Demo usage of Weights & Biases for ML Ops
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Updated
Jul 19, 2022 - Python
Demo usage of Weights & Biases for ML Ops
A simple example on how to provide ML model (DecissionTreeClassifier) as a REST Service. The app is containerize and deployed in Azure Cloud
A prefect extension that builds on top of the task decorator to reduce negative engineering!
A simple Python example of a Model Service that can be fronted by the Model Sidecar
This GitHub repository showcases the implementation of a comprehensive end-to-end MLOps pipeline using Amazon SageMaker pipelines to deploy and manage 100x machine learning models. The pipeline covers data pre-processing, model training/re-training, hyperparameter tuning, data quality check,model quality check, model registry, and model deployment.
A library of computer vision models and a streamlined framework for training them.
Dicoding Submission MLOps Heart Failure Detection using ML Pipeline, Heroku Deployment and Prometheus Monitoring
Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]
The DBT of ML, as Aligned describes data dependencies in ML systems, and reduce technical data debt
Find the samples, in the test data, on which your (generative) model makes mistakes.
Efficient streaming data ingestion, transformation & activation
Repo for running Whylogs as part of a CI workflow using github actions.
A pipeline to CI/CD of a machine learning model on Google Cloud Run
Fire up your models with the flame 🔥
An open-source ML pipeline development platform
Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
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