Lazy Loading Integrations in ZenML
November 26, 2021 - Baris Can Durak
When working on a project in Python, it is very likely that you will run into an issue where even the simplest of imports can lead to a chain of imports, which in turn can cost you a few seconds of run time before you even start to use what you imported. Left unchecked, this eager consumption of time can become even more apparent and annoying if you working on a project where the response time is critical and there are a wide variety of tools in play. Let’s put this into perspective for a tool that handles Machine Learning workflows in a production setting.
Pipeline Conversations: Our New Podcast
November 19, 2021 - Adam Probst
This first episode of Pipeline Conversations is the kick-off for a series of talks in the broader Machine Learning Start-Up Space. Our goal is not to promote any product or company but to discuss and also educate the community on different topics. We will invite thought leaders in the MLOps space, talk about how to build an open-source startup in public, and also the biggest challenges in Machine Learning. There won’t be a strict script and our guests will mainly determine the content.
Why ML should be written as pipelines from the get-go
March 31, 2021 - Hamza Tahir
Today, Machine Learning powers the top 1% of the most valuable organizations in the world (FB, ALPH, AMZ, N etc). However, 99% of enterprises struggle to productionalize ML, even with the possession of hyper-specific datasets and exceptional data science departments.
Spot the difference in ML costs
January 28th, 2021 - Hamza Tahir
Every organization at any scale understands that leveraging the public cloud is a trade-off between convenience and cost. While cloud providers like Google, Amazon and Microsoft have immensely reduced the barrier of entry for machine learning, GPU costs are still at a premium.
ZenML - Why we built it
December 21st, 2020 - ZenML Team
We did not start as an open-source Machine Learning tooling company. Our original goal was to transform the commercial vehicle industry with predictive analytics. After a few promising proofs-of-concept and projects on trucks we continuously expanded to other commercial vehicles - and other industries.
ZenML will be open source
November 11th, 2020 - ZenML Team
For the last months, we’ve been hard at work bringing ZenML to market as a commercial product. Our vision was and remains the commoditization of reproducible Machine Learning. Every pipeline for every model should forever be reproducible and have a deployable model at its end. All the invaluable lessons we’ve learned in this time, from customers, users, friends, and strangers, are now culminating in the next step towards our goal: We will make ZenML available under an Open Source License.
Avoiding technical debt with ML pipelines
June 6th, 2020 - Hamza Tahir
Okay, lets make it clear at the start: This post is NOT intended for people who are doing one-off, silo-ed projects like participating in Kaggle competitions, or doing hobby projects on jupyter notebooks to learn the trade. The value of throw-away, quick, diry script code is obvious there - and has its place. Rather, it is intended for ML practitioners working in a production setting. So if you’re working in a ML team that is struggling to manage technical debt while pumping out ML models, this one’s for you.
Predicting the winner of a DotA 2 match using distributed deep learning pipelines
May 7th, 2020 - Baris Can Durak
In the last decade, machine learning applications have proven their capabilities and potential in various applications. Especially in the past few years, they have gained rapid prominence in the gaming industry and now there are countless projects creating an endless array of models interacting with different games.
Deep Learning on 33,000,000 data points using a few lines of YAML
May 4th, 2020 - Hamza Tahir
Over the last few years at zenml, we have regularly dealt with datasets that contain millions of data points. Today, I want to write about how the we use our machine learning platform, ZenML, to build production-ready distributed training pipelines. These pipelines are capable of dealing with millions of datapoints in a matter of hours. If you also want to build large-scale deep learning pipelines, sign up for ZenML for free here and follow along.