Operations | Monitoring | ITSM | DevOps | Cloud

When working with ML models, it’s fair to say that a model is only as good as the data it was trained on. Training and testing models on quality datasets of an appropriate size is essential for model performance. Because of the intricate link between a model and the data it was trained on, it’s also important to be able to store datasets and versioned models together.

Arguably the most important part of an on-call product is knowing that you will be notified when things break, wherever you are. When it comes to SMS and phone call notifications, we have to leave the familiar realm of the internet and JSON responses, and deal with systems that provide limited observability and insight into what’s gone wrong.

Most organizations migrate to the Amazon Web Services (AWS) public cloud from on-premises data centers. However, others have switched from another Infrastructure-as-a-Service (IaaS) platform to AWS. The most common reason we hear for choosing AWS is cost savings. As the largest public cloud provider, AWS leverages economies of scale, making its cloud services more affordable than those a company would have to build itself. Yet, not every organization can claim that AWS has saved it money.

API testing is a vital part of the development lifecycle. In this comparison piece, you’ll compare Speedscale and Hypertest and show where they stand in terms of API testing.

A significant part of being on-call is the ability to respond to pages and handle escalations on the go. In the early stages of developing incident.io On-call, we considered whether a Minimum Viable Product (MVP) could rely solely on SMS and phone calls. However, we quickly realized that a fully featured mobile app was going to be essential to the on-call experience. This led us to the question: how should we build this mobile app?