Software Alternatives, Accelerators & Startups

FeedBear VS Amazon SageMaker

Compare FeedBear VS Amazon SageMaker and see what are their differences

FeedBear logo FeedBear

The smartest way to manage feedback from your customers

Amazon SageMaker logo Amazon SageMaker

Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
  • FeedBear Landing page
    Landing page //
    2023-09-28

FeedBear helps you make informed product decisions and keep users in the loop.

  • Feedback boards allow you to collect feedback from your customers in one, central place.
  • Roadmap fills up automatically as you make progress.
  • Changelog allows you to make bigger announcements in a beautiful way.

Check out our website to learn more.

  • Amazon SageMaker Landing page
    Landing page //
    2023-03-15

FeedBear

$ Details
paid Free Trial $59.0 / Monthly (All-inclusive)
Platforms
Web
Release Date
2018 August

FeedBear features and specs

  • Integrations: Intercom, Slack
  • Public roadmap: Show your progress
  • Feedback boards: Let your users post feedback & requests
  • Changelog: Announce important updates
  • Free Trial: 14-day free trial, no credit card required
  • Easy to Set-up and use: less than 5 min setup
  • Clean UI: Clean and Smooth UI, No technical jargon and just works

Amazon SageMaker features and specs

No features have been listed yet.

FeedBear videos

No FeedBear videos yet. You could help us improve this page by suggesting one.

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Amazon SageMaker videos

Build, Train and Deploy Machine Learning Models on AWS with Amazon SageMaker - AWS Online Tech Talks

More videos:

  • Review - An overview of Amazon SageMaker (November 2017)

Category Popularity

0-100% (relative to FeedBear and Amazon SageMaker)
Customer Feedback
100 100%
0% 0
Data Science And Machine Learning
User Feedback
100 100%
0% 0
Machine Learning
0 0%
100% 100

User comments

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Reviews

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

FeedBear Reviews

We have no reviews of FeedBear yet.
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Amazon SageMaker Reviews

7 best Colab alternatives in 2023
Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning. It allows users to write code, track experiments, visualize data, and perform debugging and monitoring all within a single, integrated visual interface, making the process of developing, testing, and deploying models much more manageable.
Source: deepnote.com

Social recommendations and mentions

Based on our record, Amazon SageMaker seems to be more popular. It has been mentiond 36 times since March 2021. 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.

FeedBear mentions (0)

We have not tracked any mentions of FeedBear yet. Tracking of FeedBear recommendations started around Mar 2021.

Amazon SageMaker mentions (36)

  • Observations on MLOps–A Fragmented Mosaic of Mismatched Expectations
    Damn straight. Oh, wait, some vendors have claimed to build an end-to-end solution. But, meh, that’s marketing talk. Take, for example, a well-known platform like Amazon Sagemaker, which describes itself as “a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML) for any use case.” It’s a great platform. My startup has even partnered with them.... - Source: dev.to / 18 days ago
  • Sentiment Analysis with PubNub Functions and HuggingFace
    At this point, probably everyone has heard about OpenAI, GPT-4, Claude or any of the popular Large Language Models (LLMs). However, using these LLMs in a production environment can be expensive or nondeterministic regarding its results. I guess that is the downside of being good at everything; you could be better at performing one specific task. This is where HuggingFace can utilized. HuggingFace provides... - Source: dev.to / about 1 month ago
  • Beginning the Journey into ML, AI and GenAI on AWS
    Generative Artificial Intelligence (GenAI) is a type of artificial intelligence that can generate text, images, or other media using generative models. AWS offers a range of services for building and scaling generative AI applications, including Amazon SageMaker, Amazon Rekognition, AWS DeepRacer, and Amazon Forecast. AWS has also invested in developing foundation models (FMs) for generative AI, which are... - Source: dev.to / 4 months ago
  • Technical Architecture for LLMOps
    Amazon and Azure already have much of what you're talking about in AWS SageMaker and Azure MLOps. Source: 12 months ago
  • Are AI fine-tuning tools worth learning and investing?
    And there have been several platforms that help fine-tune pretrained models, such as Google Cloud AutoML and Amazon Sagemaker. These tools are often fairly easy to use, but they come at a cost. They can be expensive, depending on the size of your dataset. Another option is Finetuner+, that also fine-tunes like AutoML and Sagemaker. The big advantage is that you don't need to transfer your data to other GPUs,... Source: about 1 year ago
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What are some alternatives?

When comparing FeedBear and Amazon SageMaker, you can also consider the following products

Canny - Canny helps you collect and organize feature requests to better understand customer needs and prioritize your roadmap.

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Upvoty - User feedback in 1 simple overview 🔥

IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.

Nolt - A fast & beautiful way to collect user feedback

Azure Machine Learning Studio - Azure Machine Learning Studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure.