Lightning Talk about sacred at PyData Berlin
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Updated
Jul 18, 2019 - Jupyter Notebook
Lightning Talk about sacred at PyData Berlin
Verta ai ModelDB on AWS Cloud with integration into Amazon SageMaker for ML training data versioning and experiment tracking
Faculty platform plugin for MLflow
A simple workspace to work with Weights & Biases, with automatic CSV dataframe generation
Source code for project with tour-tf-keras.
Example project with scikit-learn and neptune.
Example of how to use MATLAB Experiment Manager to test different classifiers for skin lesion classification using transfer learning.
Plug and play MLflow experiment tracking with Minio artifact store
🔬 Lightweight experiment and configuration manager for small ML/DL projects and Kaggling
Model versioning using Weight&Biases with Python.
List of experiment tracking resources and tools
GitHub Action That Retrieves Model Runs From Weights & Biases
A curated list of awesome open source and commercial MLOps platforms 🚀
Predicting if a lead would convert on an app, deployed using fastapi, with streamlit as frontend via CI/CD using GitHub Actions, and containerised using Docker
Custom ML tracking experiment and debugging tools.
Train and build a sentiment model using pytorch for fitness apps using Bert, dockerized and container deployed to the cloud(AWS)
An end to end ML project. Using MLflow for experiment tracking and model registry. Prefect for workflow orchestration. S3 for artifacts storage. AWS Lambda/ ECR for serverless model serving. AWS REST API gateway as endpoint to lambda function. GitHub Actions for CI/CD.
Hyperparameter optimization with Optuna and experiment tracking with NeptuneAI.
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