In production recommender systems, feature preprocessing must be faithfully replicated across training and inference environments. This often requires duplicating logic between offline and online environments, increasing engineering effort and introducing risks of dataset shift. We present Kamae, an open-source Python library that bridges this gap by translating PySpark preprocessing pipelines into equivalent Keras models. Kamae provides a suite of configurable Spark transformers and estimators, each mapped to a corresponding Keras layer, enabling consistent, end-to-end preprocessing across the ML lifecycle. Framework's utility is illustrated on real-world use cases, including MovieLens dataset and Expedia's Learning-to-Rank pipelines. The code is available at https://github.com/ExpediaGroup/kamae.

Deep learning models have become increasingly popular for a wide range of applications, including computer vision, natural language processing, and speech recognition. However, these models typically require large amounts of computational resources, making them challenging to run on low-power devices such as the Raspberry Pi. One approach to addressing this challenge is to use pruning techniques to reduce the size of the deep learning models. Pruning involves removing unimportant weights and connections from the model, resulting in a smaller and more efficient model. Pruning can be done during training or after the model has been trained. Another approach is to optimize the deep learning models specifically for the Raspberry Pi architecture. This can include optimizing the model's architecture and parameters to take advantage of the Raspberry Pi's hardware capabilities, such as its CPU and GPU. Additionally, the model can be optimized for energy efficiency by minimizing the amount of computation required. Pruning and optimizing deep learning models for the Raspberry Pi can help overcome the computational and energy constraints of low-power devices, making it possible to run deep learning models on a wider range of devices. In the following sections, we will explore these approaches in more detail and discuss their effectiveness for optimizing deep learning models for the Raspberry Pi.

In this Keras tutorial we learn about 3 Ways to Build Machine Learning Models in Keras You Must Know.

In my last post'GooglyPlusPlus now with Win Probability Analysis for all T20 matches' I had discussed the performance of my ML models, created with and without player embeddings, in computing the Win Probability of T20 matches. While the Random Forest gave excellent accuracy, it was bulky and also took an unusually long time to predict the Win Probability of a single T20 match. The above 2 ML models were built using R's Tidymodels. I had initially tried to use Tensorflow, Keras in Python but then abandoned it, since I did not know how to port the Deep Learning model to R and use in my app GooglyPlusPlus. But later, since I was stuck with a bulky Random Forest model, I decided to again explore options for saving the Keras Deep Learning model and loading it in R. I found out that saving the model as .h5, Hence, I rebuilt a Deep Learning model using Keras, Python with player embeddings and I got excellent performance.

And just to test out the model, let's go ahead and load the CIFAR-10 dataset and run model.compile

Does handling voluminous data to train your machine learning model give you nightmares? Do you know there is a way to find out similar song? Do you know how friendship recommendation in social networks works? I bet; this blog is worth a read then. The world of ML/AI has so evolved that it has started serving the sector of healthcare, security, life sciences and other domains. Eradicating time and manpower, AI business solutions have become self-sufficient.

The Keras Functional API provides a way to build flexible and complex neural networks in TensorFlow. The Functional API is used to design networks that are not linear. We used the Sequential API in the CNN tutorial to build an image classification model with Keras and TensorFlow. The Sequential API involves stacking layers. One layer is followed by another layer until the final dense layer.

Image segmentation entails partitioning image pixels into different classes. Some applications include identifying tumour regions in medical images, separating land and water areas in drone images, etc. Unlike classification, where CNNs output a class probability score vector, segmentation requires CNNs to output an image. Accordingly, traditional CNN architectures are tweaked to yield the desired result. An array of architectures, including transformers, are available to segment images.

Before we see how the tf.data API works, let's review how we usually train a Keras model. First, we need a dataset. An example is the fashion MNIST dataset that comes with the Keras API, which we have 60,000 training samples and 10,000 test samples of 28 28 pixels in grayscale and the corresponding classification label is encoded with integers 0 to 9. The dataset is a NumPy array. Then we can build a Keras model for classification, and with the model's fit() function, we provide the NumPy array as data.

The usage of machine learning models in production is now bigger than ever. One such popular library for creating powerful machine learning and deep learning models is Keras. However, the training process of these models is often very computationally expensive and lengthy, depending on the data at hand and the model architecture. Some models take weeks to months to train. This makes it so important to be able to store our models locally and retrieve them once again when we need to make predictions.