MLflow
handson-ml2
MLflow | handson-ml2 | |
---|---|---|
56 | 12 | |
17,284 | 26,926 | |
1.3% | - | |
9.9 | 0.0 | |
3 days ago | 22 days ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MLflow
-
Observations on MLOps–A Fragmented Mosaic of Mismatched Expectations
How can this be? The current state of practice in AI/ML work requires adaptivity, which is uncommon in classical computational fields. There are myriad tools that capture the work across the many instances of the AI/ML lifecycle. The idea that any one tool could sufficiently capture the dynamic work is unrealistic. Take, for example, an experiment tracking tool like W&B or MLFlow; some form of experiment tracking is necessary in typical model training lifecycles. Such a tool requires some notion of a dataset. However, a tool focusing on experiment tracking is orthogonal to the needs of analyzing model performance at the data sample level, which is critical to understanding the failure modes of models. The way one does this depends on the type of data and the AI/ML task at hand. In other words, MLOps is inherently an intricate mosaic, as the capabilities and best practices of AI/ML work evolve.
-
My Favorite DevTools to Build AI/ML Applications!
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It includes features for experiment tracking, model versioning, and deployment, enabling developers to track and compare experiments, package models into reproducible runs, and manage model deployment across multiple environments.
-
Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
-
cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
-
EL5: Difference between OpenLLM, LangChain, MLFlow
MLFlow - http://mlflow.org
- Explain me how websites like Dall-E, chatgpt, thispersondoesntexit process the user data so quickly
- [D] What licensed software do you use for machine learning experimentation tracking?
-
Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
MLflow:
-
Options for configuration of python libraries - Stack Overflow
In search for a tool that needs comparable configuration I looked into mlflow and found this. https://github.com/mlflow/mlflow/blob/master/mlflow/environment_variables.py There they define a class _EnvironmentVariable and create many objects out of it, for any variable they need. The get method of this class is in principle a decorated os.getenv. Maybe that is something I can take as orientation.
-
[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
handson-ml2
-
Book recommendations for 18yo
A text on applied data science, if you like programming and diving into datasets, this could be a good thing to have, there's a pretty good one that's free on github here.
-
Moving to TensorFlow from PyTorch
I'd recommend a skim through the Keras/TensorFlow portion of Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow (https://github.com/ageron/handson-ml2)
-
Hands-on ML with Scikit-Learn, Keras and TF2 - Aurelien Geron (Details in comment)
Here's the GitHub repo for the 2nd Ed.
-
It was exactly 2 years ago when I decided to self-study data analytics and now I accepted a 6-digit offer.
Hands-on machine learning (Python): Python reference for machine learning. Use their Github repo as a supplement because some codes in the book are outdated. Finish at least part 1: Fundamentals of machine learning.
-
I want to relearn machine learning
You get access from the github, https://github.com/ageron/handson-ml2 Its free, but wont have much context without the book(also "free" at Libgen.is)
-
Tensorflow error "W tensorflow/core/data/root_dataset.cc:163] Optimization loop failed: CANCELLED: Operation was cancelled"
Here is the repository.
-
NLP resources
I remember an NLP course on DataCamp being helpful as an intro, but a resource I keep handy is Hands-On Machine Learning (Geron) which has really helpful follow along notebooks on the git. Then when you want some background: Deep Learning (Goodfellow)
-
An OpenAI Gym docker that can render on Windows
example/18_reinforcement_learning.ipynb: This is a copy from Chapter 18 in Géron, Aurélien's book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Source code is here in GitHub.
-
[D] Thoughts on Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow by Geron
Have you tried looking at the accompanying github repo.
-
[D] Looking for good refreshers on stats / ML to go back to the ML engineer interview game after 2 years doing mostly Software.
Much of the material from that book is publicly available in this repo maintained by the author.
What are some alternatives?
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
mit-deep-learning-book-pdf - MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
ggplot2-book - ggplot2: elegant graphics for data analysis
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
tests-as-linear - Common statistical tests are linear models (or: how to teach stats)
guildai - Experiment tracking, ML developer tools
PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks
dvc - 🦉 ML Experiments and Data Management with Git
tensorflow - An Open Source Machine Learning Framework for Everyone
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.