Data augmentation can help an image ML model learn to handle variations of the image that are not in the training dataset. For example, it is likely that photographs provided to an ML model (especially if these are photographs by amateur photographers) will vary quite considerably in terms of lighting. We can therefore increase the effective size of the training dataset and make the ML model more resilient if we augment the training dataset by randomly changing the brightness, contrast, saturation, etc. of the training images.

While Keras has several built-in data augmentation layers (like RandomFlip), it doesn’t currently support changing the contrast and brightness. …

Increasingly, we see a move from building ETL pipelines (where much of the transformation is carried out in tools like Spark or Dataflow before the data is loaded into BigQuery) to ELT pipelines (where the transformation is carried out within BigQuery itself). The reasons are that (1) SQL is easier for business users to write (2) BigQuery scales better and is less expensive than alternative data processing technologies.

The problem with doing all the transformation code in SQL, though, is that it can become hard to maintain. How often have you come back to a project after a few months and been faced with a bunch of views, tables, user-defined functions, and scripts and scratched your head in bewilderment? …

When you are writing a BigQuery script, you will often want to make sure that the data matches your expectation. How many times have you written a script that worked when you deployed it. Then, someone changed your input data upstream and it was months before you discovered that the script was silently creating erroneous/empty tables? Wouldn’t it be better if the script failed and you got a heads up?

The script

Imagine that your script (you might be scheduling it, or it might be run on a trigger) creates a table of average duration of bicycle rides for some set of…

Embeddings in machine learning provide a way to create a concise, lower-dimensional representation of complex, unstructured data. Embeddings are commonly employed in natural language processing to represent words or sentences as numbers.

In an earlier article, I showed how to create a concise representation (50 numbers) of 1059x1799 HRRR images. In this article, I will show you that the embedding has some nice properties, and you can take advantage of these properties to implement use cases like compression, image search, interpolation, and clustering of large image datasets.

Compression

First of all, does the embedding capture the important information in the image? …

Autoencoder examples on the internet seem to be either about toy examples (MNIST, 28x28 images) or take advantage of transfer learning from ImageNet bottleneck layers. I will show you how to train an autoencoder from scratch, something that you will do if you have enough data and data that is completely unlike the photographs that ImageNet consists of.

In an earlier article, I showed how to take weather forecast images and create TensorFlow records out of them to make them ready for machine learning.

In this article, I will show how to do one machine learning task on these images: create concise representations of the radar reflectivity “analysis” fields from the High Resolution Rapid Refresh (HRRR) model using autoencoders. …

Many business processes, especially ones that involve interacting with customers and partners, involve paper forms. As consumers, we are used to filling out forms to apply for insurance, make insurance claims, specify healthcare preferences, apply for employment, tax withholdings, etc. Businesses on the other side of these transactions get a form that they need to parse, extract specific pieces of data from, and populate a database with.

The input form

Google Cloud Document AI comes with form-parsing capability. Let’s use it to parse a campaign disclosure form. These are forms that every US political campaign needs to file with the Federal Election Commission. …

If you have JPEG or PNG images, you can read them directly into TensorFlow using tf.io.decode_image. What if your data is in some industry-specific binary format?

Why HRRR to TensorFlow records?

The High Resolution Rapid Refresh (HRRR) model is a numerical weather model. Because weather models work best when countries all over the world pool their observations, the format for weather data is decided by the World Meteorological Organization and it is super-hard to change. So, the HRRR data is disseminated in a #@!$@&= binary format called GRIB.

Regardless of the industry you are in — manufacturing, electricity generation, pharmaceutical research, genomics, astronomy— you probably have some format like this. A format that no modern software framework supports. …

In scientific computations and simulations, you often have a really expensive calculation that you want to distribute and run in parallel. Ideally, we’d be able to deploy that calculation function to a serverless framework that will

• Run the calculation on a GPU
• Expose the function as a REST API
• Handle as many calculations as we throw at it, autoscaling as needed
• Abstract away all the complexities of Kubernetes, web servers, cluster management, etc.

Extracting such a function into a GPU-hosted microservice can allow you to greatly speed up your simulation software without having to rewrite the whole thing in CUDA…