Artificial Intelligence, Machine Learning, Deep Learning, and GenAI for IT Operations

Wondering what’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? and how it can be used in IT Operations? well, it's quite a hot topic in the industry and you will see most of your friends talking about AI, ML, and DL. Let's see what it is and how it's useful for IT folks.

So, AI is too generic and DL is too specific. ML is the best suited for IT Operations. Here are a few of the use cases of Machine learning for IT Operations.

And finally, let's see what ML tools you can use. There are hundreds of tools available however there are a few I want to list here such as TensorFlow,  H2O, KNIME, OpenAI, etc..

Also, please check out my other posts related to this subject

• AIOps for Tenant and Platform Operations
• Exploratory data analysis using Python
• Cloud Computing and Data Analytics fundamentals
• IT Operations Management
• MTTIC

DBA as a Service - DBAaaS !

Wondering what will happen to DBA community in 2020? Well they will still be around! however  they will be doing cool stuff then ever before! Want to know what and how? please read...

By 2020, 50%+ of all Enterprise Data will be managed Autonomously and 80%+ of Application and Infrastructure Operations will be resolved Autonomously. Well this is quite possible with AI becoming reality and cementing it's foot in the industry. To move towards this direction, first of all we need to automate all the DBA tasks, and then later on implement Machine Learning so that database will take it's own decision based on what is going on in database without any / minimal involvement of DBA's. To automate all of those tasks, we need to develop framework which I call it as DBAaaS, define roles based access, and develop a DBAaaS Mobile App / Portal!

Here is quick Prototype of "DBAaaS 1.0" Mobile App developed by me in less than 2 hours using Rapid Prototype Development tool!

URL : https://gonative.io/share/rzydnx
User name : testing
Password : test

Pivotal Container Service (PKS), Kubernetes (K8s) , Dockers and Containers : in nutshell

In this blog, I will cover Pivotal Container Service (PKS), Kubernetes (K8s) , Dockers and Containers. Before we touch PKS, lets understand what is Dockers and Containers !

Once upon a time, there was Physical Server era, where in we used to have a very large server, install OS and install various applications on top of it! Then the Hypervisor Architecture was born, where in on the same server, you just need to install Hypervior which enables you to create multiple Virtual Machines and in each VM you can install OS & required App. Now there is a new Container era!

There are advantages and disadvantages of running containers directly on server. However most of the companies are taking advantages of Hpyervisor technology as well as Container technology, to build the next generation platforms.

Now lets look as Kubernetes, generally called as K8s. It's Orchestration tool for containers.

K8s cluster consists of 2 major parts, Master and Nodes. Nodes are some times called as Minions as well.
Master has 4 major parts.
1) kube-apiserver : Front-end to the control plane, exposes the API (REST) and Consumes JSON
2) Cluster store: Persistent storage for Cluster state and config, it uses etcd, the “source of truth” for the cluster and have a backup plan for it!
3) kube-controller-manager: Controller of controllers, Watches for changes & Helps maintain desired state
4) kube-scheduler : Watches apiserver for new pods, assigns work to nodes

Nodes has 3 major parts and runs Pod(s) inside them.
1) Kubelet : The main Kubernetes agent, registers node with cluster, watches apiserver, instantiates pods, reports back to master, exposes endpoint on :10255
2) Container Engine: Does container management such as Pulling images, Starting/stopping containers. Generally Docker, it can be rkt as well.
3) kube-proxy: Kubernetes networking, Pod IP addresses. All containers in a pod share a single IP. Load balances across all pods in a service

You can run multiple Pods in one node, and it is not typically recommended to run a large number of containers in a pod, it is a best practice to run a primary container along with additional containers to provide services to the primary container in a given pod.

And finally lets see, PKS !!
PKS gives IT teams the flexibility to deploy and consume Kubernetes on-premises with vSphere, or in the public cloud.  PKS 1.0 is currently supports vSphere and GCE. PKS leverages a specific BOSH release for K8s which has specific requirements.

Here are major components of PKS
1) PKS Controller : The control plane where you create, operate, scale, and Kubernetes clusters from the command line and API.
2) Built with open-source Kubernetes : Constant compatibility with GKE ensures access to the latest stable K8s releases.
3) BOSH : BOSH provides a reliable and consistent operational experience. For your Private cloud running on vSphere 6.5 or GCE Public Cloud.
4) Harbor : Harbor is your container repository
5) GCP Service Broker : The GCP Service Broker allows apps to transparently access Google Cloud APIs, from anywhere. Easily move workloads to/from Google Container Engine (GKE).
6) NSX-T : Network management and security out-of-the-box with VMware NSX-T. Multi-cloud, multi-hypervisor.