ai
MLflow
ai | MLflow | |
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6 | 60 | |
19 | 17,686 | |
- | 2.3% | |
3.5 | 9.9 | |
2 months ago | 5 days ago | |
Python | Python | |
MIT License | 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.
ai
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Made the YouTube Series Implementing ML Models Using NumPy
GitHub (for model impls and other series): https://github.com/oniani/ai
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[D] What advanced models would you like to see implemented from scratch?
All of the videos are and will be available on my YouTube channel. Implementations are and will be in this GitHub repo.
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[N] I Have Released the YouTube Series Discussing and Implementing Activation Functions
GitHub: https://github.com/oniani/ai
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Implementing Logistic Regression from Scratch
Link to the YouTube video: https://www.youtube.com/watch?v=YDa3rX9yLCE Link to the repo containing the code: https://github.com/oniani/ai
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[N] AI/ML Model API Design, Numerical Stability, and More Models from Scratch! (stylepoint)
Repository for the AI/ML series - oniani/ai.
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Implementing Machine Learning Models From Scratch (stylepoint)
Thanks! One thing to note about that implementation is that we could have passed features and labels directly to the fit method. This would avoid unnecessary data copying (i.e., storing data inside the LinearRegression class). I have already updated the GitHub codebase.
MLflow
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Accelerating into AI: Lessons from AWS
CometML and mlMLFlow are popular development and experimentation tools, although some express concerns about their proprietary and weak data storage with its lack of tamper-proof guarantees.
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10 Open Source Tools for Building MLOps Pipelines
MLflow is an open source MLOps tool that allows users to manage the entire life cycle of machine learning models. It has four key components:
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A step-by-step guide to building an MLOps pipeline
Experiment tracking tools like MLflow, Weights and Biases, and Neptune.ai provide a pipeline that automatically tracks meta-data and artifacts generated from each experiment you run. Although they have varying features and functionalities, experiment tracking tools provide a systematic structure that handles the iterative model development approach.
- Mlflow: Open-source platform for the machine learning lifecycle
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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.
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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.
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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.
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
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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
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
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
guildai - Experiment tracking, ML developer tools
dvc - 🦉 ML Experiments and Data Management with Git
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
tensorflow - An Open Source Machine Learning Framework for Everyone
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.
neptune-client - 📘 The MLOps stack component for experiment tracking
gensim - Topic Modelling for Humans
dagster - An orchestration platform for the development, production, and observation of data assets.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator