Algorithmia Blog - Deploying AI at scale

Acquiring Data for Document Classification

The Document Classifier is a powerful tool for generating keyword predictions for your documents, whether they are chat transcripts, emails, historical documents, scientific abstracts, or any number of other possible sources.

However, your predictions will only be as good as the dataset you’ve trained on. Since the Document Classifier algorithm supports retraining, this can be done in chunks right on the Algorithmia platform: grab a bunch of data, train your classifier on it, then come back immediately after or weeks later to add even more training data. Read More…

Train a Machine to Turn Documents into Keywords, via Document Classification


Figuring out the meaning of a document was once a very hard problem for computers to solve… even for humans, understanding the complexity of natural language can be tricky!

Fortunately, there are some great tools that can help address those concerns. The Document Classifier turns your existing documents and associated keywords into a model which can be used to predict the most appropriate keywords for new blocks of text. Read More…

Building a Timeline of your Video: Automatically Identify Objects, Sequence Times, and Integrate with Timeline.js

When we implemented InceptionNet, a microservice to detect and label objects (features) in photos, we knew it would be helpful. Then, we built out VideoMetadataExtraction, a video pipeline which allows you to run feature-detection algorithms (and others) on an entire video. This allowed for some really powerful activities — like automatically scanning through home security footage to find all the cars of a specific make & model, or stripping out all the nudity-containing scenes of a movie to make a G-rated version.

Today, we’ll go further by showing you how to visualize all the features in your video, thanks to the VideoTagSequencer and Timeline.js, a beautiful JavaScript library for displaying timelines on the web.

If you want to skip directly to the demo, please do. but come on back for a full breakdown of the integration pipeline and code samples! Read More…