Edna  Bernhard

Edna Bernhard

1597761300

Deploy autoscaling ML model inference stack to AWS with CDK

For several years now, data science and machine learning were sexy and lots of companies embraced “artificial intelligence” as a new powerful tool to automate complex tasks as a black box. In this area, deep learning appears to be the Holy Grail to build models to detect and classify images or texts among other cool things. Once models are trained, it is common to deploy them on a small web server and expose a simple REST API to perform inference on a given sample. It is very convenient as usually, whereas training a model needs lots of computation power and lots of GPU, inference only needs a relatively low power CPU to make a prediction on a single sample. You could find numerous articles and blog posts on this approach; event open source projects and companies could be found to make the whole thing painless.

#data-science #technology #artificial-intelligence #machine-learning #programming

What is GEEK

Buddha Community

Deploy autoscaling ML model inference stack to AWS with CDK
Michael  Hamill

Michael Hamill

1617331277

Workshop Alert! Deep Learning Model Deployment & Management

The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.

Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.

Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.

In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.

#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop

Hertha  Mayer

Hertha Mayer

1595334123

Authentication In MEAN Stack - A Quick Guide

I consider myself an active StackOverflow user, despite my activity tends to vary depending on my daily workload. I enjoy answering questions with angular tag and I always try to create some working example to prove correctness of my answers.

To create angular demo I usually use either plunker or stackblitz or even jsfiddle. I like all of them but when I run into some errors I want to have a little bit more usable tool to undestand what’s going on.

Many people who ask questions on stackoverflow don’t want to isolate the problem and prepare minimal reproduction so they usually post all code to their questions on SO. They also tend to be not accurate and make a lot of mistakes in template syntax. To not waste a lot of time investigating where the error comes from I tried to create a tool that will help me to quickly find what causes the problem.

Angular demo runner
Online angular editor for building demo.
ng-run.com
<>

Let me show what I mean…

Template parser errors#

There are template parser errors that can be easy catched by stackblitz

It gives me some information but I want the error to be highlighted

#mean stack #angular 6 passport authentication #authentication in mean stack #full stack authentication #mean stack example application #mean stack login and registration angular 8 #mean stack login and registration angular 9 #mean stack tutorial #mean stack tutorial 2019 #passport.js

Christa  Stehr

Christa Stehr

1598408880

How To Unite AWS KMS with Serverless Application Model (SAM)

The Basics

AWS KMS is a Key Management Service that let you create Cryptographic keys that you can use to encrypt and decrypt data and also other keys. You can read more about it here.

Important points about Keys

Please note that the customer master keys(CMK) generated can only be used to encrypt small amount of data like passwords, RSA key. You can use AWS KMS CMKs to generate, encrypt, and decrypt data keys. However, AWS KMS does not store, manage, or track your data keys, or perform cryptographic operations with data keys.

You must use and manage data keys outside of AWS KMS. KMS API uses AWS KMS CMK in the encryption operations and they cannot accept more than 4 KB (4096 bytes) of data. To encrypt application data, use the server-side encryption features of an AWS service, or a client-side encryption library, such as the AWS Encryption SDK or the Amazon S3 encryption client.

Scenario

We want to create signup and login forms for a website.

Passwords should be encrypted and stored in DynamoDB database.

What do we need?

  1. KMS key to encrypt and decrypt data
  2. DynamoDB table to store password.
  3. Lambda functions & APIs to process Login and Sign up forms.
  4. Sign up/ Login forms in HTML.

Lets Implement it as Serverless Application Model (SAM)!

Lets first create the Key that we will use to encrypt and decrypt password.

