ClearScale Blog

From fraud detection and email filtering to self-driving cars and patient diagnosis, the potential applications of machine learning seem almost endless. But if you’re interested in deploying a machine learning (ML) application, where do you begin? Do you build it in-house or outsource it? What resources are required? What platform should you build it on? In the first of this three-blog series, we cover some of the key considerations for developing and deploying ML applications. The Basics of Machine

Before 2020, organizations all over the world were already adopting cloud-native technologies in droves. And then the pandemic hit…Cloud adoption soared, as business leaders looked for new ways to get more value out of existing IT assets. As a provider of professional cloud services, ClearScale witnessed this movement first hand. Our team worked alongside companies and nonprofit organizations on a myriad of cloud consulting services. We executed large-scale migrations, implemented serverless solutions, and packaged legacy apps in agile containers

Business and IT leaders all over the world are replacing legacy, on-premises technology with flexible, scalable, and cost-effective computing power in the cloud. From reducing IT costs to accelerating innovation, there are many compelling reasons to embark on a migration journey. However, making the transition is not easy without a well-developed plan and cloud expertise. Here, we highlight the eight biggest reasons why we see organizations migrate to a cloud platform, which include: Reducing IT costs Increasing business agility Improving

When it comes to upgrading legacy databases, there is no better answer than migrating to the cloud. The right cloud platform can easily deliver the availability, scalability, security, and cost-effectiveness of commercial databases without the high price tag. However, the prospect of migrating to the cloud can be intimidating for many business leaders. After all, a poor migration can put mission-critical workloads and sensitive customer data at risk. There is no room for error. Fortunately, IT teams don’t have

AWS re:Invent 2020 was jam-packed with cloud insights, tips, and demonstrations. We especially enjoyed learning more about how to leverage Kubernetes and optimize infrastructure for containerized applications on AWS. Our Technical Lead from the Systems Engineering team, Alexander Ivenin, sat in on sessions that covered a wide range of Kubernetes-related topics, including how to migrate legacy applications to containers and how to operate Kubernetes clusters with Amazon Elastic Kubernetes Service (EKS). We also learned best practices for how to

Many organizations that use Amazon Web Services (AWS) initially used AWS Lambda, Amazon’s Function-as-a-Service product, to automate workflows around running and scaling code. While this service does save time for developers, many companies have found challenges using AWS Lambda with more complex applications. To address these issues, Amazon launched AWS Step Functions, a serverless function orchestrator that simplifies the process of managing and sequencing Lambda functions. With AWS Step Functions, businesses can improve application resiliency, leverage other powerful AWS

At the 2020 AWS re:Invent there have been a lot of great sessions covering everything that’s new and improved in managed database services from Amazon Web Services (AWS). In this post, ClearScale’s Database Engineer, Renat Sunyaev, and Database Tech Lead, Sergey Kushnarev, summarize these exciting announcements. Over the past 15 years there was an incredible shift of application workloads from on-premises data centers or colocation facilities to cloud-based computing using services from AWS. Companies have been able

At the 2020 AWS re:Invent, Amazon Web Services (AWS) discussed best practices for application migration in sessions like The Cutover: Moving Your Traffic to the Cloud. The presenters discussed how each migration plan should contain 4 primary goals: Zero or minimum downtime Reduced risk of data loss Fast rollback Traffic and log monitoring Best Practices for Ensuring a Zero Downtime Migration To achieve these goals, it’s important to ensure that the traffic cutover is performed correctly. In ClearScale

Microservices architecture continues to be a trending topic in the application development world, largely because it makes apps easier to build, expand, and scale compared to traditional monolithic architecture. But there are numerous considerations that go into the use of microservices. One of them is how to package and deploy the services. In a previous blog, we discussed the use of containers for microservices. In this one, the focus is on serverless. Microservices and Serverless Defined What are microservices? As

The recent AWS re:Invent session, Best Practices for Running Microsoft Active Directory on AWS, featured a detailed overview of multi-region replication for AWS Managed Microsoft Active Directory. Vladimir Kolesnikov, Systems Tech Lead at ClearScale, provides his expert commentary on this exciting announcement. Overview Released in late November 2020, Amazon Web Services’ (AWS) new multi-region replication feature breaks down the wall of Active Directory (AD) managed service for many customers who wanted disaster recovery and fault tolerance of AD infrastructure