AWS Lambda Adds Amazon Simple Queue Service to Supported Event Sources

We can now use Amazon Simple Queue Service (SQS) to trigger AWS Lambda functions! This is a stellar update with some key functionality that I’ve personally been looking forward to for more than 4 years. I know our customers are excited to take it for a spin so feel free to skip to the walk through section below if you don’t want a trip down memory lane.

SQS was the first service we ever launched with AWS back in 2004, 14 years ago. For some perspective, the largest commercial hard drives in 2004 were around 60GB, PHP 5 came out, Facebook had just launched, the TV show Friends ended, GMail was brand new, and I was still in high school. Looking back, I can see some of the tenets that make AWS what it is today were present even very early on in the development of SQS: fully managed, network accessible, pay-as-you-go, and no minimum commitments. Today, SQS is one of our most popular services used by hundreds of thousands of customers at absolutely massive scales as one of the fundamental building blocks of many applications.

AWS Lambda, by comparison, is a relative new kid on the block having been released at AWS re:Invent in 2014 (I was in the crowd that day!). Lambda is a compute service that lets you run code without provisioning or managing servers and it launched the serverless revolution back in 2014. It has seen immediate adoption across a wide array of use-cases from web and mobile backends to IT policy engines to data processing pipelines. Today, Lambda supports Node.js, Java, Go, C#, and Python runtimes letting customers minimize changes to existing codebases and giving them flexibility to build new ones. Over the past 4 years we’ve added a large number of features and event sources for Lambda making it easier for customers to just get things done. By adding support for SQS to Lambda we’re removing a lot of the undifferentiated heavy lifting of running a polling service or creating an SQS to SNS mapping.

Let’s take a look at how this all works.

https://aws.amazon.com/blogs/aws/aws-lambda-adds-amazon-simple-queue-service-to-supported-event-sources/

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Choosing the Right DynamoDB Partition Key

This blog post will cover important considerations and strategies for choosing the right partition key while migrating from a relational database to DynamoDB. This is an important step in the design  and building of scalable and reliable applications on top of DynamoDB.

What is a partition key?

DynamoDB supports two types of primary keys:

  • Partition key: Also known as a hash key, the partition key is composed of a single attribute. Attributes in DynamoDB are similar in many ways to fields or columns in other database systems.
  • Partition key and sort key: Referred to as a composite primary key or hash-range key, this type of key is composed of two attributes. The first attribute is the partition key, and the second attribute is the sort key

https://aws.amazon.com/blogs/database/choosing-the-right-dynamodb-partition-key/

A visual introduction to machine learning

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.

http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

Model Tuning and
the Bias-Variance Tradeoff

The goal of modeling is to approximate real-life situations by identifying and encoding patterns in data. Models make mistakes if those patterns are overly simple or overly complex.

http://www.r2d3.us/visual-intro-to-machine-learning-part-2/

Face recognition with OpenCV, Python, and deep learning

In today’s blog post you are going to learn how to perform face recognition in both images and video streams using:

  • OpenCV
  • Python
  • Deep learning

As we’ll see, the deep learning-based facial embeddings we’ll be using here today are both (1) highly accurate and (2) capable of being executed in real-time.

To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading!

https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/

AWS DeepLens: first impressions + tutorial

Getting up-and-running with Amazon’s new machine learning-enabled camera

tl;dr It’s awesome. Get one.

At the end of 2017, Amazon announced DeepLens, a camera with specialized hardware that allows developers to deploy machine learning and computer vision models to “the edge,” and integrate the data it collects with other AWS services.

On a whim, I put in a one-click order on Prime (devices started shipping just last week); it arrived a couple days later and just hours from unboxing — with one or two minor hiccups — I got it up-and-running and integrated with other AWS services. I’ve been pleasantly surprised, to say the least.

https://medium.com/@CUlstrup/aws-deeplens-first-impressions-tutorial-17e6d448d58d

Redis 4.0 Compatibility in Amazon ElastiCache

Amazon ElastiCache makes it easy for you to set up a fully managed in-memory data store and cache with Redis or Memcached. Today we’re pleased to launch compatibility with Redis 4.0 in ElastiCache. You can now launch Redis 4.0 compatible ElastiCache nodes or clusters, in all commercial AWS regions. ElastiCache Redis clusters can scale to terabytes of memory and millions of reads / writes per second to serve the most demanding needs of games, IoT devices, financial applications, and web applications.

https://aws.amazon.com/blogs/aws/new-redis-4-0-compatibility-in-amazon-elasticache

Machine Learning on AWS

Why machine learning on AWS?

Machine Learning for everyone

Whether you are a data scientist, ML researcher, or developer, AWS offers machine learning services and tools tailored to meet your needs and level of expertise.

API-driven ML services

Developers can easily add intelligence to any application with a diverse selection of pre-trained services that provide computer vision, speech, language analysis, and chatbot functionality.

Broad framework support

AWS supports all the major machine learning frameworks, including TensorFlow, Caffe2, and Apache MXNet, so that you can bring or develop any model you choose.

Breadth of compute options

AWS offers a broad array of compute options for training and inference with powerful GPU-based instances, compute and memory optimized instances, and even FPGAs.

Deep platform integrations

ML services are deeply integrated with the rest of the platform including the data lake and database tools you need to run ML workloads. A data lake on AWS gives you access to the most complete platform for big data.

Comprehensive analytics

Choose from a comprehensive set of services for data analysis including data warehousing, business intelligence, batch processing, stream processing, data workflow orchestration.

Secure

Control access to resources with granular permission policies. Storage and database services offer strong encryption to keep your data secure. Flexible key management options allow you to choose whether you or AWS will manage the encryption keys.

Pay-as-you-go

Consume services as you need them and only for the period you use them. AWS pricing has no upfront fees, termination penalties, or long term contracts. The AWS Free Tier helps you get started with AWS.