Recent posts by Mona

Large language models (e.g., GPT-4) seem poised to revolutionize the business world. It’s only a matter of time before many professions are transformed in some way by AI, as GPT can already generate functional code, review and draft legal documents, give tax advice, and turn hand-sketched diagrams into fully-functioning websites. Among the roles most likely to be affected by GPT are those involving sales, marketing, customer support, and media, although it’s almost impossible to imagine a domain that won’t in some way be affected by GPT. While certain tasks invariably demand a human touch, it’s likely that the focus of many roles will shift toward these key human endeavors and away from those that can be automated. With all this in mind, it’s pertinent to ask what challenges organizations are likely to encounter as they begin to invest in advanced AI and which roadblocks developers are likely to run up against as they work to incorporate GPT APIs into software products. While it is still too early to anticipate all possible hurdles teams using GPT are likely to experience, our understanding of AI and large language models suggest at least a few that will be particularly prominent.

More than ever before, people around the world are impacted by the advancement in AI. AI is becoming ubiquitous and it can be seen in healthcare, retail, finance, government, and practically anywhere imaginable. We use it to improve our lives in many ways such as automating our driving, detecting diseases more accurately, improving our understanding of the world, and even creating art. Lately, AI is becoming even more available and “democratized” with the rise of accessible generative AI such as ChatGPT.

In recent years, the term MLOps has become a buzzword in the world of AI, often discussed in the context of tools and technology. However, while much attention is given to the technical aspects of MLOps, what's often overlooked is the importance of the operations. There is often a lack of discussion around the operations needed for machine learning (ML) in production, and monitoring specifically. Things like accountability for AI performance, timely alerts for relevant stakeholders, the establishment of necessary processes to resolve issues, are often disregarded for discussions about specific tools and tech stacks. 

As the use of AI becomes more widespread across various industries, the need to monitor AI-driven applications for anomalies and unexpected behaviors has become increasingly important. Each use case is different and may require a unique set of fields and metrics to effectively identify and surface anomalous behaviors early before business is negatively impacted. At Mona, we are committed to providing the most advanced AI monitoring platform to improve the accuracy and reliability of AI-based systems. We have developed a one-of-a-kind insight generator designed to detect the specific data segment in which the anomaly lies, providing users with the full context of the behavior including possible explanations on why it is occurring. The key to successful AI monitoring lies in the ability to adhere to the intricacies of each use case, providing users with valuable insights to optimize their models and processes. Following numerous user requests, we just enhanced our monitoring capabilities to use geo-location and multiple timestamp fields in any monitoring context.