How to Train a Classification Model with TensorFlow in 10 Minutes
Deep learning is everywhere. From sales forecasting to segmenting skin diseases on image data — there’s nothing deep learning algorithms can’t do, given quality data. If deep learning and TensorFlow are new to you, you’re in the right place. This article will show you the entire process of... Read more
Interactive Pipeline and Composite Estimators for Your End-to-End ML Model
A data science model development pipeline involves various components including data injection, data preprocessing, feature engineering, feature scaling, and modeling. A data scientist needs to write the learning and inference code for all the components. The code structure sometimes becomes messier and difficult to interpret for... Read more
MLOps V2 Solution Accelerator – Unifying MLOps at Microsoft
MLOps means different things to different people, however, the fundamental essence of MLOps is to deliver models into productions faster with a consistent, repeatable, and reliable approach. Machine Learning Operations (MLOps) is key to accelerating how data scientists and ML engineers can impact organizational needs. A... Read more
Area Under the Curve and Beyond with Integrated Discrimination Improvement and Net Reclassification
TLDR AUC is a good starting metric when comparing the performance of two models but it does not always tell the whole story NRI looks at the new models ability to correctly reclassify cancers and benigns and should be used alongside AUC IDI quantifies improvement of the slopes of... Read more
Decision Trees From Scratch With Python
Decision trees are one of the most intuitive machine learning algorithms used both for classification and regression. After reading, you’ll know how to implement a decision tree classifier entirely from scratch. This is the fifth of many upcoming from-scratch articles, so stay tuned to the blog... Read more
How to Interpret Any Machine Learning Prediction
Local Interpretable Model-agnostic Explanations (LIME) is a Python project developed by Ribeiro et al. to interpret the predictions of any supervised Machine Learning (ML) model. Most ML algorithms are black-boxes; we are unable to properly understand how they perform a specific prediction. This is a... Read more
Attention Mechanism in Seq2Seq and BiDAF – An Illustrated Guide
This article is the third in a series of four articles that aim to illustrate the working of Bi-Directional Attention Flow (BiDAF), a popular machine learning model for question and answering (Q&A). To recap, BiDAF is a closed-domain, extractive Q&A model. This means that to be able to answer... Read more
How to Use Large AI Models at Low Costs
Editor’s Note: James Demmel, PhD and Yang You, PhD are speakers for ODSC West 2022 coming this November 1st-3rd. Be sure to check out their talk, “Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training,” there! The success of the Transformer models has pushed the... Read more
Deploy a Machine Learning Model in Seconds
Editor’s note: Abubakar Abid, PhD is a speaker for ODSC West this November 1st-3rd. Be sure to check out his talk, “A Practical Tutorial on Building Machine Learning Demos with Gradio,” there! Several years ago, when I built my first machine learning model to classify handwritten... Read more
Many Models Training with Hyperparameter Optimization
This article presents an approach for you to train multiple machine learning models, optimizing the hyperparameters of each model in an automated way with Azure Machine Learning. Before getting into the part where I explain how to do this, let’s first get a better understanding of... Read more