KmsKey:
    Type: AWS::KMS::Key
    Properties: 
      Description: CMK for encrypting and decrypting
      KeyPolicy:
        Version: '2012-10-17'
        Id: key-default-1
        Statement:
        - Sid: Enable IAM User Permissions
          Effect: Allow
          Principal:
            AWS: !Sub arn:aws:iam::${AWS::AccountId}:root
          Action: kms:*
          Resource: '*'
        - Sid: Allow administration of the key
          Effect: Allow
          Principal:
            AWS: !Sub arn:aws:iam::${AWS::AccountId}:user/${KeyAdmin}
          Action:
          - kms:Create*
          - kms:Describe*
          - kms:Enable*
          - kms:List*
          - kms:Put*
          - kms:Update*
          - kms:Revoke*
          - kms:Disable*
          - kms:Get*
          - kms:Delete*
          - kms:ScheduleKeyDeletion
          - kms:CancelKeyDeletion
          Resource: '*'
        - Sid: Allow use of the key
          Effect: Allow
          Principal:
            AWS: !Sub arn:aws:iam::${AWS::AccountId}:user/${KeyUser}
          Action:
          - kms:DescribeKey
          - kms:Encrypt
          - kms:Decrypt
          - kms:ReEncrypt*
          - kms:GenerateDataKey
          - kms:GenerateDataKeyWithoutPlaintext
          Resource: '*'

The important thing in above snippet is the KeyPolicy. KMS requires a Key Administrator and Key User. As a best practice your Key Administrator and Key User should be 2 separate user in your Organisation. We are allowing all permissions to the root users.

So if your key Administrator leaves the organisation, the root user will be able to delete this key. As you can see **KeyAdmin **can manage the key but not use it and KeyUser can only use the key. ${KeyAdmin} and **${KeyUser} **are parameters in the SAM template.

You would be asked to provide values for these parameters during SAM Deploy.

#aws #serverless #aws-sam #aws-key-management-service #aws-certification #aws-api-gateway #tutorial-for-beginners #aws-blogs

Mckenzie  Osiki

Mckenzie Osiki

1623906928

How To Use “Model Stacking” To Improve Machine Learning Predictions

What is Model Stacking?

Model Stacking is a way to improve model predictions by combining the outputs of multiple models and running them through another machine learning model called a meta-learner. It is a popular strategy used to win kaggle competitions, but despite their usefulness they’re rarely talked about in data science articles — which I hope to change.

Essentially a stacked model works by running the output of multiple models through a “meta-learner” (usually a linear regressor/classifier, but can be other models like decision trees). The meta-learner attempts to minimize the weakness and maximize the strengths of every individual model. The result is usually a very robust model that generalizes well on unseen data.

The architecture for a stacked model can be illustrated by the image below:

#tensorflow #neural-networks #model-stacking #how to use “model stacking” to improve machine learning predictions #model stacking #machine learning

Seamus  Quitzon

Seamus Quitzon

1601341562

AWS Cost Allocation Tags and Cost Reduction

Bob had just arrived in the office for his first day of work as the newly hired chief technical officer when he was called into a conference room by the president, Martha, who immediately introduced him to the head of accounting, Amanda. They exchanged pleasantries, and then Martha got right down to business:

“Bob, we have several teams here developing software applications on Amazon and our bill is very high. We think it’s unnecessarily high, and we’d like you to look into it and bring it under control.”

Martha placed a screenshot of the Amazon Web Services (AWS) billing report on the table and pointed to it.

“This is a problem for us: We don’t know what we’re spending this money on, and we need to see more detail.”

Amanda chimed in, “Bob, look, we have financial dimensions that we use for reporting purposes, and I can provide you with some guidance regarding some information we’d really like to see such that the reports that are ultimately produced mirror these dimensions — if you can do this, it would really help us internally.”

“Bob, we can’t stress how important this is right now. These projects are becoming very expensive for our business,” Martha reiterated.

“How many projects do we have?” Bob inquired.

“We have four projects in total: two in the aviation division and two in the energy division. If it matters, the aviation division has 75 developers and the energy division has 25 developers,” the CEO responded.

Bob understood the problem and responded, “I’ll see what I can do and have some ideas. I might not be able to give you retrospective insight, but going forward, we should be able to get a better idea of what’s going on and start to bring the cost down.”

The meeting ended with Bob heading to find his desk. Cost allocation tags should help us, he thought to himself as he looked for someone who might know where his office is.

#aws #aws cloud #node js #cost optimization #aws cli #well architected framework #aws cost report #cost control #aws cost #aws tags