Fernando Camargo
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Machine Learning Reproducibility: A Kaggle Competition Use-CaseEven though Reproducibility in Machine Learning is a theme that people hear
about now and then, we still see that people are practicing it only to a certain
degree. Even between Kaggle [https://www.kaggle.com/] competition winners, we
still see a lot of hard-to-reproduce code in Notebooks. Our goal here is to
outline some reproducibility elements and how we tackled them in a recent
competition.
First, what reproducibility stands for in Machine Learning? During a Machine
Learning project, we hav
Fernando CamargoDecember 16, 2020
The path to putting your ML model in productionSuppose you are a Data Scientist or Machine Learning Engineer (or another role
name of this kind). You took your time to analyze your dataset, clean it, and
prepare it to train your model. You then prepared many model candidates using
the most recent techniques and took your time to fine-tune them. After all this
extensive work, you finally created a model to be proud of. You finally finished
your job. Well, unfortunately, not. If your model never goes live and is
actively used, delivering value
Fernando CamargoNovember 24, 2020
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Articles
Potatoes Cluster: Training a Deep Learning Model Using a 100 Potatoes
Have you ever heard about battery potatoes? Did you know that it is also
possible to process information using potatoes? In this post we are going to
show you how it’s possible to create a cluster of potatoes, that not only serves
as a power supply but also processes data in a very cheap way. With that, we
could achieve the performance of a Nvidia GTX 1060 using 100 potatoes connected
with nails and clips.
Building the Cluster
In order to build our cluster, we are going to need some other item
Igor MunizApril 01, 2021
Machine Learning Reproducibility: A Kaggle Competition Use-CaseEven though Reproducibility in Machine Learning is a theme that people hear
about now and then, we still see that people are practicing it only to a certain
degree. Even between Kaggle [https://www.kaggle.com/] competition winners, we
still see a lot of hard-to-reproduce code in Notebooks. Our goal here is to
outline some reproducibility elements and how we tackled them in a recent
competition.
First, what reproducibility stands for in Machine Learning? During a Machine
Learning project, we hav
Fernando CamargoDecember 16, 2020
The path to putting your ML model in productionSuppose you are a Data Scientist or Machine Learning Engineer (or another role
name of this kind). You took your time to analyze your dataset, clean it, and
prepare it to train your model. You then prepared many model candidates using
the most recent techniques and took your time to fine-tune them. After all this
extensive work, you finally created a model to be proud of. You finally finished
your job. Well, unfortunately, not. If your model never goes live and is
actively used, delivering value
Fernando CamargoNovember 24, 2020
Ranking labs-of-origin for genetically engineered DNA using Metric LearningWith the constant advancements of genetic engineering, a common concern is to be
able to identify the lab-of-origin of genetically engineered DNA sequences. For
that reason, AltLabs has hosted the Genetic Engineering Attribution Challenge to
gather many teams to propose new tools to solve this problem. Here we show our
proposed method that aims to rank the most likely labs-of-origin and generate
embeddings for DNA sequences and labs. These embeddings can also be used to
perform various other tas
Fernando CamargoOctober 23, 2020