High touch AI solution
Companies of all sizes can now tap into the power of AI with Amalgam's powerful and cost effective solutions.Create projectsWork on multiple problems at the same time, using the same or multiple datasets
Track experimentsSet performance goals, and track each experiments's progress in real time
Data cleaningAmalgam's intelligent solutions simplify and walks you through the data wrangling process step-by-step
What is Mercury?Mercury is a completely integrated and highly customizable solution designed to create AI models for problems of all sizes and nature. It runs on top of our proprietary and open source solutions to increase training speeds to levels unheard of in the industry. All of that, while running on state of the art hardware at a cost that you can afford.
What does that mean for your company?Our solutions offer your business the edge required to harness the power of AI to disrupt your industry at a cost effective manner. From predictions, to simulations, to decision making automation, your industry is waiting to be disrupted by AI and with Mercury you can achieve that in a cost-effective manner.
Open source
AurumAurum is a new and simplified approach for data scientists to keep track of data and code without having to get another PhD for it. It keeps track of all code and data changes, and lets you easily reproduce any experiment as well as easily compare metrics across experiments.
StrippingEasy to use pipeline solution for your AI/ML experiments, with a neat caching feature that allows you to skip the overhead and jump straight to the step that was changed most recently. It's almost like having Jupyter in the shell, but now you can use git and keep history of code changes.
AurumAurum is a new and simplified approach for data scientists to keep track of data and code without having to get another PhD for it. It keeps track of all code and data changes, and lets you easily reproduce any experiment as well as easily compare metrics across experiments.StrippingEasy to use pipeline solution for your AI/ML experiments, with a neat caching feature that allows you to skip the overhead and jump straight to the step that was changed most recently. It's almost like having Jupyter in the shell, but now you can use git and keep history of code changes.CompetitionsWe cover the whole range of AI solutions, from Data Center, to Optimized Hardware, to Models. Not only that, we keep pushing the State-of-the-art in all industries we come in. The proof is in the pudim, check our public competition results.
News
Consumer vs Server-Grade Hardware and why you can't do Business-Grade work with the formerIn any market or industry, quality work at scale requires purpose-built
high-performance tools.
In a previous article (The Eight Challenges You'll Face With On-Premise
Artificial Intelligence
[https://amalgam.ai/posts/the-eight-challenges-youll-face-with-on-premise-artificial-intelligence/]
), we promised to cover this topic more in-depth in a separate post. Well, we
like to keep our promises, so here we are.
It turns out that 3 of the Challenges we listed (Servers, Networking, and
Storage) ar
Distributed learning in an on-premise cluster - A Kaggle Reinforcement Learning case
Have you tried any distributed learning algorithms? If you are just starting out
in this area, I have my doubts, but if you have been on this path for a few
years, you might have faced one of those models. The incredible development of
the machine learning area in the last decade has not only brought a new state of
the art to several problems but has also taken processing optimization and
parallelization to another level. With increasingly larger models, any common
machine or even a single sup
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