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Top 8 Jupyter Notebook Automl Projects
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H2O
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Auto_TS
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.
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x-stable-diffusion
Real-time inference for Stable Diffusion - 0.88s latency. Covers AITemplate, nvFuser, TensorRT, FlashAttention. Join our Discord communty: https://discord.com/invite/TgHXuSJEk6
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automl-app
AutoML Web App - build Machine Learning pipeline in automatic way with Graphical User Interface (GUI). You can run app locally!
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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ToLD-Br
Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis
I would use H20 if I were you. You can try out LLMs with a nice GUI. Unless you have some familiarity with the tools needed to run these projects, it can be frustrating. https://h2o.ai/
I really like the simplicity of this framework, and they hit on a lot of common problems found in other agent-based frameworks. Most intrigued by the RAG improvements.
Seems like Microsoft was frustrated with the pace of movement in this space and the shitty results of agents (which admittedly kept my interest turned away from agents for the last few months). I'm interested again because it makes practical sense, and from looking at the example notebooks, seems fairly easy to integrate into existing applications.
Maybe this is the 'low code' approach that might actually work, and bridge together engineering and non-engineering resources.
This example was what caught my eye: https://github.com/microsoft/FLAML/blob/main/notebook/autoge...
The dataset is based on ToLD-Br, which is a huge dataset of tweets (or is it Xeets now?) that contains some additional info such as a classification if the text contains homophobia, obscenity, insults, racism, misogyny and xenophobia. The dataset for the competition, however, is a simple toxicity column.
Jupyter Notebook Automl related posts
- AutoGen: Enabling Next-Gen GPT-X Applications
- Show HN: Open-Source Web App with User Interface for AutoML on Tabular Data
- Slowdown / normalization on the Front Lines
- alguém sabe alguma coisa sobre AdaNetQuantum?
- Auto_TS: NEW Data - star count:558.0
- Auto_TS: NEW Data - star count:558.0
- Auto_TS: NEW Data - star count:558.0
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A note from our sponsor - WorkOS
workos.com | 20 Apr 2024
Index
What are some of the best open-source Automl projects in Jupyter Notebook? This list will help you:
Project | Stars | |
---|---|---|
1 | H2O | 6,721 |
2 | automl | 6,154 |
3 | FLAML | 3,663 |
4 | adanet | 3,470 |
5 | Auto_TS | 672 |
6 | x-stable-diffusion | 548 |
7 | automl-app | 133 |
8 | ToLD-Br | 32 |
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