AWS DeepLens vs Deepkit

Overview

Deepkit

Stacks2

Followers8

Votes0

AWS DeepLens

Stacks1

Followers11

Votes0

AWS DeepLens vs Deepkit: What are the differences?

Cost: One key difference between AWS DeepLens and Deepkit is the cost structure. AWS DeepLens is a physical device developed by Amazon Web Services and comes with an upfront cost. Deepkit, on the other hand, is a software platform with a subscription-based pricing model, allowing users to pay as they go without the need for hardware investment.
Integration with AWS Services: Another significant difference is the integration with other AWS services. AWS DeepLens seamlessly integrates with various AWS cloud services like Amazon S3, Kinesis, and Rekognition, providing a comprehensive ecosystem for machine learning applications. Deepkit, however, may offer limited or no integration with AWS services, depending on the specific functionalities of the platform.
Hardware Requirements: AWS DeepLens is a dedicated hardware device optimized for machine learning tasks, which provides faster processing and better performance. In contrast, Deepkit is a software solution that can run on a variety of hardware configurations, offering flexibility but potentially sacrificing processing power and efficiency compared to a specialized device like AWS DeepLens.
Customization and Control: Deepkit may offer more customization options and control over the machine learning models and algorithms used in applications compared to AWS DeepLens, which is more geared towards out-of-the-box solutions and easier deployment for users with limited technical expertise.
Community Support and Ecosystem: AWS DeepLens benefits from the extensive AWS community and ecosystem, providing access to resources, tutorials, and support from a large user base. Deepkit, being a newer or less widely adopted platform, may have a smaller community or limited external resources available for users seeking assistance or guidance.
Target Audience: The target audience for AWS DeepLens tends to be developers, researchers, and professionals looking for a dedicated hardware solution for deploying machine learning models at the edge. Deepkit, on the other hand, may cater to a broader range of users, including hobbyists, students, and small businesses seeking a more accessible and affordable AI platform for experimentation and deployment.

In Summary, the key differences between AWS DeepLens and Deepkit lie in the cost structure, integration with AWS services, hardware requirements, customization and control options, community support, and target audience.

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Detailed Comparison

Deepkit	AWS DeepLens
It is the collaborative and analytical training suite for insightful, fast, and reproducible modern machine learning. All in one cross-platform desktop app for you alone, corporate or open-source teams.	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.
Real-time UI and collaboration; Unified experiments; Model debugger; Any framework, all languages; Job scheduling; Pipeling; Docker and GPU support; Docker and GPU support; Offline first; Git integration / CI	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
Stacks 2	Stacks 1
Followers 8	Followers 11
Votes 0	Votes 0
Integrations
Docker Python TensorFlow Git Keras PyTorch	Amazon S3 Amazon DynamoDB TensorFlow Amazon SQS Amazon SNS Amazon SageMaker Caffe Amazon IoT

What are some alternatives to Deepkit, 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.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

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.

Related Comparisons

AWS DeepLens vs Deepkit: What are the differences?

Cost: One key difference between AWS DeepLens and Deepkit is the cost structure. AWS DeepLens is a physical device developed by Amazon Web Services and comes with an upfront cost. Deepkit, on the other hand, is a software platform with a subscription-based pricing model, allowing users to pay as they go without the need for hardware investment.
Integration with AWS Services: Another significant difference is the integration with other AWS services. AWS DeepLens seamlessly integrates with various AWS cloud services like Amazon S3, Kinesis, and Rekognition, providing a comprehensive ecosystem for machine learning applications. Deepkit, however, may offer limited or no integration with AWS services, depending on the specific functionalities of the platform.
Hardware Requirements: AWS DeepLens is a dedicated hardware device optimized for machine learning tasks, which provides faster processing and better performance. In contrast, Deepkit is a software solution that can run on a variety of hardware configurations, offering flexibility but potentially sacrificing processing power and efficiency compared to a specialized device like AWS DeepLens.
Customization and Control: Deepkit may offer more customization options and control over the machine learning models and algorithms used in applications compared to AWS DeepLens, which is more geared towards out-of-the-box solutions and easier deployment for users with limited technical expertise.
Community Support and Ecosystem: AWS DeepLens benefits from the extensive AWS community and ecosystem, providing access to resources, tutorials, and support from a large user base. Deepkit, being a newer or less widely adopted platform, may have a smaller community or limited external resources available for users seeking assistance or guidance.
Target Audience: The target audience for AWS DeepLens tends to be developers, researchers, and professionals looking for a dedicated hardware solution for deploying machine learning models at the edge. Deepkit, on the other hand, may cater to a broader range of users, including hobbyists, students, and small businesses seeking a more accessible and affordable AI platform for experimentation and deployment.

AWS DeepLens vs Deepkit

Overview