gidim

6 ms·

1.
by gidim 2y ago
Really awesome seeing more people work on this! I’m one of the founders of Opik https://github.com/comet-ml/opik which does similar things but also has a UI and supports massive scale. Curious to hear if you have any f
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by gidim 4y ago
Excited to see more people building in this space. From what we've seen with customers it's critical to be able to compare what you're seeing in production to what you trained on (rather than historical period). That's a
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by gidim 6y ago
Hi HN! Excited to share with you our recent open source project CodeCarbon - a collaboration between Mila, BCG GAMMA, Haverford College, and Comet.ml. CodeCarbon records the amount of power being used by the underlying infrastructure from m
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Show HN: Code Carbon-Estimate and track carbon emissions from ML models training(github.com)
4 points by gidim 6y ago|1 comments
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by gidim 6y ago
Hey! I'm one of the founders at Comet.ml. We believe that Git should continue to be the approach for managing code (similar to dvc) but we adapted it to the ML workflow. Our approach is to compute a git patch on every run so later you
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by gidim 6y ago
Streamlit is awesome but solves a different use case. There's definitely similarities but also a few core differences. 1. Comet Panels reads the experimentation data from our API/Database. Streamlit is much more like Jupyter noteb
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by gidim 6y ago
Hi HN! Really excited to share this with with those of you training ML models. Comet Panels is an easy and free way to build visualizations, widgets and apps in HTML/JS/CSS on top of your ML experiment data and a gallery where y
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Show HN: Comet Panels – Custom visualizations for your ML experiments(comet.ml)
16 points by gidim 6y ago|3 comments
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Predictive Early Stopping – A Meta Learning Approach(comet.ml)
1 points by gidim 6y ago|0 comments
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How to Make Remote Work Effective for Data Science Teams(comet.ml)
3 points by gidim 6y ago|0 comments
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How to make remote work effective for data science teams(comet.ml)
2 points by gidim 6y ago|0 comments
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Estimating Uncertainty in Machine Learning Models(comet.ml)
2 points by gidim 7y ago|0 comments
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by gidim 7y ago
Also keep in mind that unlike tensorboard we keep your full data series available in the API and only downsample the charts to 15k points.
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by gidim 7y ago
M0zg do you mind sending me an email with your project? Happy to look into it. gideon a t comet.ml
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by gidim 7y ago
just to clarify - are you referring to this experiment? https://www.comet.ml/demo/urbansound8k/be09e32700cd435fb6b55...
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by gidim 7y ago
we're working on another version fixing the folds issue on Urbansound8k and will update the article asap.
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by gidim 7y ago
Hi M0zg! Gideon from Comet- sorry to hear you're having issues. Did you every try to report these? if you share more info at support@comet.ml or at our slack channel i'm sure we can fix it / improve. On a general note: 1. you
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Applying machine learning and deep learning methods to audio analysis(comet.ml)
93 points by gidim 7y ago|20 comments
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by gidim 7y ago
Really excited to see this relaunched. Every DS team has issues around dataset management. We previously shared a tutorial on how to get a fully reproducible pipeline with Quilt + Comet.ml https://blog.quiltdata.com/building
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by gidim 7y ago
Comet.ml - multiple engineering positions | New York City | Full-time | Onsite (NYC) or REMOTE https://www.comet.ml About us: Comet is doing for ML what Github did for code. We allow data science teams to automatically track the
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Uber releases Ludwig 0.2 with audio improvements, plus Comet.ml integration(venturebeat.com)
1 points by gidim 7y ago|0 comments
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Organizing machine learning projects: project management guidelines(medium.com)
1 points by gidim 7y ago|0 comments
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by gidim 7y ago
A common error is target leaking. An AutoML system will likely consider this a "strong feature". This is where having someone that actually understands the business domain is critical. There's no question that there's va
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The complete checklist for debugging neural networks(towardsdatascience.com)
10 points by gidim 8y ago|1 comments
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Checklist for Debugging Neural Networks(towardsdatascience.com)
7 points by gidim 8y ago|1 comments
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by gidim 8y ago
Comet.ml - multiple engineering positions | New York City | Full-time | Onsite (NYC) or REMOTE www.comet.ml About us: Comet is doing for ML what Github did for code. We allow data science teams to automatically track their datasets, co
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by gidim 8y ago
Sure you can reuse tools to achieve similar results. As with everything else the devil is in the details. Does your monitoring system saves results forever or it only let you report 90 days back? Can you compare two runs in a meaningful way
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by gidim 8y ago
Runs and experiments are not 1:1 mapped. A single container run could generate multiple experiments such as with the case of parameter search. Additionaly traditional tools for version control are not well suited for ML results and explorat
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by gidim 8y ago
Comet.ml | ONSITE | New York, NY | Senior Backend Engineer | Full-time | VISA | Comet.ml is doing for Machine Learning what GitHub did for code. We allow data science teams to automagically track their datasets, code changes, experimentatio
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by gidim 8y ago
My biggest issue with notebooks is that we're throwing aways years of best practices. Notebooks often lead to untested code with poor structure, frequent use of global variables and readability issues. Even using it as debugger is limi

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