fellowship-prediction
MLOps
| fellowship-prediction | MLOps | |
|---|---|---|
| 1 | 1 | |
| 49 | 7 | |
| - | - | |
| 0.0 | 1.6 | |
| over 2 years ago | about 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
fellowship-prediction
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internships can vary greatly. What are some things to look for, for the VERY FIRST internship when you know very little?
Unfortunately, you can never tell what project will go through because it's all subjective. The only sure way to know is to try and apply. Share your achievements with the project and what you learned from it. In case you're wondering how your GitHub profile compares to an average MLH Fellow, we built this small app two weeks ago, called Fellowship Prediction: https://github.com/dtemir/fellowship-prediction
MLOps
What are some alternatives?
NLP-CNN-Subreddit-Sorter-Heroku-App - End-to-end development of an application using a convolutional neural network that suggests to users/moderators which technical subreddit a post actually belongs to. Novel method to determine # of CNN filters. Custom Word2vec embeddings. The subreddits chosen are all technical and similar, and benefit users/moderators interested in data science and related fields. (Exploratory data analysis, feature engineering, custom word2vec embeddings, convolutional neural network, deployment via flask to Heroku )
VevestaX - 2 Lines of code to track ML experiments + EDA + check into Github
bodywork-pipeline-with-aporia-monitoring - Integrating Aporia ML model monitoring into a Bodywork serving pipeline.
mlflow-deployments - Source code for the post Effortless deployments with MLFlow, showcasing how logging models using MLFLow can provide you want to easily deploy them in production later.