Machine Learning Drift is a common phenomenon that occurs once the machine learning algorithm is deployed to production. It can adversely affect the overall performance of your machine learning model if not monitored closely and mitigated at the right time. This article will provide an overview of machine learning drift and various types of drift, as well as cover some practical techniques to eliminate drift. Machine learning and AI models are built on the assumption that historical data projects an accurate representation of the future. But in a fast-changing world, this is rarely the case.

Within the space of just two or three decades, artificial intelligence (AI) has left the pages of science fiction novels and become one of the cornerstone technologies of modern-day society. Success in machine learning (ML) has led to a torrent of new AI applications that are almost too numerous to count, from autonomous machines and biometrics to predictive analytics and chatbots. One emerging application of AI in recent years has been conversational intelligence (CI). While automated chatbots and virtual assistants are concerned with human-to-computer interaction, CI aims to explore human-to-human interaction in greater detail. The potential to monitor and extract data from human conversations, including tone, sentiment and context, has seemingly limitless potential.

"Just use AI!" While you may have heard this before, using artificial intelligence can seem like a lot of work. But it doesn't have to be, and there are many AI APIs out there ready for you to leverage. Check out some of them in Postman's latest featured list, Artificial Intelligence APIs, then get started straight away by forking any or all of these popular APIs to your own workspace. OpenAI is a non-profit AI research company whose goal is to advance digital intelligence. They've been widely talked about recently when they announced Codex, an AI that translates natural language to code.

A business's most valuable assets are its conversations – both internal conversations amongst employees and external revenue generating conversations with customers. Symbl is a comprehensive suite of APIs that analyze natural human conversations. It provides developers the ability to amplify these interactions and build extraordinary customer experiences in any channel – be it voice, video or text. Symbl is built on Contextual Conversation Intelligence (C2I) technology, enabling developers to rapidly integrate sophisticated artificial intelligence that goes beyond natural language processing (NLP) and text conversations. With Symbl, developers can automate contextual analysis of natural conversations without training/wake words and can provide real-time summary topics, action items, follow-ups, ideas, and questions.