Software Alternatives, Accelerators & Startups

Amazon Rekognition VS Deepnote

Compare Amazon Rekognition VS Deepnote and see what are their differences

Amazon Rekognition logo Amazon Rekognition

Add Amazon's advanced image analysis to your applications.

Deepnote logo Deepnote

A collaboration platform for data scientists
  • Amazon Rekognition Landing page
    Landing page //
    2023-04-18
  • Deepnote Landing page
    Landing page //
    2023-10-09

Amazon Rekognition videos

AWS Rekognition Tutorial | Image Recognition using AWS | Amazon Rekognition | AWS Training | Edureka

More videos:

  • Review - Extract Data from Images and Videos with Amazon Rekognition (Level 300)
  • Demo - Can Amazon's Facial Recognition identify my 15 years younger picture? | Amazon Rekognition Demo

Deepnote videos

Could this be the Best Data Science Notebook? (Deepnote)

Category Popularity

0-100% (relative to Amazon Rekognition and Deepnote)
Image Analysis
100 100%
0% 0
Data Science And Machine Learning
Machine Learning
100 100%
0% 0
AI
61 61%
39% 39

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Amazon Rekognition and Deepnote

Amazon Rekognition Reviews

2019 Examples to Compare OCR Services: Amazon Textract/Rekognition vs Google Vision vs Microsoft Cognitive Services
Pricing: Amazon Rekognition, Amazon Textract, Google, Microsoft. We don't really care which one you use, but Microsoft did best by our sample data. Textract was a very close second if you only need its headline feature: extracting text from digital documents. If someone wants to email bill -at- amplenote.com with comparable data for other images/services, I can try to...

Deepnote Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Deepnote is a cloud-based data science notebook platform comparable to Jupyter Notebooks but with a focus on real-time collaboration and editing. It lets users write and run code in several programming languages, as well as include text, equations, and visualizations in a single document.
Source: lakefs.io
7 best Colab alternatives in 2023
Deepnote is a real-time collaborative notebook. It offers features like real-time collaboration, version control, and smart autocomplete. It also provides direct integrations with popular data sources like GitHub, Google Drive, and BigQuery. Its modern, intuitive interface makes it a compelling choice for both beginners and experienced data scientists.
Source: deepnote.com
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Deepnote is a cloud-based, data science notebook platform that is similar to Jupyter Notebooks, but with a focus on collaboration and real-time editing. It allows users to write and execute code in a variety of programming languages, as well as include text, equations, and visualizations in a single document. Deepnote also has a built-in code editor and supports a wide range...
Source: noteable.io
The Best ML Notebooks And Infrastructure Tools For Data Scientists
A Jupyter-notebook enabled platform, Deepnote boasts of many advanced features. Deepnote supports real-time collaboration to discuss and debug the code. The platform will soon have functions such as versioning, code review, and reproducibility. Deepnote has intelligent features to quickly browse the code, find patterns in your data, and autocomplete code. It can integrate...

Social recommendations and mentions

Amazon Rekognition might be a bit more popular than Deepnote. We know about 33 links to it since March 2021 and only 32 links to Deepnote. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Amazon Rekognition mentions (33)

  • Afraid of outgrowing AWS Rekognition? Try YOLO in Lambda.
    AWS Rekognition is a great choice for many types of real-world projects or just for testing an idea on your images. The issue eventually comes with its cost, unfortunately, which we will see later in a specific example. Don’t get me wrong, Rekognition is a great service and I love to use it for its simplicity and reliable performance on quite a few projects. - Source: dev.to / about 2 months ago
  • Amazon Rekognition Custom Labels for Bears
    I don’t really want to spend so much time manually adjusting labels. For most machine learning, the next step would be to fine tune your model. You can essentially fine tune Amazon Rekognition by using Custom Labels. You can do this to make it better at detecting specific objects (like bears) or train it to detect new objects like your product or logo. It really depends on your application needs. - Source: dev.to / 10 months ago
  • “During a gold rush, sell shovels.” What are the shovels in the AI rush?
    For instance, are you a company with lots of security cameras? Hire me to write a program that pipes your data into AWS rekognition and then shows you a dashboard of what happened on your cams today. Got a ton of products with no meta-description? Hire me to write a program that pipes your data into OpenAI, and then saves the generated description to your custom CMS. Source: 11 months ago
  • Serverless Facial Recognition Voting Application Using AWS Services
    Amazon Rekognition: Used to index, detect faces in the picture, and compare faces when users try voting, it was the heart of the facial voting feature. - Source: dev.to / about 1 year ago
  • Amazon Prime Video playback features
    Sure. But if you think generating thumbnails and detecting intros/credits takes a long time, wait until your computer is running machine learning/computer vision over your entire library. They also have to build and train that model which is no trivial task. And I know what you're thinking, why don't they just use Amazon's Rekognition service that does celebrity identification? Well, it's $0.10 per minute of... Source: about 1 year ago
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Deepnote mentions (32)

  • A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
    Deepnote - A new data science notebook. Jupyter is compatible with real-time collaboration and running in the cloud. The free tier includes unlimited personal projects, up to 750 hours of standard hardware, and teams with up to 3 editors. - Source: dev.to / 4 months ago
  • JupyterLab 4.0
    We looked into many of these issues with Deepnote (YC S19) [https://deepnote.com/]. What we found is that these are not necessarily problems of the underlying medium (a notebook), but more of the specific implementation (Jupyter). We've seen a lot of progress in the Jupyter ecosystem, but unfortunately almost none in the areas you mentioned. - Source: Hacker News / 11 months ago
  • Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
    Upload your ipynb to Deepnote and publish as an app. That simple. https://deepnote.com. - Source: Hacker News / about 1 year ago
  • Quick tip: Using a SingleStoreDB Recursive CTE with London Underground data
    Using Deepnote, we'll create a Python notebook and upload the two GeoJSON files into a data directory. - Source: dev.to / over 1 year ago
  • free-for.dev
    Deepnote - A new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud. Free tier includes unlimited personal projects, up to 750 hours of standard hardware and teams with up to 3 editors. - Source: dev.to / over 1 year ago
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What are some alternatives?

When comparing Amazon Rekognition and Deepnote, you can also consider the following products

Google Vision AI - Cloud Vision API provides a comprehensive set of capabilities including object detection, ocr, explicit content, face, logo, and landmark detection.

Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

Clarifai - The World's AI

Apache Zeppelin - A web-based notebook that enables interactive data analytics.

Kairos - Facial recognition & mood detection API

Saturn Cloud - ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.