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We have explored how we can deploy Machine Learning models using AWS Lambda. Deploying ML models with AWS Lambda is suitable for early-stage projects as there are certain limitations in using Lambda function. However, this is not a reason to worry if you need to utilize AWS Lambda to its full potential for your Machine Learning project. When working with Lambda functions its a constant worry about the size of deployment packages for a developer.

Working with Serverless computing is like riding an electric bike. You get speed, flexibility, automatic assistance to scale with ease. Development is usually hassle-free because you can focus on code and only pay for usage of the service. Except when your users hit an error. Debugging that issue feels like your bike’s battery just died while climbing a steep hill.

Many businesses use cloud services on AWS and also run workloads on Kubernetes and Knative. Today, it’s difficult to integrate events from AWS to workloads on a Rancher cluster, preventing you from taking full advantage of your data and applications. To trigger a workload on Rancher when events happen in your AWS service, you need an event source that can consume AWS events and send them to your Rancher workload.

Technology is evolving exponentially, and with it the size of the data that needs to be saved increases and the great need to access them quickly, easily and, above all, from anywhere. Every day that goes by, the use of cloud becomes even more essential for companies. For that reason, cloud computing services have started to emerge in order to meet these needs. Among them, Azure.

If it wasn’t clear before the global pandemic, it is now: Cloud applications can keep businesses moving forward. During this time, people have been able to shop, connect with friends and, of course, work from home.

In this article I’ll be sharing my own lessons learned with Dashbird in building reliable Well-Architected serverless applications and the highest impact areas to focus on no matter where you are in the Serverless journey.

The widespread shift to remote working in response to the COVID-19 pandemic has been a disruptive change for countless businesses; some 13% of organisations say they have faced major disruption. But at Canonical, remote working has long been the status quo for many of our teams. In spite of the challenging circumstances in which we all find ourselves, Canonical has been able to continue to operate and support our customers despite their working practices undergoing significant changes.

Working with the right AWS provider can make or break a company, but many companies discover that after a few years, what was once a great fit just doesn’t work anymore. Over time, your needs change, and the provider you’ve been with for years might not be able to provide what you need. Alternatively, maybe your AWS provider simply isn’t living up to your expectations, or perhaps your company is looking to redesign their cloud infrastructure. When this happens, what are your options?

When looking at an EBS volume in CloudWisdom, you’ll notice that in addition to the metrics we collect from AWS, we also create a number of computed metrics, one of which is netuitive.aws.ebs.iopsutilization. Simply put, IOPS Utilization compares the current number of IOPS that the disk is performing against the total IOPS capacity, and expresses this as a percentage. Thus, if you are currently running 1050 IOPS against a volume whose capacity is 3000 IOPS, the IOPS Utilization would be 35%.