Software Alternatives & Reviews

Amazon Forecast VS Qubole

Compare Amazon Forecast VS Qubole and see what are their differences

Amazon Forecast logo Amazon Forecast

Accurate time-series forecasting service, based on the same technology used at Amazon.com. No machine learning experience required.

Qubole logo Qubole

Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.
  • Amazon Forecast Landing page
    Landing page //
    2022-02-05
  • Qubole Landing page
    Landing page //
    2023-06-22

Amazon Forecast videos

Learn How to Accurately Forecast Demand with Amazon Forecast - AWS Online Tech Talks

More videos:

  • Review - Amazon Forecast Overview
  • Review - AWS re:Invent 2020: Building a successful inventory planning solution with Amazon Forecast

Qubole videos

Fast and Cost Effective Machine Learning Deployment with S3, Qubole, and Spark

More videos:

  • Review - Migrating Big Data to the Cloud: WANdisco, GigaOM and Qubole
  • Review - Democratizing Data with Qubole

Category Popularity

0-100% (relative to Amazon Forecast and Qubole)
Data Science And Machine Learning
Data Dashboard
24 24%
76% 76
Big Data
0 0%
100% 100
Data Science Tools
100 100%
0% 0

User comments

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Social recommendations and mentions

Based on our record, Amazon Forecast seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Amazon Forecast mentions (4)

  • Beginning the Journey into ML, AI and GenAI on AWS
    Generative Artificial Intelligence (GenAI) is a type of artificial intelligence that can generate text, images, or other media using generative models. AWS offers a range of services for building and scaling generative AI applications, including Amazon SageMaker, Amazon Rekognition, AWS DeepRacer, and Amazon Forecast. AWS has also invested in developing foundation models (FMs) for generative AI, which are... - Source: dev.to / 3 months ago
  • [Discussion] Amazon's AutoML vs. open source statistical methods
    In this reproducible experiment, we compare Amazon Forecast and StatsForecast a python open-source library for statistical methods. Source: over 1 year ago
  • How to forecast or predict data?
    It sounds like you need something that mostly runs itself, without you necessarily needing to have in-depth knowledge of time series modeling. If you have an AWS account, I'd recommend checking out Amazon Forecast. One of the recommendations I saw in this thread is to run auto.arima in R. That's actually one of the algorithms AWS will run for you, among others. I don't know if it handles differencing and... Source: over 2 years ago
  • AWS Machine Learning Tools in 2021
    With the help of Amazon Forecast, the forecasting technology at the heart of Amazon.com, it is now possible to build forecasting models for your own applications. - Source: dev.to / about 3 years ago

Qubole mentions (0)

We have not tracked any mentions of Qubole yet. Tracking of Qubole recommendations started around Mar 2021.

What are some alternatives?

When comparing Amazon Forecast and Qubole, you can also consider the following products

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

machine-learning in Python - Do you want to do machine learning using Python, but you’re having trouble getting started? In this post, you will complete your first machine learning project using Python.

MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming

Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.