This tutorial will help us to understand the concept, benefits and usage of AWS Single Sign-On with detail demo on how to configure AWS Single Sign-On using AWS SSO.

This video will cover the following topics:
– How to configure AWS SSO with Azure AD?
– Syncing users & groups from Azure AD.
– How to set up Permission Sets?
– Detailed DEMO of AWS SSO.

Reference link: https://awskarthik82.medium.com/single-sign-on-sso-between-aws-sso-and-java-saml-app-36704d989ee0

Learn more about AWS Single Sign On at – https://amzn.to/2JK75CT

AWS Single Sign-On (SSO) is a cloud SSO service that makes it easy to centrally manage SSO access to multiple AWS accounts and business applications, such as Salesforce, Box, and Office 365. Your users simply sign in to a user portal with credentials they configure in AWS SSO or using their existing corporate credentials to access all their assigned accounts and applications from one place.

With just a few clicks, you can enable a highly available SSO service without the upfront investment and on-going maintenance costs of operating your own SSO infrastructure. With AWS SSO, you can easily manage SSO access and user permissions to all of your accounts in AWS Organizations centrally. Further, by using the AWS SSO application configuration wizard, you can create Security Assertion Markup Language (SAML) 2.0 integrations and extend SSO access to any of your SAML-enabled applications.

If you like the video please like, comment, share and subscribe the channel to get more updates on technical videos.

Happy Learning !!!

#awssso #saml #sso #singlesignon

The term “tensor” is often misunderstood. Let’s figure out what they are through vector examples like velocity, angular momentum, the stress tensor, and the electromagnetic tensor.

A tensor is a generalization of vectors and matrices and is easily understood as a multidimensional array. It is a term and set of techniques known in machine learning in the training and operation of deep learning models can be described in terms of tensors.

Tensorflow, Tensorlab, Deep Tensorized Networks, Tensorized LSTMs… it’s no surprise that the word “tensor” is embedded in the names of many machine learning technologies. But what are tensors? And how do they relate to machine learning? In part one of Quick ML Concepts, I aim to provide a short yet concise summary of what tensors are.

Don’t let the word “tensor” scare you. It is nothing more than a simple mathematical concept. Tensors are mathematical objects that generalize scalars, vectors and matrices to higher dimensions. If you are familiar with basic linear algebra, you should have no trouble understanding what tensors are. In short, a single-dimensional tensor can be represented as a vector. A two-dimensional tensor, as you may have guessed, can be represented as a matrix.

#tensor #tensors

This Azure DevOps Pipeline Tutorial will give you a brief introduction on how you can implement DevOps practices on Microsoft Azure.

Following topics are covered in this live session:
1. What is Azure DevOps?
2. What is DevOps?
3. DevOps Azure Components
4. Demo: Building digital pipeline on Cloud

What is DevOps? This DevOps is the most famous word in the IT industry nowadays. People want to become DevOps engineers but they don’t know where to start. Most people think that DevOps is a programming language or technology that they can learn and become a DevOps engineer. But DevOps does not work in such a way.

DevOps is a mindset, a way of working for an organization so that new features reach the users as quickly as possible.
This video has a simple explanation of DevOps. if you want to be a DevOps engineer then it will be your first question. Azure tutorial | Azure DevOps Tutorial will help you to become an azure DevOps engineer.

In a single statement, Azure DevOps is everything you need to build your software product from beginning to end. Learn how Azure DevOps helps you plan your project with Agile tools, manages your code using Git, and deploys your code through the best CI/CD system on the planet. Get full traceability and visibility across all your development activities. In this video we will show you how to get started with Azure DevOps up and running with your project in under 15 minutes.

#azuredevops #azure

This AWS Athena Data Lake Tutorial shows how you can reduce your query processing time and cost by partitioning your data in S3 and using AWS Athena to leverage the partition feature.

# Learn AWS Athena with a demo.
# Learn AWS Glue.
# Different attributes of AWS S3 bucket.
# Specifying VPC & IAM role for Athena Data Lake.
# AWS Glue Deep Dive.
# Checking Athena logs.
# Understanding AWS Glue pricing.

► What is S3?
S3 stands for Simple Storage Service, an online storage service that allows you to store unlimited files in the cloud.

► Why would I use S3 instead of a regular host?
Regular shared hosting providers charge you a fixed amount every month. That’s regardless of the amount of files you store or the amount of visitors you get. Most cheaper webhosting companies also don’t cope well with high traffic websites. Suddenly got featured on Reddit or Hacker News? A regular hosting provider might suspend your account for all the traffic! With S3 you only pay for what you use and it’s highly scalable.

► What does it cost?
To give you an example of cost: when my blog receiver 1000 visitors per month I was paying just 6 cents to host it!

#amazonathena #awsathena #amazonwebservices #aws

Within machine learning, the hardest aspect often becomes deploying to production, until the time comes to address the issue. Applied at scale, this issue can hinder deployment, and at the worst, kill the project entirely.

