how performant and cost effective it is.

In a machine learning (ML) journey, one crucial step before building any ML model is to transform your data and design features from your data so that your data can be machine-readable. This step is known as feature engineering. This can include one-hot encoding categorical variables, converting text values to vectorized representation, aggregating log data to a daily summary, and more. The quality of your features directly influences your model predictability, and often needs a few iterations until a model reaches an ideal level of accuracy. Data scientists and developers can easily spend 60% of their time designing and creating features, and the challenges go beyond writing and testing your feature engineering code.

Amazon SageMaker Feature Store is a new capability of Amazon SageMaker that helps data scientists and machine learning (ML) engineers securely store, discover, and share curated data used in training and prediction workflows. As organizations build data-driven applications using ML, they're constantly assembling and moving features between more and more functional teams. This constant movement of data can lead to inconsistencies in features and become a bottleneck when designing ML initiatives spanning multiple teams. For example, an ecommerce company might have several data science and engineering teams working on different aspects of their platform. The Core Search team focuses on query understanding and information retrieval tasks. The Product Success team solves problems involving customer reviews and feedback signals. The Personalization team uses clickstream and session data to create ML models for personalized recommendations.

The digital revolution has been increasingly enhancing the impact of marketing on management. Digital marketing is quite a broad term encompassing social media marketing, e-mail marketing, internet marketing, and search engine marketing, among others. While e-mails, social media, websites, and e-commerce, are tools that marketers use to promote services and products to their target audience, Artificial Intelligence (AI) is now intrinsically being used for digital marketing to map the right kind of promotional tools for specific market offerings. AI and Customer Value A combination of AI and digital marketing has been adopted extensively across sectors such as healthcare, financial services, retail, automobiles, education, and entertainment. As a case in point, the ability to track data makes it possible for retailers to develop strategies for a better understanding of consumer behaviour by keeping the user's purchase life-cycle at the center.

Delhi-based last mile delivery startup NOW Delivery is exploring artificial intelligence and developing a voice-based platform to help small merchants for orders. The company offers horizontal delivery solutions to hyperlocal business. "We are using artificial intelligence and machine learning for our order allocation which means we are devising an algorithm which would match the rider with the order. Apart from that we are also developing a voice-based platform for smaller merchants. There are voice-based speakers in the market, so the idea is to create a simple voice-based platform where a person can announce an order and get captured on our platform and we later passed it on to our rider," said Vivek Pandey, founder and CEO, NOW Delivery.

The field of eCommerce continues to be a hotbed for emerging technologies. On one level, online stores are competing with other online stores. But on another level, online stores have to compete with the conventional marketplace of physical stores. One of the key differentiators is artificial intelligence which is making significant inroads into addressing the inherent flaws of eCommerce. Earlier prototypes of artificial intelligence were known to be brittle and prone to wide margins of error.