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8 Managed Kubernetes Platform for Containerized Application

Some of the simplest cloud-based hosted Kubernetes to deploy and manage application containers.

Kubernetes is trending quite ever. And, why not – every organization is looking to containerize the appliance a little introduction

Kubernetes is an open-source, initially developed by Google for automatic deployment and managing the containerized applications. it's different than Docker.

Docker helps to create application containers, and Kubernetes group them for straightforward management. So, if you've got multiple containers, then you would like something to manage and find out them – that’s where Kubernetes helps. a number of the outs of the box features are:

Scale up or down with command, console, or automatically

Detached credential configuration management


Manage the workload and batch execution

Progressive application deployment

If you're a newbie, then you'll want to see this Docker and Kubernetes guide Udemy.

And, now let’s discuss the ways of using Kubernetes.

Technically, you'll either install, administer, and manage yourself or choose a managed solution. Doing everything in-house could also be expensive and challenging to seek out the proper skills for production management. If you're not prepared for that, you'll leverage the subsequently managed solutions.

Kubernetes Engine

A production-ready solution by Google Cloud. cash in of Google’s experience of running Gmail and YouTube for quite a decade.

Kubernetes Engine offer all-in-one solutions to deploy, update, manage, and monitor your applications. Not just the container apps, but you'll also run the database, attach storage to the cluster. With the auto-scaling features, you don’t need to manually increase the infrastructure capacity to handle the upcoming application traffics. information technology degree you'll configure to proportion when demand rises or scale down supported the usage. So, buy what you employ .nd take advantage of the great you can run Kubernetes behind a load balancer with anycast IP for better performance and secure them with network policies. Google Kubernetes Engine (GKE) is additionally available on-premises, and therefore the great point is you'll move your applications across cloud and on-premises. It's great flexibility, isn’t it?

Still in Beta but GKE supports GPU to supply better processing power to run machine learning and other heavy workloads.


DigitalOcean (DO) isn't just popular cloud hosting for developers, but recently they launched the managed Kubernetes platform and gained good popularity. And it’s affordable. you'll catch on started from as low as $10 per month. Let’s mention a number of features.

Run and scale all kinds of applications – integrate GitLab, web applications, API, backend-services, etc.

Configuration guide – it's a relatively new technology, and you'll not remember configuring them, so their getting started wizard would be useful guidance. Full API support – run Serverless frameworks, service mesh, integrate CI/CI, in-depth insights, etc.

Port application from DO to anywhere Kubernetes is support. Great for a multi-cloud strategy.

DO may be a great cost-effective option to run your applications on the cloud Kubernetes cluster.


An enterprise-ready Kubernetes as a service – Platform9 works on your favorite public cloud platform, on-premises, and VMware. It's a complete SaaS solution so you'll specialize in your application rather than continuous monitoring, infrastructure upgrade, and managing them.Platform9 offers high-availability across multiple public cloud availability zones so you'll operate a very global application without downtime, albeit you lose one availability zone. They got a simple to use the dashboard to manage multiple clusters and their services.

Play around on their Sandbox to ascertain how it works and the way you'll enjoy their solutions.


OpenShift by Red Hat supports an outsized number of container images, applications, frameworks, middleware, database. you'll run cloud-native or traditional applications on one platform. You can test drive their container platform for free of charge.

Amazon EKS

The list won’t be complete without including Amazon Elastic Container Service (EKS) for Kubernetes. employed by a number of reputed companies like Verizon, FICO, GoDaddy, Skyscanner, Pearson, Intuit – you can’t fail.

EKS runs Kubernetes on multiple AWS availability zones for high-availability, and AWS manages complete infrastructure. If you already use AWS for something else, then EKS would be an excellent option to integrate with CloudTrail, IAM, Cloud Map, App Mesh, ELB, etc.

