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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

Having the most skilled developers isn't enough if testing processes are hindering their progress; in fact, a staggering 80% of your software tests may be ineffective. The challenge lies in identifying which 80% is truly unnecessary. We utilize your data to pinpoint the essential 20%, enabling you to accelerate your release process. Our predictive test selection tool, inspired by machine learning techniques employed by leading companies like Facebook, is designed for easy adoption by any organization. We accommodate a variety of programming languages, test frameworks, and continuous integration systems—just integrate Git into your workflow. Launchable employs machine learning to evaluate your test failures alongside your source code, sidestepping traditional code syntax analysis. This flexibility allows Launchable to effortlessly extend its support to nearly any file-based programming language, ensuring it can adapt to various teams and projects with differing languages and tools. Currently, we provide out-of-the-box support for languages including Python, Ruby, Java, JavaScript, Go, C, and C++, with a commitment to continually expand our offerings as new languages emerge. In this way, we help organizations streamline their testing process and enhance overall efficiency.

Description

Pachyderm's Data Versioning offers teams an efficient and automated method for monitoring all changes to their data. With file-based versioning, users benefit from a comprehensive audit trail that encompasses all data and artifacts at each stage of the pipeline, including intermediate outputs. The data is stored as native objects rather than mere metadata pointers, ensuring that versioning is both automated and reliable. The system can automatically scale by utilizing parallel processing for data without the need for additional coding. Incremental processing optimizes resource usage by only addressing the differences in data and bypassing any duplicates. Additionally, Pachyderm’s Global IDs simplify the tracking of results back to their original inputs, capturing all relevant analysis, parameters, code, and intermediate outcomes. The intuitive Pachyderm Console further enhances user experience by providing clear visualizations of the directed acyclic graph (DAG) and supports reproducibility through Global IDs, making it a valuable tool for teams managing complex data workflows. This comprehensive approach ensures that teams can confidently navigate their data pipelines while maintaining accuracy and efficiency.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS)
Android
Apple iOS
Bitbucket
Cucumber
Cypress
Determined AI
GitHub
GitLab
Gradle
JUnit
Jenkins
Jest
Kubernetes
Label Studio
Maven
NUnit
Travis CI
pytest

Integrations

Amazon Web Services (AWS)
Android
Apple iOS
Bitbucket
Cucumber
Cypress
Determined AI
GitHub
GitLab
Gradle
JUnit
Jenkins
Jest
Kubernetes
Label Studio
Maven
NUnit
Travis CI
pytest

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Launchable

Country

United States

Website

www.launchableinc.com

Vendor Details

Company Name

Pachyderm

Website

www.pachyderm.com

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Software Testing

Automated Testing
Black-Box Testing
Dynamic Testing
Issue Tracking
Manual Testing
Quality Assurance Planning
Reporting / Analytics
Static Testing
Test Case Management
Variable Testing Methods
White-Box Testing

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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