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What is Strong Analytics?

Our platforms establish a dependable foundation for the creation, development, and execution of customized machine learning and artificial intelligence solutions. You can design applications for next-best actions that incorporate reinforcement-learning algorithms, allowing them to learn, adapt, and refine their processes over time. Furthermore, we offer bespoke deep learning vision models that continuously evolve to meet your distinct challenges. By utilizing advanced forecasting methods, you can effectively predict future trends. With our cloud-based tools, intelligent decision-making can be facilitated across your organization through seamless data monitoring and analysis. However, transitioning from experimental machine learning applications to stable and scalable platforms poses a considerable challenge for experienced data science and engineering teams. Strong ML effectively tackles this challenge by providing a robust suite of tools aimed at simplifying the management, deployment, and monitoring of your machine learning applications, thereby enhancing both efficiency and performance. This approach ensures your organization remains competitive in the fast-paced world of technology and innovation, fostering a culture of adaptability and growth. By embracing these solutions, you can empower your team to harness the full potential of AI and machine learning.

What is Ray?

You can start developing on your laptop and then effortlessly scale your Python code across numerous GPUs in the cloud. Ray transforms conventional Python concepts into a distributed framework, allowing for the straightforward parallelization of serial applications with minimal code modifications. With a robust ecosystem of distributed libraries, you can efficiently manage compute-intensive machine learning tasks, including model serving, deep learning, and hyperparameter optimization. Scaling existing workloads is straightforward, as demonstrated by how Pytorch can be easily integrated with Ray. Utilizing Ray Tune and Ray Serve, which are built-in Ray libraries, simplifies the process of scaling even the most intricate machine learning tasks, such as hyperparameter tuning, training deep learning models, and implementing reinforcement learning. You can initiate distributed hyperparameter tuning with just ten lines of code, making it accessible even for newcomers. While creating distributed applications can be challenging, Ray excels in the realm of distributed execution, providing the tools and support necessary to streamline this complex process. Thus, developers can focus more on innovation and less on infrastructure.

Media

Media

Integrations Supported

Integrations Supported

Amazon EKS
Amazon SageMaker
Amazon Web Services (AWS)
Anyscale
Azure Kubernetes Service (AKS)
Dask
Databricks
Feast
Flyte
Google Cloud Platform
Google Kubernetes Engine (GKE)
Kubernetes
LanceDB
MLflow
PyTorch
Python
Snowflake
TensorFlow
Union Cloud
io.net

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Free
Open source. Consumption-based.
Free Version
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Strong Analytics

Company Location

United States

Company Website

www.strong.io/platforms

Company Facts

Organization Name

Anyscale

Date Founded

2019

Company Location

United States

Company Website

ray.io

Categories and Features

AI Vision Models

Not specified

Computer Vision

Not specified

Deep Learning

Not specified

Machine Learning

Not specified

Categories and Features

Deep Learning

Not specified

Machine Learning

Not specified

ML Model Deployment

Not specified

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