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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

Discover a revolutionary approach to training machine learning models directly on your Mac with Create ML, which simplifies the process while delivering robust Core ML models. You can train several models with various datasets all within one cohesive project. Utilize Continuity to preview your model's performance by connecting your iPhone's camera and microphone to your Mac, or simply input sample data for evaluation. The training process allows you to pause, save, resume, and even extend as needed. Gain insights into how your model performs against test data from your evaluation set and delve into essential metrics, exploring their relationships to specific examples, which can highlight difficult use cases, guide further data collection efforts, and uncover opportunities to enhance model quality. Additionally, if you want to elevate your training performance, you can integrate an external graphics processing unit with your Mac. Experience the lightning-fast training capabilities available on your Mac that leverage both CPU and GPU resources, and take your pick from a diverse selection of model types offered by Create ML. This tool not only streamlines the training process but also empowers users to maximize the effectiveness of their machine learning endeavors.

Description

Originally created by Uber, Horovod aims to simplify and accelerate the process of distributed deep learning, significantly reducing model training durations from several days or weeks to mere hours or even minutes. By utilizing Horovod, users can effortlessly scale their existing training scripts to leverage the power of hundreds of GPUs with just a few lines of Python code. It offers flexibility for deployment, as it can be installed on local servers or seamlessly operated in various cloud environments such as AWS, Azure, and Databricks. In addition, Horovod is compatible with Apache Spark, allowing a cohesive integration of data processing and model training into one streamlined pipeline. Once set up, the infrastructure provided by Horovod supports model training across any framework, facilitating easy transitions between TensorFlow, PyTorch, MXNet, and potential future frameworks as the landscape of machine learning technologies continues to progress. This adaptability ensures that users can keep pace with the rapid advancements in the field without being locked into a single technology.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS) No 
Azure Databricks No 
Flyte No 
Keras No 
MXNet No 
Microsoft Azure No 
PyTorch No 
Python No 
TensorFlow No 

Integrations

Amazon Web Services (AWS) Yes 
Azure Databricks Yes 
Flyte Yes 
Keras Yes 
MXNet Yes 
Microsoft Azure Yes 
PyTorch Yes 
Python Yes 
TensorFlow Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Apple

Country

United States

Website

developer.apple.com/machine-learning/create-ml/

Vendor Details

Company Name

Horovod

Website

horovod.ai/

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

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