Operations | Monitoring | ITSM | DevOps | Cloud

Running machine learning (ML) inference in Edge devices close to where the data is generated offers several important advantages over running inference remotely in the cloud. These include real-time processing, lower cost, the ability to work without connectivity and with increased privacy.

The heart of any software development operation is infrastructure. This combination of virtual and physical assets ensures that the flow, storage, processing, and analysis of data remains efficient and as seamless as possible. When it comes to selecting a model for managing and deploying infrastructure, IT managers typically have two choices: infrastructure as code (IAC) or infrastructure as a service (IaaS).

With the launch of the Cycle Partner Program, we are committing to making it easier for companies to work with Cycle by creating more transparent and predictable relationships, offering training, resources, incentives, and benefits, some of which will roll out over time as the program evolves. Interested in partnering with Cycle? Contact our partner lead to schedule a meeting.

So, you’re looking for the right instance type for your public cloud workload, but how do you decide? Major cloud providers such as Amazon Web Services (AWS), Microsoft Azure Cloud and Google Cloud Platform (GCP) now offer such a large catalog of IaaS instances that it can become difficult to make sense of it all.

If you’ve worked with us before, you know we take security seriously. We take measures necessary to safeguard your sensitive and personally identifiable information and comply with a variety of compliance standards. These include maintaining best security practices and aligning with regulations such as SOC-2, PCI-DSS, and GDPR.