CI/CD for generative AI applications presents unique challenges in model deployment, testing, and monitoring. Unlike traditional software applications, generative AI systems involve large model artifacts, complex dependencies, and specialized hardware requirements, making a sophisticated CI/CD pipeline essential for reliable delivery. As organizations embrace generative AI technologies, the need for specialized CI/CD solutions becomes critical.

For platform teams, success means empowering developers with efficient, reliable tools and workflows that accelerate software delivery. As organizations scale, platform engineering teams must balance developer autonomy with standardization while providing infrastructure that supports rapid, reliable software delivery. In today’s complex development environments, platform teams are crucial for maintaining productivity at scale.

Most modern web applications are frequently updated to implement new features, execute marketing campaigns, or enhance their UX with new libraries or APIs. While this helps you better engage your users, constant UI updates make designing flexible, long-lasting tests challenging.

Datadog Database Monitoring (DBM) Recommendations help you proactively optimize performance throughout your database fleet. DBM draws on a wide range of data sources in order to detect and provide actionable guidance on issues such as blocking queries, low disk space, and missing indexes. In this post, we’ll show you how DBM formulates targeted indexing recommendations to help you optimize database performance.

Today’s SRE and security operations center (SOC) teams often find themselves overwhelmed by the sheer volume and variety of logs generated by critical AWS services such as VPC Flow Logs, AWS WAF, and Amazon CloudFront. While these logs can be valuable for detecting and investigating security threats, as well as troubleshooting issues in your environment, managing them at scale can be challenging and costly.

Modern network operators need modern observability tools. In this post, we explore why Deepfield — a traditional network flow analytics platform — falls short in providing comprehensive insights required for today’s network operations, and how Kentik’s modern data platform is purpose-built for today’s infrastructure teams.

One of the cloud’s greatest advancements is its ability to scale resources like vCPU and memory based on demand. However, this auto-scaling can also lead to unexpected cost fluctuations — sometimes subtle, sometimes dramatic — especially with new or unpredictable workloads. That’s why cloud forecasting is essential. It helps you stay ahead of unpredictable costs by aligning your spending with business goals. How? We’ll break down how cloud cost forecasting works.

FSM is quite important for businesses today. Jobs included in FSM include installing, maintaining, and repairing services that are done away from the main office. To overcome the challenges of field service work, businesses need reliable tools to make processes better, reduce costs, and raise customer satisfaction. This is where Asset Infinity makes a difference with a new solution designed to address the issues related to managing field assets effectively and efficiently.

In the evolving and changing business landscape that demands the management of assets, availing them in support to operational success is associated with immense basic asset management need. It is no longer around tracking and maintaining assets but is about providing organizations within actionable insights useful to optimize usage, reduce costs, and thus improve decision-making.