#
ml-platform
Here are 14 public repositories matching this topic...
data-science
machine-learning
reinforcement-learning
deep-learning
tensorflow
keras
collaboration
pytorch
hyperparameter-optimization
reproducibility
hyperparameter-tuning
data-versioning
hyperparameter-search
model-versioning
mlops
experiment-track
ml-platform
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Updated
Feb 1, 2022 - Python
The Unified Model Serving Framework 🍱
kubernetes
machine-learning
ai
aws-lambda
tensorflow
ml
model-management
model-deployment
model-serving
ml-infrastructure
azure-ml
mlops
aws-sagemaker
machine-learning-operations
bentoml
ml-platform
bentoml-format
prediction-service
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Updated
Feb 1, 2022 - Python
Determined: Deep Learning Training Platform
kubernetes
data-science
machine-learning
deep-learning
tensorflow
pytorch
hyperparameter-optimization
hyperparameter-tuning
hyperparameter-search
distributed-training
ml-infrastructure
mlops
ml-platform
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Updated
Feb 1, 2022 - Python
Neptune client library - integrate your Python scripts with Neptune
machine-learning
reinforcement-learning
deep-learning
metadata-store
data-version-control
metadata-management
data-versioning
model-repository
model-versioning
metadata-storage
mlops
model-monitoring
model-registry
ml-operations
production-ml
experiment-tracking
experiment-track
ml-platform
experiment-tracker
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Updated
Feb 1, 2022 - Python
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Updated
Jan 31, 2022 - TypeScript
LeonardSchuler
commented
Jul 3, 2021
While reading the code I found the following (minor) issue.
The aiohttp docs states: app.make_handler() is deprecated and will be removed in future aiohttp versions. Please use Application runners ins
coder46
commented
Feb 7, 2021
Currently you can only do training when you've committed and pushed all new changes in your branch. This introduces a blocker when a Data Scientist is trying out lots of different changes in their code.
Allow for training even with uncommitted changes. This can be done by taking a git diff of the current branch, storing it and then doing an git apply to the current branch during training
dbericat
commented
Jan 28, 2022
Add overview and map proper issues to it.
MONAI Deploy aims to become the de-facto standard for developing, packaging, testing, deploying and running medical AI applications in clinical production.
python
machine-learning
ai
deep-learning
inference
dicom
pytorch
healthcare
medical-imaging
fhir
guidelines
radiology
pathology
open-standard
mlops
ml-platform
monai
ai-application-development
ai-application-deployment
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Updated
Jan 25, 2022
Open-Source Machine Learning Platform
aws
machine-learning
machine-learning-platform
sagemaker
aws-sagemaker
continuous-training
ml-platform
cd4ml
open-source-ml-platform
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Updated
Oct 6, 2021 - Go
Model versioning using Weight&Biases with Python.
data-science
machine-learning
deep-learning
model
model-validation
model-deployment
model-versioning
mlops
experiment-tracking
ml-platform
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Updated
Jan 31, 2022 - Python
python
docker
machine-learning
dashboard
grafana
jupyter-notebook
prometheus
caddy
anomaly-detection
real-time-monitoring
mlops
real-time-machine-learning
fastapi
ml-engineering
ml-platform
ml-deployment
ml-monitoring
koushikvikram
koushik-vikram
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Updated
Nov 24, 2021 - Jupyter Notebook
Arup Dutta - Portfolio
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Updated
Aug 26, 2020
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Metaflow currently supports Py>=3.4 (with limited support for Py2.7) and R>=3.8. The GH tests only test for Py3.7 and R4.1. We should ensure we test on the whole set of permutations.