good first issue
Good for newcomers
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machine-learning-workflow
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AI Flow is an open source framework that bridges big data and artificial intelligence.
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Jun 21, 2022 - Python
BentoML Sample Projects Gallery 🎨
data-science
machine-learning
gallery
aws-lambda
serverless
machine-learning-library
model-management
azure-machine-learning
model-deployment
model-serving
machine-learning-workflow
gcp-cloud-functions
aws-sagemaker
bentoml
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Jun 22, 2022 - Jupyter Notebook
This repository demonstrated how you can use Github Actions to perform inference with your ML model
machine-learning
sklearn
inference
iris-dataset
machine-learning-workflow
github-actions
github-actions-docker
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Jun 22, 2022 - Jupyter Notebook
Machine Learning with Python can be used to predict the likelihood of events based on data. Includes the steps to be followed which applying machine learning to the problem statement.
machine-learning
naive-bayes
supervised-learning
logistic-regression
decision-trees
unsupervised-learning
machine-learning-workflow
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May 28, 2017 - Jupyter Notebook
Machine Learning Workflow
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Oct 15, 2018 - Jupyter Notebook
A curated list of awesome open source tools and commercial products that will help you train, deploy, monitor, version, scale, and secure your production machine learning on kubernetes 🚀
kubernetes
workflow
machine-learning
deep-learning
ml
orchestration
dataops
operator
machine-learning-pipelines
model-management
model-training
machine-learning-workflow
machine-learning-projects
machine-learning-training
mlops
ml-pipeline
model-monitoring
machine-learning-operations
ml-workbench
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Apr 20, 2022
Tutorial followed from the Understanding Machine Learning with Python pluralsight course by Jerry Kurata. Used the Machine Learning workflow to process and transform Pima Indian Diabetes data to create a prediction model that would predict which people are likely to develop diabetes with 70% or greater accuracy.
random-forest
naive-bayes
scikit-learn
pandas
logistic-regression
supervised-machine-learning
machine-learning-workflow
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Sep 21, 2017 - Jupyter Notebook
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We no longer need to control the number of concurrent kernels, since now we control the number of concurrent tasks