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# Title
MLOps: experiment tracking and monitoring in production
# Description
As the field of machine learning advances, managing and monitoring intelligent models in production, also known as ma…
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Hi, thanks for your great work!
I notice that you use SimpleTrack as baseline in your 'tracking by detection' experiment. So I am wondering if you could provide the config.yaml of SimpleTrack under …
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### Description
AWS SageMaker has recently released experiments tracking via a MLFlow Tracking Server.
### Requested Resource(s) and/or Data Source(s)
aws_sagemaker_mlflow_tracking_server
### Pote…
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Gitlab has introduced an integrated MLFlow backend as an experimental feature, which can be enabled apparently using this [guide](https://gitlab.com/gitlab-org/gitlab/-/issues/381660).
Apart from m…
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## Context and Problem Statement
In ML projects, we need to track datasets, models and experiments. MLOps is the process of tracking experiments and moving machine learning models into production s…
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#### Creating MLFlow server with custom s3 bucket as Docker container
Dockerfile
```
FROM python:3.9.7-slim
WORKDIR /mlflow/
COPY requirements.txt .
RUN pip install --no-cache-dir -r require…
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## Description
When serving a project statically via `kedro viz build`, I would like to turn off unused features:
- Experiment Tracking
- Publish and Share pipeline
- Autoreload
## Context
W…
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**Is your feature request related to a problem? Please describe.**
The model development process has been, at times, slow, and this has been made worse by having a less defined monitoring/tracking pr…
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I'm integrating fmeval with experiments tracking solutions (MLflow for now), and the lack of [callback mechanisms](https://www.askpython.com/python/built-in-methods/callback-functions-in-python) means…
acere updated
3 weeks ago
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# Context
Related:
- [x] https://github.com/kedro-org/kedro-viz/issues/1891
- [ ] https://github.com/kedro-org/kedro-viz/pull/1915
As Kedro-viz is introducing a default "SESSION_STORE_ARGS", we …