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Average Ratings 0 Ratings

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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

Scikit-learn offers a user-friendly and effective suite of tools for predictive data analysis, making it an indispensable resource for those in the field. This powerful, open-source machine learning library is built for the Python programming language and aims to simplify the process of data analysis and modeling. Drawing from established scientific libraries like NumPy, SciPy, and Matplotlib, Scikit-learn presents a diverse array of both supervised and unsupervised learning algorithms, positioning itself as a crucial asset for data scientists, machine learning developers, and researchers alike. Its structure is designed to be both consistent and adaptable, allowing users to mix and match different components to meet their unique requirements. This modularity empowers users to create intricate workflows, streamline repetitive processes, and effectively incorporate Scikit-learn into expansive machine learning projects. Furthermore, the library prioritizes interoperability, ensuring seamless compatibility with other Python libraries, which greatly enhances data processing capabilities and overall efficiency. As a result, Scikit-learn stands out as a go-to toolkit for anyone looking to delve into the world of machine learning.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS)
Android
Cucumber
Cypress
Databricks Data Intelligence Platform
GitHub
GitLab
Gradle
Guild AI
Intel Tiber AI Studio
JUnit
Keepsake
Kubernetes
Matplotlib
Maven
ModelOp
NumPy
Python
Travis CI
pytest

Integrations

Amazon Web Services (AWS)
Android
Cucumber
Cypress
Databricks Data Intelligence Platform
GitHub
GitLab
Gradle
Guild AI
Intel Tiber AI Studio
JUnit
Keepsake
Kubernetes
Matplotlib
Maven
ModelOp
NumPy
Python
Travis CI
pytest

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
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

scikit-learn

Country

United States

Website

scikit-learn.org/stable/

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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