In this video, we will learn:
– State of Machine Learning
– Why do ML projects & teams struggle to reach production?
– Working as a single data scientist at a startup
– ML project management
– Demo – Building a CI/ CD ML Pipeline
– Monitoring (tentative)
– Cloud best practices

Application: http://skirunrecommender.com/
Code: https://github.com/gregwchase/ski-recommender/tree/ski-recommender-v2

Deploying AI/ML based applications is far from trivial. On top of the traditional DevOps challenges, you need to foster collaboration between multidisciplinary teams (data-scientists, data/ML engineers, software developers and DevOps), handle model and experiment versioning, data versioning, etc. Most ML/AI deployments involve significant manual work, but this is changing with the introduction of new frameworks that leverage cloud-native paradigms, Git and Kubernetes to automate the process of ML/AI-based application deployment.

#MLOps #Machinelearning #CICD

This workshop is intended to be a thorough tutorial on how to build an application in EKS from start to finish, and explain the various resources Kubernetes has to offer.

Topics covered in this video:

1. AWS Managed:
– AWS Timestream to store Cryptocurrency data.
2. Kubernetes:
– Grafana with public internet access through an Ingress resource to view and visualize Cryptocurrency data.
– Cryptocurrency workers inside of the “crypto” namespace.
– Horizontal Pod Autoscaling to ensure self-healing.
– ReplicaSet to increase redundancy.
– DaemonSet to monitor application metrics with Prometheus.
– Grafana to visualize Prometheus metrics.
3. AGENDA:
– Intro/What is Kubernetes
– Set up KubeConfig/Deploy first EKS Cluster
– Build a basic application inside of EKS
– Explain Kubernetes resources in-depth
– Build a larger EKS application using the Cryptocurrency app
– Q&A (Come with questions)

For any questions/feedback/issues, please leave me a comment.
Thanks for watching this video and hope it was helpful. Please like and share it with your friends. Also don’t forget to subscribe to my channel.

Kubernetes becomes a complex environment with so many moving resources, monitoring even a small Kubernetes cluster is challenging. Monitoring Kubernetes cluster requires an in depth understanding of the application architecture and functionality in order to design and manage an effective solution.

Prometheus is an open-source systems monitoring and alerting toolkit originally built at SoundCloud. Since its inception in 2012, many companies and organizations have adopted Prometheus, and the project has a very active developer and user community. It is now a standalone open source project and maintained independently of any company. To emphasize this, and to clarify the project’s governance structure, Prometheus joined the Cloud Native Computing Foundation in 2016 as the second hosted project, after Kubernetes.

Grafana is a multi-platform open source analytics and interactive visualization web application. It provides charts, graphs, and alerts for the web when connected to supported data sources.

#Kubernetes #Grafana #Prometheus

A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it’s readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.

In this video, you’ll learn how to:

— Easily build a data lake environment on the AWS Cloud in minutes
— Load large volumes of disparate data into Amazon S3 without writing code
— Rapidly integrate your data lake with Amazon Redshift for analytics

Today’s organisations require a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Data Lake is a new and increasingly popular way to store all of your data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.

In this session we will discover how AWS gives you fast access to flexible and low-cost IT resources, so you can rapidly scale and build your data lake that can power any kind of analytics such as data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity and variety of data.

#datalake

This video is a walk through tutorial on how to create a user AWS Cognito user pool and connect using a mobile client iOS Using a hosted UI.

Source code:
https://github.com/muhaye/iOSCognitoClient

OpenID Connect 1.0 is a simple identity layer on top of the OAuth 2.0 protocol. It allows Clients to verify the identity of the End-User based on the authentication performed by an Authorization Server, as well as to obtain basic profile information about the End-User in an interoperable and REST-like manner.

OpenID Connect allows clients of all types, including Web-based, mobile, and JavaScript clients, to request and receive information about authenticated sessions and end-users. The specification suite is extensible, allowing participants to use optional features such as encryption of identity data, discovery of OpenID Providers, and session management, when it makes sense for them.

Amazon Cognito lets you add user sign-up, sign-in, and access control to your web and mobile apps quickly and easily. Amazon Cognito scales to millions of users and supports sign-in with social identity providers, such as Apple, Facebook, Google, and Amazon, and enterprise identity providers via SAML 2.0 and OpenID Connect.

#openid #oidc #openidconnect #cognito

This Localstack tutorial will show you what localstack is, how to install it, configure AWS CLI and a couple of examples with a local s3 bucket and creating a local dynamodb table.

Timestamps:
00:00 Intro
02:32 Installation
04:10 Docker Image Configuration
05:35 Aws cli configuration
06:08 Localstack S3 use
07:40 Localstack DynamoDb Table
9:57 Wrap up and Conclusion

Localstack is a very cool project that allows you to run AWS cloud services in your local machine.

Website: https://localstack.cloud/

#LocalStack #S3 #DynamoDB #CloudGuru

#transitgateway

This AWS Transit Gateway Tutorial consists of two parts; first concept and then demo. We will explain why companies should use Transit Gateway and then we have shown a step by step demo of how to implement Transit Gateway, how to attach VPCs to Transit Gateway, and then modify Route Tables to allow traffic. Then we provided a demo of how to peer two Transit Gateways between two different regions to connect VPCs across regions.

#AWS #Transit #Gateway is a network transit hub that you can use to interconnect your virtual private clouds (VPC) and on-premises networks.

The following are the key concepts for transit gateways:
#attachment
#transit gateway route table
#associations
#route propagation