Some of the good EKS features are:

Manage through web UI or CLI

Optimized AMI with NVIDIA drivers for advanced computational power

Run a cluster behind AWS load balancer

AWS EKS pricing is paid as you employ, and you'll catch on started from as low as $0.20 per hour.


These pioneer platform like Azure, AWS, GCP features a significant advantage – integration. If you're already on their platform, then it makes tons of sense to increase your application integration with their offering solution. Microsoft offers Azure Kubernetes Service (AKS), which is fully managed like others listed above.

Azure offers multiple ways to provision a cluster – web console, instruction, Azure resource manager, Terraform. you'll cash in of Azure traffic manager to route the appliance requests to the closest data centers for a quick response.

IBM Cloud

IBM Cloud Kubernetes service may be a certified KS8 provider and offers all the quality features to deploy an application within the Kubernetes cluster. you'll cash in on over 170 IBM Cloud services to modernize and build Blockchain, IoT, API, microservices, machine learning, analytics, etc. applications.

You can catch on started with their trial to experience the IBM Cloud platform.

Alibaba Cloud

Alibaba Cloud would be a superb choice for a business in China. Below may be a typical continuous delivery solution illustration for automatic DevOps, a uniform environment, and constant feedback. You can catch on started in FREE with Alibaba Cloud to making a Kubernetes cluster.


Most of the above listed hosted Kubernetes platform offer trial, so fiddle and see what works best for your application requirements. And, if you're curious to find out and manage it by yourself, then inspect this hands-on course.

Once your applications are containerized, then don’t forget to watch them with Kubernetes open-source tools.

How to Implement Google Managed Certificate on Cloud Load Balancer?

Let Google Cloud manage the SSL/TLS certificate for your website.

Google recently announced a managed certificate which you'll provision on Google Cloud load balancer. the great thing about using managed cert is that you simply don’t need to worry about creating a CSR and getting it signed regularly.

And, it's FREE.

Implementing managed cert is optional, and you'll always secure your site with a billboard certificate which I explained here.

So, let’s catch on started…

I assume you have already got Google cloud load balancer (if you would like help in creating then check this guide).

Log in to Cloud Console and navigate to Network service >> load balancing

Select the LB where you would like to implement Google managed cert and click on editIt seems like the default GCP SSL policy needs some customization — not excellent news.

But, don’t worry – you'll fix the way I did. The default GCP SSL policy is configured with minimum TLS 1.0, so my understanding is it should work on a browser that supports TLS 1.0 and better. Am I correct in saying this?

To make it work, I had to make a replacement SSL policy with TLS 1.2

Navigate to Network security >> SSL policies >> create policy

Enter the name, select version as TLS 1.2, compatible profile

Add target as a load balancer and save

How to Create a Load Balancer on Google Cloud?

Creating an HTTP(s) cloud load balancer on the Google Cloud Platform (GCP)

If you're hosting your applications on Google Cloud and searching for better high-availability, then you ought to try implementing a load balancer (LB). Google Cloud LB is a sensible .technology credit union

 It offers quite a standard one.

HTTP/2 enabled

Terminate SSL handshake

Custom SSL/TLS policies

Route traffic to the closest server

Path-based routing


and tons more…

Following, I even have two servers (one within the US and another one within the UK). Let’s create a load balancer and route traffic to both the servers. Since Google offers auto-scaling, you've got multiple options and choose what your business requires. However, during this article, I will be able to explain the way to create a load balancer using unmanaged instance groups that don’t support auto-scaling.

Create Instance Groups

All the servers should be inside the instance groups. So this is often a pre-requisite to making an LB.

Login to GCP Console

Navigate to Compute Engine >> Instance groups

Click create an instance group

Enter the name, select zone as one, a region where your servers are, unmanaged instance group, choose the server from VM instance drop-down, and click on Create

Create an HTTP(s) LB

Google offers three sorts of LB.

HTTP (s)



To manage web application traffic distribution, HTTP(s) is suitable. Let’s create that.

