Category: Artificial Intelligence

Customers expect personalized contact center experiences. They want easy access to customer support and quick resolution of their issues. Delighting callers with a quick and easy interaction remains central to the customer experience (CX) strategy for support organizations. Enterprises often deploy omni-channel contact centers so that they can provide simple mechanisms for their customers to […]

Do you want to monitor your business metrics and detect anomalies in your existing streaming data pipelines? Amazon Lookout for Metrics is a service that uses machine learning (ML) to detect anomalies in your time series data. The service goes beyond simple anomaly detection. It allows developers to set up autonomous monitoring for important metrics […]

Regardless of the industry or product, customers are the most important component in a business’s success and growth. Businesses go to great lengths to acquire and more importantly retain their existing customers. Customer satisfaction links directly to revenue growth, business credibility, and reputation. These are all key factors in a sustainable and long-term business growth […]

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across multiple locations and content repositories within your organization. With Amazon Kendra, you can search through […]

July 2023: This post was reviewed for accuracy. The ability to scale machine learning operations (MLOps) at an enterprise is quickly becoming a competitive advantage in the modern economy. When firms started dabbling in ML, only the highest priority use cases were the focus. Businesses are now demanding more from ML practitioners: more intelligent features, […]

Receipts and invoices are documents that are critical to small and medium businesses (SMBs), startups, and enterprises for managing their accounts payable processes. These types of documents are difficult to process at scale because they follow no set design rules, yet any individual customer encounters thousands of distinct types of these documents. In this post, […]

There are multiple scenarios in which you may want to use computer vision to detect small objects or symbols within a given image. Whether it’s detecting company logos on grocery store shelves to manage inventory, detecting informative symbols on documents, or evaluating survey or quiz documents that contain checkmarks or shaded circles, the size ratio […]

The world we live in is constantly changing, and so is the data that is collected to build models. One of the problems that is often seen in production environments is that the deployed model doesn’t behave the same way as it did during the training phase. This concept is generally called data drift or […]

With machine learning (ML), more powerful technologies have become available that can automate the task of detecting visual anomalies in a product. However, implementing such ML solutions is time-consuming and expensive because it involves managing and setting up complex infrastructure and having the right ML skills. Furthermore, ML applications need human oversight to ensure accuracy […]

Every machine learning (ML) model demands data to train it. If your model isn’t predicting Titanic survival or iris species, then acquiring a dataset might be one of the most time-consuming parts of your model-building process—second only to data cleaning. What data cleaning looks like varies from dataset to dataset. For example, the following is […]