Need advice about which tool to choose?Ask the StackShare community!
Add tool
Amazon SageMaker vs wise.io: What are the differences?
Amazon SageMaker: Accelerated Machine Learning. A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale; wise.io: Machine Learning as a Service and Big Data Analytics. Wise.io builds machine intelligence products that make it easy for companies to derive actionable insight from their greatest corporate resource: their data.
Amazon SageMaker and wise.io belong to "Machine Learning as a Service" category of the tech stack.
Some of the features offered by Amazon SageMaker are:
- Build: managed notebooks for authoring models, built-in high-performance algorithms, broad framework support
- Train: one-click training, authentic model tuning
- Deploy: one-click deployment, automatic A/B testing, fully-managed hosting with auto-scaling
On the other hand, wise.io provides the following key features:
- Use Wise.io for: Fraud detection, Intelligent sensors, Ad Targeting & Personalization, Genomics, Business Analytics, Finance, Healthcare, Sentiment Analysis
- Dead simple machine learning.- Our intuitive, easy-to-use platform for machine learning enables anyone to build and deploy models with a few simple clicks.
- A data science marketplace.- With the feature marketplace, we provide companies access to an expansive knowledge base.
Manage your open source components, licenses, and vulnerabilities
Learn More98
3K
6
What is Amazon SageMaker?
A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
What is wise.io?
Wise.io builds machine intelligence products that make it easy for companies to derive actionable insight from their greatest corporate resource: their data.
Need advice about which tool to choose?Ask the StackShare community!
Jobs that mention Amazon SageMaker and wise.io as a desired skillset
What companies use Amazon SageMaker?
What companies use wise.io?
What companies use Amazon SageMaker?
What companies use wise.io?
No companies found
Manage your open source components, licenses, and vulnerabilities
Learn MoreSign up to get full access to all the companiesMake informed product decisions
What tools integrate with Amazon SageMaker?
What tools integrate with wise.io?
What tools integrate with Amazon SageMaker?
What tools integrate with wise.io?
No integrations found
Sign up to get full access to all the tool integrationsMake informed product decisions
What are some alternatives to Amazon SageMaker and wise.io?
Amazon Machine Learning
This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
Databricks
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
Azure Machine Learning
Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
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
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.