The cloud changed how businesses work, making things more flexible and adaptable. But keeping track of app performance from a user’s point of view in this new setup is tough. Legacy tools tend to not give developers an understanding of their users' perspective. That's where synthetic monitoring comes in. It's a strong way to focus on users and fix the problems that legacy tools miss.

In the last couple of years, the AI landscape has evolved from a researched-focused practice to a discipline delivering production-grade projects that are transforming operations across industries. Enterprises are growing their AI budgets, and are open to investing both in infrastructure and talent to accelerate their initiatives – so it’s the ideal time to make sure that you have a comprehensive toolkit for machine learning (ML).

Top tips is a weekly column where we highlight what’s trending in the tech world today and list out ways to explore these trends. This week, we’re examining four use cases for AI in the ever-growing FinTech sector. The FinTech sector has transformed the discussion around the financial services industry from top to bottom.

Amazon Bedrock is a fully managed service that offers foundation models (FMs) built by leading AI companies, such as AI21 labs, Meta, and Amazon along with other tools for building generative AI applications. After enabling access to validation and training data stored in Amazon S3, customers can fine-tune their FMs to invoke tasks such as text generation, content creation, and chatbot Q&A—without provisioning or managing any infrastructure.

Canonical, the publisher of Ubuntu, announced today the general availability of Charmed Kubeflow 1.8. Charmed Kubeflow is an open source, end-to-end MLOps platform that enables professionals to easily develop and deploy AI/ML models. It runs on any cloud, including hybrid cloud or multi-cloud scenarios. This latest release also offers the ability to run AI/ML workloads in air-gapped environments.

Learn the difference between HTTP and gRPC API mocking protocols, plus steps and considerations for implementing a gRPC mock server using Speedscale.