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ML / DL framework for deployment and experiment
*) Listed framework is opensource
*) It provides dashboard
*) Configure, organize, log and reproduce experiments
*) make prototyping experiments easier
Machine learning life cycle framework
List of opensource ML framework
*) MLFlow
** An open source platform for the complete machine learning lifecycle
** It currently offers three components: MLflow Tracking, MLflow Projects, MLflow Models
*) Sacred ** configure, organize, log and reproduce experiments ** easily run your experiment for different settings
*) FGLab ** machine learning dashboard, designed to make prototyping experiments easier. ** here are 3 ways to run FGLab: Installing locally, via Docker, or hosted on Heroku.
*) Sacred ** configure, organize, log and reproduce experiments ** easily run your experiment for different settings
*) FGLab ** machine learning dashboard, designed to make prototyping experiments easier. ** here are 3 ways to run FGLab: Installing locally, via Docker, or hosted on Heroku.
If anything can be add / missed out, mention in the comments section.
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Latest Popular Machine Learning Frameworks are :
Amazon Machine Learning
Apache SINGA
TensorFlow
scikit-learn
MLlib Spark
Torch
PyTorch
Shogun
Spark ML
Caffe
H2O
Keras
mlpack
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I recently explored some top-notch open-source machine learning frameworks, and I’m excited about what I discovered. MLFlow stands out as a comprehensive solution for managing the entire ML lifecycle, with robust tools for tracking, projects, and models. Sacred is fantastic for its ability to configure, organize, and reproduce experiments seamlessly. Lastly, FGLab offers a user-friendly dashboard that simplifies prototyping experiments, with flexible installation options. These frameworks truly enhance the design, experimentation, and deployment phases of machine learning projects. Highly recommended for anyone looking to streamline their ML workflows
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This blog provides a concise overview of three top open-source machine learning frameworks: MLflow, Sacred, and FGLab. Each framework is designed to simplify the machine learning lifecycle, from experimentation to deployment.
MLflow stands out for its comprehensive platform, offering tracking, project management, and model deployment. Sacred focuses on organizing, logging, and reproducing experiments with ease, while FGLab is ideal for prototyping, providing a user-friendly dashboard. The blog is a helpful guide for data scientists looking for efficient tools to streamline their ML processes.
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The three top machine learning frameworks for design, experimentation, and deployment are TensorFlow, PyTorch, and Scikit-Learn.
Developed by Google, TensorFlow is widely used for building deep learning models, offering extensive libraries and tools for scalable ML solutions. With TensorFlow Serving, deployment becomes streamlined, making it ideal for production environments.
Known for its flexibility and ease of use, PyTorch, developed by Facebook, is favored for research and prototyping. It supports dynamic computation graphs, making model experimentation intuitive. Deploying models with PyTorch is increasingly robust with tools like TorchServe.
Ideal for traditional ML algorithms, Scikit-Learn is simple to implement and widely used for data preprocessing and model experimentation. It's a popular choice for initial design stages and smaller applications.
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"Choosing the right machine learning framework can be challenging, but this article does a great job of breaking down the key features and use cases of the top ones. Excited to dive deeper into these tools for my next project!"
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