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Lucjan Suski, Co-Founder & CEO of Surfer – Interview Series

Unite.AI

When did you initially get interested in search engine optimization? I also knew that SEOs use their tools heavily, and usage is directly connected to revenue, that’s why I started to dig deeper. Back in 2016, after a couple of years working as a Product Engineer, I started to entertain the idea of a startup as a technical co-founder.

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Data-centric ML benchmarking: Announcing DataPerf’s 2023 challenges

Google Research AI blog

To realize this potential, however, organizations need ML solutions to be reliable with ML solution development that is predictable and tractable. The process of creating high quality datasets is complicated and error-prone, from the initial selection and cleaning of raw data, to labeling the data and splitting it into training and test sets.

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How to Build an Experiment Tracking Tool [Learnings From Engineers Behind Neptune]

The MLOps Blog

This article is a summary of what we’ve learned from building and maintaining one of the most popular experiment trackers for the past five years. This article is a summary of what we’ve learned from building and maintaining one of the most popular experiment trackers for the past five years.

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Learnings From Building the ML Platform at Mailchimp

The MLOps Blog

How to transition from data analytics to MLOps engineering Piotr: Miki, you’ve been a data scientist, right? How did you manage to jump from a more analytical, scientific type of role to a more engineering one? In this episode, Mikiko Bazeley shares her learnings from building the ML Platform at Mailchimp.

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MLOps Is an Extension of DevOps. Not a Fork — My Thoughts on THE MLOPS Paper as an MLOps Startup CEO

The MLOps Blog

They tackle the ugly problem in the canonical MLOps movement: How do all those MLOps stack components actually relate to each other and work together? They tackle the ugly problem in the canonical MLOps movement: How do all those MLOps stack components actually relate to each other and work together? Some are my 3–4 year bets.

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Logging PyMC and Arviz Artifacts on Neptune

The MLOps Blog

The sheer amount of artifacts the iterative Bayesian modeling process generates can be challenging to keep organized. The sheer amount of artifacts the iterative Bayesian modeling process generates can be challenging to keep organized. Experiment trackers like neptune.ai Even though neptune.ai

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Best Machine Learning Datasets

Flipboard

In this post, we’ll show you the datasets you can use to build your machine learning projects. If you’re short on time, we’ll cut right to the chase here. We think the best datasets are available for free on Roboflow. After you create a free account, you’ll have access to the best machine learning datasets. Many call this software 2.0.