Say you’re looking for a smart product to detect anomalies in your organization’s IT environment. A sales rep drops by and shows you all kinds of great artificial intelligence (AI) features with fancy-sounding algorithms. It sounds very impressive and seems like there is a lot of very valuable AI in the product. But, in fact, the opposite is true. This is a manual AI product wrapped in a deceiving jacket. Let me tell you more.
Google Kubernetes Engine (GKE) is the preferred way to run Kubernetes on Google Cloud as it removes the operational overhead of managing the control plane. Earlier today, Google Cloud announced the general availability of GKE Autopilot, which manages your cluster’s entire infrastructure—both the control plane and worker nodes—so that you can spend more time building your applications.
DevOps came about as a result of ever-growing lags between development and operation. It’s a framework that deals with communication bottlenecks, allowing for smooth change management. DevOps monitoring is a crucial element and a necessity for this framework to succeed. Monitoring plays a vital role in realizing the underlying goals of DevOps. DevOps is all about eliminating technical inefficiencies and improving the speed of the whole cycle from development to deployment.
Security is paramount in today's digital world. Bad actors can use sensitive data to wreak havoc across thousands of machines in minutes if organizations do not have a solid cybersecurity strategy. Compliance requirements and regulations are increasingly calling for key management and strong encryption as part of a business's cybersecurity strategy. These are no longer optional but mandatory security requirements as DevOps also gains in popularity for agile development and application deployment.
Your continuous integration (CI) pipelines are at the core of the change management process for your applications. When set up correctly, the CI pipeline can automate many manual tasks to ensure that your application and the environments it runs in are consistent and repeatable. This pipeline can be an integral part of your security strategy if you use it to scan applications, containers, and infrastructure configuration for vulnerabilities.
Bringing a new tool into an organization is no small task. Adopting a CI/CD tool, or any other tool should follow a period of research, analysis and alignment within your organization. In my last post, I explained how the precursor to any successful tool adoption is about people: alignment on purpose, getting some “before” metrics to support your assessment, and setting expectations appropriately.
The CircleCI API provides a gateway for developers to retrieve detailed information about their pipelines, projects, and workflows, including which users are triggering the pipelines. This gives developers great control over their CI/CD process by supplying endpoints that can be called to fetch information and trigger processes remotely from the user’s applications or automation systems.
AI and Machine Learning (ML) are key priorities for enterprises, with a recent survey showing that 72% of CIOs expect to be heavy or moderate users of the technology. Unfortunately, other research has found that the vast majority—87%—of AI projects never make it into production. And even those that do often take 90 days or more to get there. Why this disconnect between intent and outcome? What are the roadblocks to enterprise ML? And what can be done about them?
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