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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Lobe.ai vs PredictionIO

Lobe.ai vs PredictionIO

OverviewComparisonAlternatives

Overview

PredictionIO
PredictionIO
Stacks67
Followers110
Votes8
Lobe.ai
Lobe.ai
Stacks7
Followers21
Votes0

Lobe.ai vs PredictionIO: What are the differences?

  1. Technology Used: Lobe.ai utilizes a drag-and-drop interface to create machine learning models, making it user-friendly for those without coding experience, while PredictionIO requires programming skills and knowledge of Apache Spark for building ML models.

  2. Deployment Options: Lobe.ai offers cloud-based deployment, which simplifies the process of making models accessible for production use, whereas PredictionIO requires setting up and configuring on-premise servers for deployment, making it more complex and time-consuming.

  3. Customization and Flexibility: Lobe.ai provides limited customization options due to its simplified interface, which can be restrictive for advanced users, whereas PredictionIO allows for extensive customization and flexibility in model creation and deployment.

  4. Scalability: Lobe.ai may have limitations in scalability as it is tailored towards smaller projects or individuals, while PredictionIO is designed to handle large-scale machine learning projects with its scalability features.

  5. Pre-built Models: Lobe.ai offers pre-built models that can be easily implemented, saving time and effort for users, whereas PredictionIO mainly focuses on providing a platform for building custom models from scratch.

  6. Community Support: Lobe.ai has a smaller community and available resources compared to PredictionIO, which has a larger user base and active community for support, troubleshooting, and knowledge sharing.

In Summary, Lobe.ai and PredictionIO differ in technology used, deployment options, customization, scalability, pre-built models, and community support.

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Detailed Comparison

PredictionIO
PredictionIO
Lobe.ai
Lobe.ai

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

It helps you train machine learning models with a free, easy to use tool. It has everything you need to bring your machine learning ideas to life. Just show it examples of what you want it to learn, and it automatically trains a custom machine learning model that can be shipped in your app.

Integrated with state-of-the-art machine learning algorithms. Fine-tune, evaluate and implement them scientifically.;Customize the modularized open codebase to fulfill any unique prediction requirement.;Built on top of scalable frameworks such as Hadoop and Cascading. Ready to handle data of any scale.;Build powerful features in minutes, not months. Streamline the data engineering process.
Machine learning made easy; Free and Private; Ship Anywhere; Label, Train, Play
Statistics
Stacks
67
Stacks
7
Followers
110
Followers
21
Votes
8
Votes
0
Pros & Cons
Pros
  • 8
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What are some alternatives to PredictionIO, Lobe.ai?

TensorFlow

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

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