Software Alternatives, Accelerators & Startups

LinearB VS Amazon Machine Learning

Compare LinearB VS Amazon Machine Learning and see what are their differences

LinearB logo LinearB

LinearB delivers software leaders the insights they need to make their engineering teams better through a real-time SaaS platform. Visibility into key metrics paired with automated improvement actions enables software leaders to deliver more.

Amazon Machine Learning logo Amazon Machine Learning

Machine learning made easy for developers of any skill level
  • LinearB Landing page
    Landing page //
    2023-08-19
  • Amazon Machine Learning Landing page
    Landing page //
    2023-03-13

LinearB videos

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Amazon Machine Learning videos

Introduction to Amazon Machine Learning - Predictive Analytics on AWS

More videos:

  • Tutorial - AWS Machine Learning Tutorial | Amazon Machine Learning | AWS Training | Edureka

Category Popularity

0-100% (relative to LinearB and Amazon Machine Learning)
Data Dashboard
100 100%
0% 0
AI
0 0%
100% 100
Predictive Analytics
100 100%
0% 0
Developer Tools
0 0%
100% 100

User comments

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Social recommendations and mentions

Based on our record, LinearB seems to be a lot more popular than Amazon Machine Learning. While we know about 26 links to LinearB, we've tracked only 2 mentions of Amazon Machine Learning. 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.

LinearB mentions (26)

  • 🦊 GitLab: A Python Script Calculating DORA Metrics
    LinearB is a SaaS solution that retrieves metrics overtime, some of them being used to calculate DORA Metrics. They also have a Youtube channel that advocate for DORA Metrics and more. - Source: dev.to / 4 months ago
  • 6 Proven Strategies For Being A Great Platform Engineer
    In helping engineering orgs get visibility into developer workflows with LinearB, Dan Lines and Ori Keren discovered that the majority of cycle time was being spent in pull request and code review. They found that:. - Source: dev.to / 11 months ago
  • How to consolidate metrics from across the entire organisation
    LinearB and there are a few cheaper alternatives. Ties in DORA metrics from gut repos and agile project management tools like JIRA. https://linearb.io. Source: about 1 year ago
  • Measure Performance and Engagement Meaningfully
    You can also try these alternatives - LinearB, Jellyfish. Source: about 1 year ago
  • 'Software Engineering Dashboards: Types, Risks, and Future' by Margaret-Anne Storey & Christoph Treude
    Some recommended dashboard tools that you can try: Jellyfish, Typo, LinearB. Source: about 1 year ago
View more

Amazon Machine Learning mentions (2)

  • Rant + Planning to learn full stack development
    There’s also the ML as a service (MLaaS) movement that lowers the barrier for common ML capabilities (eg image object detection and audio transcription). Basically, you use APIs. See: https://aws.amazon.com/machine-learning/. Source: almost 2 years ago
  • Ask the Experts: AWS Data Science and ML Experts - Mar 9th @ 8AM ET / 1PM GMT!
    Do you have questions about Data Science and ML on AWS - https://aws.amazon.com/machine-learning/. Source: over 3 years ago

What are some alternatives?

When comparing LinearB and Amazon Machine Learning, you can also consider the following products

Waydev - Waydev analyzes your codebase from Github, Gitlab, Azure DevOps & Bitbucket to help you bring out the best in your engineers work.

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

GitPrime - GitPrime uses data from any Git based code repository to give management the software engineering metrics needed to move faster and optimize work patterns.

Lobe - Visual tool for building custom deep learning models

Swarmia - Swarmia is an engineering productivity software trusted by 600+ engineering teams worldwide. Use key engineering metrics to unblock the flow, align engineering with business objectives, and drive continuous improvement.

Apple Machine Learning Journal - A blog written by Apple engineers