ML experiment tracking with DagsHub, MLFlow, and DVC

• Data-Experiment_Tracking

  1 - -

You can find the code for this project in this repository.

• experiment-tracking

  1 0 10.0 Jupyter Notebook

  This project uses open-source tools(DagsHub, MLflow, DVC) to demonstrate the concept of "models/data management" workflow and process in the MLOps lifecycle

You can find the code for this project in this repository.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• MLflow

  56 17,335 9.9 Python

  Open source platform for the machine learning lifecycle

  

Here, we’ll implement the experimentation workflow using DagsHub, Google Colab, MLflow, and data version control (DVC). We’ll focus on how to do this without diving deep into the technicalities of building or designing a workbench from scratch. Going that route might increase the complexity involved, especially if you are in the early stages of understanding ML workflows, just working on a small project, or trying to implement a proof of concept.

• dvc

  109 13,166 9.6 Python

  🦉 ML Experiments and Data Management with Git

  

Here, we’ll implement the experimentation workflow using DagsHub, Google Colab, MLflow, and data version control (DVC). We’ll focus on how to do this without diving deep into the technicalities of building or designing a workbench from scratch. Going that route might increase the complexity involved, especially if you are in the early stages of understanding ML workflows, just working on a small project, or trying to implement a proof of concept.

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