bodywork
TFLearn
| bodywork | TFLearn | |
|---|---|---|
| 8 | 2 | |
| 430 | 9,618 | |
| - | 0.0% | |
| 0.0 | 0.0 | |
| about 1 year ago | 5 months ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | GNU General Public License v3.0 or later |
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bodywork
- Deployment automation for ML projects of all shapes and sizes
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A tutorial on how to handle prediction uncertainty in production systems, by using Bayesian inference and probabilistic programs
how to deploy it to Kuberentes using Bodywork.
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[P] [D] How are you approaching prediction uncertainty in ML systems?
I usually turn to generative models - e.g. probabilistic programs and Bayesian inference. I’ve written-up my thoughts on how to engineer these into a ‘production system’ deployed to Kubernetes, using PyMC and Bodywork (an open-source ML deployment tool that I contribute to).
- Bodywork: MLOps tool for deploying ML projects to Kubernetes
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Tool for mapping executable Python modules to Kubernetes deployments
I’m one of the core contributors to Bodywork, an open-source tool for deploying machine learning projects developed in Python, to Kubernetes.
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[P] [D] The benefits of training the simplest model you can think of and deploying it to production, as soon as you can.
I’ve had many successes with this approach. With this in mind, I’ve put together an example of how to make this Agile approach to developing machine learning systems a reality, by demonstrating that it takes under 15 minutes to deploy a Scikit-Learn model, using FastAPI with Bodywork (an open-source MLOps tool that I have built).
- bodywork - MLOps for Python and K8S
- bodywork-ml/bodywork-core - MLOps automation for Python and Kubernetes
TFLearn
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Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
TFLearn – Deep learning library featuring a higher-level API for TensorFlow
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Base ball
Both the teams in a game are given their individual ID values and are made into vectors. Relevant data like the home and away team, home runs, RBI’s, and walk’s are all taken into account and passed through layers. There’s no need to reinvent the wheel here, there's a multitude of libraries that enable a coder to implement machine learning theories efficiently. In this case we will be using a library called TFlearn, documentation available from http://tflearn.org. The program will output the home and away teams as well as their respective score predictions.
What are some alternatives?
NuPIC - Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
Keras - Deep Learning for humans
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
tensorflow - An Open Source Machine Learning Framework for Everyone
Crab - Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).
scikit-learn - scikit-learn: machine learning in Python
gensim - Topic Modelling for Humans
skflow - Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning
PyBrain
neptune-contrib - This library is a location of the LegacyLogger for PyTorch Lightning.
tfgraphviz - A visualization tool to show a TensorFlow's graph like TensorBoard