Vignan Baligari

Yeah. Hi. This is Vignan, and, yeah, I have 6 years of experience in IT. Out of that, I have 5 years of experience in AWS, DevOps. We use Terraform for our infrastructure creation. And, yeah, currently, I'm working for PEP Systems. When it comes to my roles and responsibilities, I'm a DevOps engineer in PEP Systems. When it comes to my responsibilities, we use infrastructure creation. We create infrastructure using Terraform to automate our configurations. And we use Linux systems for our VM deployments. And, when it comes to tech stack, we use GitHub Enterprise and Bitbucket for our SCM. And, when it comes to CICD, we use Jenkins. And, when it comes to build tools, we use Maven, PIP, and Docker. And we use Docker for our image building. And, also, we use EKS for our deployment platform. And, we have a few legacy applications also. Those applications are either deployed on VMs. And, when it comes to branching strategy, we use a master and feature branch strategy. When it comes to daily responsibilities, we mostly collaborate with the developers, and we discuss their branching strategies, deployment platforms, and Jenkins pipelines. Actually, we use shared libraries for our Jenkins pipelines. Like, we develop pipelines for each of our 2 deployment platforms. One is VM and one is EKS. We develop central pipelines, like a Java CI VM pipeline or a Java EKS pipeline or a Python EKS pipeline or a Python VM pipeline. And, yeah, when it comes to releasing that, we complete all the developer commits, and then it has to go through some pipelines, and finally, it gets deployed. Once the code is ready, we raise a change request, and developers need to educate and give all the information regarding the applications, scanning tests, everything. And once everything is passed and gets reviewed by both developers, leads, and our clients. And also, once we get any approval, then we support the military cutting deployment from production to production.

To manage secrets securely, it's crucial to manage them in our AWS cloud. We have a secret manager servicing our AWS cloud. So, we use this secret manager to secure our secrets. And when it comes to AWS, we have the secret manager there. So, we use the secret manager.

What would include in a Terraform model for usable multi cloud infrastructure components? I'm not aware of that.

How would you integrate Kubernetes RBS in an Azure? AWS identity system. I'm not experienced with Azure.

An automated approach to scale Kubernetes deployments in response to increased web traffic loads. They have to implement auto scaling. Auto scaling increases the scaling capacity. Like, we can implement HPA or Horizontal Pod Autoscaler. Thoughts are that this is an automated approach to scale Kubernetes deployments in response to increased traffic loads. We can implement HPA as auto scaling to achieve this.

By using the AWS CDK versus Terraform for infrastructure as code, let's focus on a specific use case like network provisioning. AWS CDK is a service used to create infrastructure in the AWS platform. However, it has limitations, such as requiring the use of only AWS to create infrastructure. In contrast, with Terraform, we can create any kind of infrastructure across all platforms and providers, including cloud platforms and their providers. Terraform is more flexible with automation and can focus on specific use cases, like network provisioning.

I plan to get the with the classes and look at what is wrong with this code that might fail the build process. I plan to get the classes with the correct syntax and look at what is wrong with this code that might fail the build process.

I assume that the actual security is clear. Yeah, my instance type is a key in where that deployment key. We pay a security group IDs. Thanks. Variable deployment key description. This is a key name to use for the instance. Type string, with a default value. We should not expose keys or any confidential matters like secrets. They're storing keys and variables. Once it gets exposed, it may be a security risk.

How would you design a system to auto scale content as to machine learning workloads in a hybrid cloud setup using Kubernetes? Content is the machine learning workloads. I'm not aware of, machine learning.

What methodologies would you apply in the DevOps life cycle to meet compliance standards like SOC 2 or GDPR while deploying applications? I did not work much on SOC and GDPR.

Your experience with setting up a distributed ML inference. I'm not experienced in ML and AWS SageMaker.