AWS DeepLens vs MLflow

Overview

MLflow

Stacks228

Followers524

Votes9

GitHub Stars22.8K

Forks5.0K

AWS DeepLens

Stacks1

Followers11

Votes0

MLflow vs AWS DeepLens: What are the differences?

MLflow: An open source machine learning platform. MLflow is an open source platform for managing the end-to-end machine learning lifecycle; AWS DeepLens: Deep learning enabled video camera for developers. It helps put machine learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills.

MLflow and AWS DeepLens can be primarily classified as "Machine Learning" tools.

Some of the features offered by MLflow are:

Track experiments to record and compare parameters and results
Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production
Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms

On the other hand, AWS DeepLens provides the following key features:

A new way to learn machine learning
Custom built for deep learning
Build custom models with Amazon SageMaker

MLflow is an open source tool with 62 GitHub stars and 25 GitHub forks. Here's a link to MLflow's open source repository on GitHub.

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Detailed Comparison

MLflow	AWS DeepLens
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.	It helps put machine learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills.
Track experiments to record and compare parameters and results; Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production; Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms	A new way to learn machine learning; Custom built for deep learning; Build custom models with Amazon SageMaker; Broad framework support; Integrated with AWS
Statistics
GitHub Stars 22.8K	GitHub Stars -
GitHub Forks 5.0K	GitHub Forks -
Stacks 228	Stacks 1
Followers 524	Followers 11
Votes 9	Votes 0
Pros & Cons
Pros 5 Code First 4 Simplified Logging	No community feedback yet
Integrations
No integrations available	Amazon S3 Amazon DynamoDB TensorFlow Amazon SQS Amazon SNS Amazon SageMaker Caffe Amazon IoT

What are some alternatives to MLflow, AWS DeepLens?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Related Comparisons

Some of the features offered by MLflow are:

Track experiments to record and compare parameters and results
Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production
Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms

On the other hand, AWS DeepLens provides the following key features:

A new way to learn machine learning
Custom built for deep learning
Build custom models with Amazon SageMaker

MLflow is an open source tool with 62 GitHub stars and 25 GitHub forks. Here's a link to MLflow's open source repository on GitHub.

AWS DeepLens vs MLflow