Have you considered introducing anomaly detection technology to your business? Anomaly detection is a technique used to identify rare items, events, or observations which raise suspicion by differing significantly from the majority of the data you are analyzing. The applications of anomaly detection are wide-ranging including the detection of abnormal purchases or cyber intrusions in banking, spotting a malignant tumor in an MRI scan, identifying fraudulent insurance claims, finding unusual machine behavior in manufacturing, and even detecting strange patterns in network traffic that could signal an intrusion.
There are many commercial products to do this, but you can easily implement an anomaly detection system by using Amazon SageMaker, AWS Glue, and AWS Lambda. Amazon SageMaker is a fully-managed platform to help you quickly build, train, and deploy machine learning models at any scale. AWS Glue is a fully-managed ETL service that makes it easy for you to prepare your data/model for analytics. AWS Lambda is a well-known a serverless real-time platform. Using these services, your model can be automatically updated with new data, and the new model can be used to alert for anomalies in real time with better accuracy.
In this blog post I’ll describe how you can use AWS Glue to prepare your data and train an anomaly detection model using Amazon SageMaker. For this exercise, I’ll store a sample of the NAB NYC Taxi data in Amazon DynamoDB to be streamed in real time using an AWS Lambda function.
The solution that I describe provides the following benefits:
- You can make the best use of existing resources for anomaly detection. For example, if you have been using Amazon DynamoDB Streams for disaster recovery (DR) or other purposes, you can use the data in that stream for anomaly detection. In addition, stand-by storage usually has low utilization. The data in low awareness can be used for training data.
- You can automatically retrain the model with new data on a regular basis with no user intervention.
- You can make it easy to use the Random Cut Forest built-in Amazon SageMaker algorithm. Amazon SageMaker offers flexible distributed training options that adjust to your specific workflows in a secure and scalable environment.
Recommender systems (or recommendation engines) are useful and interesting pieces of software. I wanted to compare recommender systems to each other but could not find a decent list, so here is the one I created. Please help me keep this post up-to-date by submitting corrections and additions via pull-request, or tweet me @grahamjenson.
If you haven’t heard, universities around the world are offering their courses online for free (or at least partially free). These courses are collectively called MOOCs or Massive Open Online Courses.
In the past six years or so, over 800 universities have created more than 10,000 of these MOOCs. And I’ve been keeping track of these MOOCs the entire time over at Class Central, ever since they rose to prominence.
In the past four months alone, 190 universities have announced 600 such free online courses. I’ve compiled a list of them and categorized them according to the following subjects: Computer Science, Mathematics, Programming, Data Science, Humanities, Social Sciences, Education & Teaching, Health & Medicine, Business, Personal Development, Engineering, Art & Design, and finally Science.
If you have trouble figuring out how to signup for Coursera courses for free, don’t worry — here’s an article on how to do that, too.
Many of these are completely self-paced, so you can start taking them at your convenience.
Imagine you could know the mood of the people on the Internet. Maybe you are not interested in its entirety, but only if people are today happy on your favorite social media platform. After this tutorial, you’ll be equipped to do this. While doing this, you will get a grasp of current advancements of (deep) neural networks and how they can be applied to text.
Reading the mood from text with machine learning is called sentiment analysis, and it is one of the prominent use cases in text classification. This falls into the very active research field of natural language processing (NLP). Other common use cases of text classification include detection of spam, auto tagging of customer queries, and categorization of text into defined topics. So how can you do this?
In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.
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.
In today’s blog post you are going to learn how to perform face recognition in both images and video streams using:
- 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!