Julien Heiduk
Polars Tutorial Part 1: Efficient Data Manipulation Compared to Pandas
Polars offers a faster and more memory-efficient alternative to Pandas for data manipulation tasks, particularly with large datasets, due to its use of parallel processing, lazy evaluation, and Arrow memory format, making it an ideal tool for data engineers and scientists seeking enhanced performance.
Polars Tutorial Part 1: Efficient Data Manipulation Compared to PandasZenML Pipeline Tutorial Part 3: Understanding Artefacts and Adding Inference Steps
In this tutorial, we will dive deep into the concept of artefacts in ZenML pipelines. We’ll explain how to define and use them, with a focus on the newly added inference step in an existing pipeline…
ZenML Pipeline Tutorial Part 3: Understanding Artefacts and Adding Inference StepsZenML Pipeline Tutorial Part 2: Adding MLflow to a ZenML Pipeline
In this tutorial, we will integrate MLflow into a ZenML pipeline for tracking machine learning experiments. MLflow is an open-source platform to manage the ML lifecycle, including experimentation, reproducibility, and deployment…
ZenML Pipeline Tutorial Part 2: Adding MLflow to a ZenML PipelineZenML Pipeline Tutorial Part 1: How to Use ZenML for Building a Machine Learning Pipeline
In this tutorial, we will explore how to use ZenML to build a machine learning pipeline that performs data loading, model training, and evaluation…
ZenML Pipeline Tutorial Part 1: How to Use ZenML for Building a Machine Learning PipelineManage Product Compatibility with Machine Learning
The paper Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction by Rongzhi Zhang, Rebecca West, Xiquan Cui, and Chao Zhang presents an innovative method…
Manage Product Compatibility with Machine Learning