Software Alternatives & Reviews

KNIME Analytics Platform VS Amazon SageMaker

Compare KNIME Analytics Platform VS Amazon SageMaker and see what are their differences

KNIME Analytics Platform logo KNIME Analytics Platform

Predictive Analytics

Amazon SageMaker logo Amazon SageMaker

Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
  • KNIME Analytics Platform Landing page
    Landing page //
    2023-10-22
  • Amazon SageMaker Landing page
    Landing page //
    2023-03-15

KNIME Analytics Platform

Categories
  • Statistics
  • Technical Computing
  • Data Analysis
  • Numerical Computation
Website knime.com  

Amazon SageMaker

Categories
  • Machine Learning
  • AI
  • Office & Productivity
  • Data Science And Machine Learning
Website aws.amazon.com  

KNIME Analytics Platform videos

KNIME tutorial: Performing joins on KNIME Analytics Platform

Amazon SageMaker videos

Build, Train and Deploy Machine Learning Models on AWS with Amazon SageMaker - AWS Online Tech Talks

More videos:

  • Review - An overview of Amazon SageMaker (November 2017)

Category Popularity

0-100% (relative to KNIME Analytics Platform and Amazon SageMaker)
Technical Computing
100 100%
0% 0
Data Science And Machine Learning
Numerical Computation
100 100%
0% 0
Machine Learning
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare KNIME Analytics Platform and Amazon SageMaker

KNIME Analytics Platform Reviews

Top 10 Tableau Open Source Alternatives: A Comprehensive List
Knime Analytics Platform is an open-source Business Intelligence software that has been developed as an integration platform for creating analytical reports. It is a software that might be difficult for a novice to use. However, for Data Scientists and other Data professionals, particularly those who want to work with R, Python, or other Predictive Machine Learning tools,...
Source: hevodata.com

Amazon SageMaker Reviews

7 best Colab alternatives in 2023
Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning. It allows users to write code, track experiments, visualize data, and perform debugging and monitoring all within a single, integrated visual interface, making the process of developing, testing, and deploying models much more manageable.
Source: deepnote.com

Social recommendations and mentions

Based on our record, Amazon SageMaker seems to be more popular. It has been mentiond 34 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.

KNIME Analytics Platform mentions (0)

We have not tracked any mentions of KNIME Analytics Platform yet. Tracking of KNIME Analytics Platform recommendations started around Mar 2021.

Amazon SageMaker mentions (34)

  • 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 / about 2 months ago
  • Technical Architecture for LLMOps
    Amazon and Azure already have much of what you're talking about in AWS SageMaker and Azure MLOps. Source: 10 months ago
  • Are AI fine-tuning tools worth learning and investing?
    And there have been several platforms that help fine-tune pretrained models, such as Google Cloud AutoML and Amazon Sagemaker. These tools are often fairly easy to use, but they come at a cost. They can be expensive, depending on the size of your dataset. Another option is Finetuner+, that also fine-tunes like AutoML and Sagemaker. The big advantage is that you don't need to transfer your data to other GPUs,... Source: 11 months ago
  • Live object recognition system using Kinesis and SageMaker
    In this blog, you will see how to ingest audio/video feeds from any live recording camera into the AWS Kinesis Video Stream and apply various machine learning algorithms on the video stream to analyze these video feeds using Amazon SageMaker. We'll also use a few other services like Lambda, S3, EC2, IAM, ECS, CloudFormation, ECR, CloudWatch, etc. To power our model. Source: 11 months ago
  • Instance type or cost for an NLP server?
    First, can you use a different AWS service, such as Comprehend or SageMaker? You only "pay for what you use" instead of paying for an idle server. This is especially helpful for a start up, since you don't pay a lot if you don't have a lot of customers.. Source: about 1 year ago
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What are some alternatives?

When comparing KNIME Analytics Platform and Amazon SageMaker, you can also consider the following products

RStudio - RStudio™ is a new integrated development environment (IDE) for R.

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

IBM SPSS Statistics - IBM SPSS Statistics is software that provides detailed analysis of statistical data. The company behind the product practically needs no introduction, as it's been a staple of the technology industry for over 100 years.

IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.

RapidMiner - RapidMiner is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.

Azure Machine Learning Studio - Azure Machine Learning Studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure.