Creating Custom Lambda Layers
In the late 2018 AWS announced two new features for Lambda to make serverless deployment much easier. They are:
· Lambda layers — which is a way to manage code and dependencies across multiple lambda functions.
· Lambda Runtime API — to develop lambda functions on any programming language or a specific language version.
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Policy Assessment Using ML
In the 21st century, every person and organization, both public and private, are somehow connected. So, being able to quickly understand and efficiently analyze whether your third-party policy documents such as NIST 800–171, ISO 27001, ISO 9001, etc., meet the standards you set for them is critical to the success of your business. Current policy assessment tools are manual, inefficient, and don’t adequately reduce risk.
We at Mindboard developed a platform to solve these problems. We are utilizing machine-learning, semantic technology, a repository of standard-meeting model documents we provide the most advanced and efficient methodology for automating and evaluating policy documents.
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Deploying deep learning models on AWS lambda
AWS Lambda is a serverless computing service provided by Amazon Web Services. The definition of serverless architecture is — it is a stateless compute container designed for event-driven solutions just like microservice architecture where monolithic applications are broken into simple smaller services which are easy to code, manage and scale.
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Serving Machine Learning Models Using TensorFlow Serving
Exploring how TensorFlow models can be served using TensorFlow Serving…
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Deploying Machine Learning Models Using Docker
Productionize the Flask API for deployment using Docker via nginx, gunicorn and Docker Compose to create a scalable template for deploying machine learning models.
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Time-Series Prediction Using A Simple RNN
For deeper networks, the obsession with image classification tasks seems to have also caused tutorials to appear on the more complex convolutional neural networks. This is great if you’re into that sort of thing, however, if someone is more interested in data with timeframes then recurrent neural networks (RNNs) come in handy.
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LSTM for Time Series Prediction — Part I
A time series contains a sequence of data points observed at specific intervals over time. A time series prediction uses a model to predict future values based on previously observed values. The natural temporal order of time series data makes analysis of time series different from cross-sectional or spatial data analyses, neither of which depends on a time component.
Time series predictions can be useful in a variety of settings, from processing signal data streaming from a sensor at an industrial site to monitoring trends in a financial market or maintaining inventory in a commercial setting. In all these scenarios, recent data can be used to inform predictions about future goal values.
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Crowd Density Estimation
In the light of problems caused due to poor crowd management, such as crowd crushes and blockages, there is an increasing need for computational models which can analyze highly dense crowds using video feeds from surveillance cameras. Crowd counting is a crucial component of such an automated crowd analysis system. This involves estimating the number of people in the crowd, as well as the distribution of the crowd density over the entire area of the gathering. Identifying regions with crowd density above the safety limit can help in issuing prior warnings and can prevent potential crowd crushes. Estimating the crowd count also helps in quantifying the significance of the event and better handling of logistics and infrastructure for the gathering.
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A deep learning solution to detect and mask human faces in videos
The objective of the project is to develop a smart deep-learning based solution, which possesses the capability to detect human faces in images / videos and mask them. The solution discussed in this article comprises of a set of open source tools and combining them together to form a pipeline, which processes a given video and outputs another video with faces masked in them.
Read the rest of the article at Mindboard’s Medium channel.
Blocking Inappropriate Images in Search Results
Most Internet porn filters apply the all-or-nothing approach to blocking websites and typically don’t block popular search engines, rightly so. These search engines are the key to the internet’s potential. While they are generally well protected using features like safe search, there are also glaring misses, especially when one looks at the images served up by the search. Seemingly innocent searches can throw up inappropriate images and several search engines optimize their performance by embedding images into the html as a data. This breaks most internet filters since filters operate by blocking entire websites using a static list.
Enter the vRate Chrome extension. By running within the browser, vRate has the ability to analyze content within the page, even if the image is embedded as data instead of a URL. vRate overcomes what traditional internet filters have failed at, by applying a combination of approaches to block inappropriate content. In addition to using static blacklists, vRate analyzes dynamic content on the page, specifically images and video thumbnails to filter inappropriate content. Seen below is a sample search result with vRate enabled.
We tried searching an “innocent” key word. In this case vRate replaced inappropriate images inline, this is because the search results for the most part were benign, except for the odd one or two. However, vRate can also automatically re-direct to a block page if the number of inappropriate images exceeds thresholds and this can be controlled through our sensitivity settings.
The Chrome extension is currently available for free preview, so do download and test drive it, especially check out how search results are handled.