Kaggle
Colaboratory
Driven Data
HackerRank
Numerai
DataSource.ai
Infosec Skills
DataHack & DSAT
Algorithmia
MCenter
5Analytics
Spell
neptune.ai
MuleSoft Anypoint Platform
Zapier
Datadog
Kaggle AlgorithmiaAlgorithmia is recommended for data scientists, machine learning engineers, and developers who need a flexible and scalable environment to deploy, manage, and share AI and machine learning models. It is particularly suitable for teams seeking to collaborate and leverage pre-built algorithms from a community-driven marketplace. Businesses looking to integrate machine learning capabilities into their operations without extensive infrastructure management will also benefit from Algorithmia's offerings.
Based on our record, Kaggle seems to be a lot more popular than Algorithmia. While we know about 103 links to Kaggle, we've tracked only 5 mentions of Algorithmia. 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.
A good example: the results we published are one-shot success rates with no retries and no manual intervention. But we did re-run some failed tasks afterward. Take Task #197 on kaggle.com ("Identify the ongoing competition that offers the highest prize and find the code that received the most votes in that competition"). In our benchmark submission, it failed on an anti-bot block. On a subsequent run, TinyFish... - Source: dev.to / 3 months ago
The key to mastering data analysis is practice. Kaggle.com and World Bank provide hands-on experience with real-world data, helping you consolidate your learning and apply your skills. Trying small projects like: Analyzing Netflix ratings, Visualizing COVID-19 data and Cleaning messy sales data in Excel can help strengthen your skill. - Source: dev.to / about 1 year ago
Before you even build a model, you are going to need some kind of dataset. Usually a CSV or JSON file. You can build your own dataset from scratch using your own data, scrape data from somewhere, or use Kaggle. - Source: dev.to / over 1 year ago
Kaggle: For data science and machine learning competitions. - Source: dev.to / almost 2 years ago
Need help with last minute python project (due today). Project involves choosing a dataset from kaggle.com to analyze and creating questions to answer through analyzing the data. I have a pdf file of the project guidelines if you want more details. Also on a budget. Source: about 3 years ago
To push a model into production, there are additional concerns which the tools in the versioning, deployment and release space aim to solve. This includes obtaining adequate infrastructure to run the model reliably and facilitating easy model release or rollback. Solutions in the MLOps space includes Kubeflow, Pachyderm and Algorithmia. - Source: dev.to / over 4 years ago
And for enterprises that want to do the same with ML you can use algorithmia.com. Source: over 4 years ago
Algorithmia advertises themselves as an MLops platform for data scientists, and they provide an easy way to host models on a scalable REST API. Source: over 4 years ago
Seems similar to https://algorithmia.com. Source: over 4 years ago
Algorithmia.com โ Host algorithms for free. Includes free monthly allowance for running algorithms. Now with CLI support. - Source: dev.to / about 5 years ago
Colaboratory - Free Jupyter notebook environment in the cloud.
MCenter - Machine Learning Operationalization
Driven Data - DrivenData hosts data science competitions to build a better world, bringing cutting-edge predictive models to organizations tackling the world's toughest problems.
5Analytics - The 5Analytics AI platform enables you to use artificial intelligence to automate important commercial decisions and implement digital business models.
HackerRank - HackerRank is a platform that allows companies to conduct interviews remotely to hire developers and for technical assessment purposes.
Spell - Deep Learning and AI accessible to everyone