Data Science Tools
Cloud Computing
On-demand availability of computer system resources, especially data storage and computing power. Used in Omdena’s Remote Sensing, NLP, and Computer Vision projects.
Data Labeling
Using different tools to identify raw data (images, text files, videos, etc.) and add labels to provide context so that a machine learning model can learn from it.
Deployment Platforms
Easy-to-use platforms that provide no or few lines of code to deploy final Machine Learning models.
Google Cloud is a suite of cloud computing services that plays an important role in high power computing. In Omdena projects, collaborators use Google Workspace (G Suite), and application programming interfaces (APIs) for machine learning and enterprise mapping services.
When public cloud computing is needed, Omdena’s collaborators get access to Azure. In many applications like Remote Sensing and Computer Vision with big datasets, power computing with Azure is a key tool to use.
Labeling data is made easier with this automated labeling tool – recently raised $50 M.
It is widely used in Omdena’s challenges in annotating text for Natural Language Processing tasks and in annotating images for Computer Vision or Remote Sensing tasks.
MLOps within an intuitive UI – recently raised $15 M.
A platform that combines Jupyter notebooks with powerful tools for model training, hyperparameter search, and experiment management. Now that’s how MLOps should be.
Activeloop YC 2019 – Their Hub uses Python API that makes data streaming and Git versioning easier. Also, it facilitates deployment on AWS, Azure, Google cloud, and their own storage.
Automatic workflows. Integrated Git, DVC & MLflow. Open core. Log experiments with ease using the DAGsHub logger to visualize and compare the performance. Review & merge data + models alongside your code.
A no-code app builder for your machine learning models
It helps to transform machine learning models into apps in minutes.
Apify can automate anything you can do manually in a web browser, and run it at scale. A one-stop shop for web scraping, data extraction, and web RPA.
They have API building, web automation, and other services but we mainly focused on web scraping, like web scraper, Google search scraper, Twitter scraper, etc.
Crawl arbitrary websites, extract structured data from them, and export it to formats such as Excel, CSV, or JSON.
It offers multiple types of storage and buckets to choose from. It can be used for file indexing and storage, archiving for a longer time, high-performance writing or reading, and running critical applications.
It offers the distributed version control and source code management functionality of Git, plus its own features. Used in all Omdena’s challenges for code hosting and management. Git simplifies the process of working with other team members and makes it easy to collaborate on projects. Team members can work on files and easily merge their changes with the master branch of the project.
An open-source Python library that makes it easy to create and share interactive, custom web apps for machine learning and data science. It allows building and deploying powerful data apps. Omdena teams use it to make insightful and interactive apps.
Learn by doing
Apply Data Science skills in solving real-world problems while using and practicing best-integrated Machine Learning in market tools.