ML implementations tend to get complicated quickly. This article will explain how ML system can be split into as few services as possible. — Introduction After implementing several ML systems and running them in production, I realized there is a significant maintenance overload for monolithic ML apps. ML app code complexity grows exponentially. …

An end-to-end example of TensorFlow.js code flow for the data classification task. This app is built with React, but the same code can be reused with any JS toolkit/framework. — I thought it would be helpful to create a plain simple React application with a well-structured TensorFlow.js code logic for data classification. The main idea is that someone who would like to code logic and build a model with TensorFlow.js, should be able to copy-paste from my sample app easily…

Keras functional API provides an option to define Neural Network layers in a very flexible way. Developers have an option to create multiple outputs in a single model. This allows to minimize the number of models and improve code quality. — When implementing a slightly more complex use case with machine learning, very likely you may face the situation, when you would need multiple models for the same dataset. Take for example Boston housing dataset. This dataset comes with various features and there is one target attribute — Price. We would…

In this post, I show handy Python libraries to extract and process such information as price, date, and IBAN. It is hard to process this kind of data, but with proper libraries is simple. — It may look like a simple task to parse dates, currencies, and IBAN’s. But think for a moment about all the different combinations, locales, and formats. Parsing USA or German format dates, extracting decimal values from prices in EUR, USD, or Rupees. …

End-to-end example to explain how to fine-tune the Hugging Face model with a custom dataset using TensorFlow and Keras. I show how to save/load the trained model and execute the predict function with tokenized input. — There are many articles about Hugging Face fine-tuning with your own dataset. Many of the articles are using PyTorch, some are with TensorFlow. I had a task to implement sentiment classification based on a custom complaints dataset. I decided to go with Hugging Face transformers, as results were not great…

This post is about detecting text sentiment in an unsupervised way, using Hugging Face zero-shot text classification model. — A few weeks ago I was implementing POC with one of the requirements to be able to detect text sentiment in an unsupervised way (without having training data in advance and building a model). More specifically it was about data extraction. Based on some predefined topics, my task was to…

This post is about how I passed TensorFlow Developer Certificate exam. Also, it is about my journey to Machine Learning and my views about software development powered by Machine Learning. — About me I’m an enterprise software developer, who is jumping onto the Machine Learning train. For the past 15 years, I was doing independent Oracle consulting, mainly related to Oracle Developer tools and Java. I love to share my knowledge with the community, I posted 1030 blog entries with sample code from…

Anomaly detection with autoencoders for fraudulent health insurance claims. — This post is about unsupervised learning and about my research related to the topic of fraudulent claims detection in health insurance. There are several challenges related to fraudulent claims detection in health insurance. First, of them — there is no public data related to health insurance claims fraud, this is…

Bringing together all essential parts to build a simple, but powerful Machine Learning pipeline. This will cover Keras/TensorFlow model training, testing, auto re-training, and REST API — In this article, I’m going to cover multiple topics and explain how to build Machine Learning pipeline. What is ML pipeline? This is a solution that helps to re-train ML model automatically and make it available through API. …