Keith Pijanowski

MLflow Tracking and MinIO

Keith Pijanowski Keith Pijanowski on AI/ML 3 August 2023

Introduction It’s challenging to keep track of machine learning experiments. Let’s say you have a collection of raw files in a MinIO bucket to be used to train and test a model. There will always be multiple ways to preprocess the data, engineer features, and design the model. Given all these options, you will want to run many

AI/ML Best Practices During a Gold Rush

Keith Pijanowski Keith Pijanowski on AI/ML 31 July 2023

Introduction The California Gold Rush started in 1848 and lasted until 1855. It is estimated that approximately 300,000 people migrated to California from other parts of the United States and abroad. Economic estimates suggest that, on average, only half made a modest profit. The other half either lost money or broke even. Very few gold seekers made a significant

Setting up a Development Machine with MLFlow and MinIO

Keith Pijanowski Keith Pijanowski on AI/ML 21 July 2023

About MLflow MLflow is an open-source platform designed to manage the complete machine learning lifecycle. Databricks created it as an internal project to address challenges faced in their own machine learning development and deployment processes. MLflow was later released as an open-source project in June 2018. As a tool for managing the complete lifecycle, MLflow contains the following components. * MLflow

Object Management for AI/ML

Keith Pijanowski Keith Pijanowski on AI/ML 29 June 2023

Introduction In a few previous posts on AI/ML, I mentioned that one of the benefits of MinIO is that you have tools for Versioning, Lifecycle Management, Object Locking, Object Retention and Legal Holds. These capabilities have a variety of uses. You may need a simple way to keep track of training experiments. You could also use these features to

Building an ML Training Pipeline with MinIO and Kubeflow v2.0

Keith Pijanowski Keith Pijanowski on AI/ML 20 June 2023

Introduction In a previous post, I covered Building an ML Data Pipeline with MinIO and Kubeflow v2.0. The data pipeline I created downloaded US Census data to a dedicated instance of MinIO. This is different from the MinIO instance Kubeflow Pipelines (KFP) uses internally. We could have tried to use KFP’s instance of MinIO - however, this is

The Architect’s Guide to Storage for AI

Keith Pijanowski Keith Pijanowski on AI/ML 15 June 2023

This post first appeared in The New Stack. Developers gravitate to technologies that are software defined, open source, cloud native and simple. That essentially defines object storage. Introduction Choosing the best storage for all phases of a machine learning (ML) project is critical. Research engineers need to create multiple versions of datasets and experiment with different model architectures. When a

Setting up a Development Machine with Kubeflow Pipelines 2.0 and MinIO

Keith Pijanowski Keith Pijanowski on AI/ML 2 June 2023

Engineers like to play and learn locally. It does not matter which tool is under investigation: a high-end storage solution, a workflow orchestration engine, or the latest thing in distributed computing. The best way to learn a new technology is to find a way to cram it all on a single machine so that you can put your hands on

Building an ML Data Pipeline with MinIO and Kubeflow v2.0

Keith Pijanowski Keith Pijanowski on AI/ML 25 May 2023

Kubeflow Pipelines (KFP) is the most popular feature of Kubeflow. A Python engineer can turn a function written in plain old Python into a component that runs in Kubernetes using the KFP decorators. If you used KFP v1, be warned - the programming model in KFP v2 is very different - however, it is a big improvement. Transforming plain old

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