Navigate to Network Services >> load balancing

Click Create a load balance enter the LB name

On the backend configuration tab, select the drop-down and make a backend service

Enter the name and choose backend type as instance groups

Add both instance groups (server-us and server-UK)

Adjust the port number – port number of web server or application are going to be listening on the servers under checkup, click create

Enter the name, select the protocol, port

A checkup is important for LB to understand which instance is down, so it stops sending traffic. Below, I'm instructing LB to hit the server IP with port 80 every 10 seconds. If a server doesn’t respond 3 times consecutively then, LB will mark that instance down. As you'll see both instances are healthy and LB is technically operational.

Next, you bought to update your domain A record to point to the LB frontend IP. Once done, once you hit your domain, it should hit LB and distribute traffic to the instances.

I did some load tests and here is that the result. You can see the traffic from Europe is getting routed to the closest server located in London, and North America and Asia traffic are to the US server. the great thing is you don’t need to configure anything for geo traffic routing, it's the default feature. The above monitoring is out there under the backends tab.


Creating an LB is straightforward, and that I hope this provides you thought about it. there's tons of configuration you'll do to satisfy your application requirement like session affinity, CDN integration, SSL cert, etc. If you're exploring a choice to have a load balancer for your application, then fiddle and see how it helps.

Costing is predicated on usage so there's no monthly or annual locking. I feel the minimal usage would cost around $18 per month. If you're curious to find out about Google Cloud administration, then you'll consider taking this online course.

How to Find the External IP of Google Cloud VM?

You are locating external IP addresses within GCP Server.

Are you performing on a project where you would like to retrieve external (Internet/Public) IP of respective VM instance for application?

Good news – you'll quickly get them.

I am sure you'd have tried running ifconfig command. And, you'll notice the results contain only internal IP.

GCP and AWS, both have a friendly web interface where you'll see the general public IP, but if you need to urge them on a server directly, then the subsequent commands will assist you.

Getting External IP on GCP VM

There are two possible ways I'm conscious of. the primary one is employing a cloud command.

All about face recognition for Businesses

By Ankush Thakur on June 16, 2019

Posted in

Cloud Computing

Facial Recognition isn’t confined to the realms of computing. it's solid business applications.

One of the most well-liked buzzwords of this decade is face recognition.

It’s the part of applied machine learning which will detect and identify human faces, a drag that has been notoriously difficult for computers up to now. And this has burst open an entirely new world of exciting possibilities and challenges for businesses, governments, and individuals alike.

If you’re a baron and are wondering what the fuss is all about, and whether there’s some utility during this new development, we’ve got you covered. during this article, we’ll check out the history of face recognition, its development, current uses, controversies, deployment, and lots of more facets.

By the top of it, you’ll have a solid grasp of what face recognition technology is all about, and what its implications are for businesses.

Let’s get started!

Evolution of face recognition 

For all the hype and media coverage surrounding face recognition, the technology has been around for a few times. the primary serious algorithmic add detecting faces was the Viola-Jones Object Detection Framework published in 2001. Though a general-purpose framework for identifying objects within images, it had been quickly applied to face detection with excellent success. the most reason for this algorithm’s popularity was its speed; while the training process was excruciatingly slow, the detection process was extremely fast.

As early as 2001/2004, the typical personal computer running this algorithm was ready to process a 300px X 300px frame in 0.07 seconds (more here). The accuracy rates, though not like what humans are able to do, was impressive at 90%.

However, real progress wasn’t made until the last decade of 2010-2020, when Convolutional Neural Networks emerged because the best method to perform facial detection. the rationale was the supply of raw processing power and gigantic system memories made available through cloud computing by Infrastructure-as-a-Service (IaaS) providers. information technology degrees

 For the primary time in history, computers were consistently beating humans in recognizing faces, especially when an outsized number of random faces were involved.

How does face recognition work?

Facial recognition may be a multi-step process, with several specialized sub-systems involved.