What are the tools?
We are often obsessed by many cloud based Machine Learning(ML) tools that helps us to get the job done. But the main purpose of the tools are to use few tools(One tool) to make the ML models and deploy it for the real life and business.
Cloud based tools
Google Colab
Free GPU powered Jupyter Notebook. A Colaboratory, or “Colab” for short, is a product from Google Research. Colab allows us to write and execute arbitrary python code through the browser, and is especially well suited to machine learning, data analysis and education.
AWS(Amazon Web Services)
is a secure cloud services platform, offering compute power, database storage, content delivery and other functionality to help businesses scale and grow. Among many other AWS offer Sagemaker a tool that help data scientist, AI researcher, ML engineer to develop model on Jupyter Notebook like environment.
Google Cloud Platform(GCP)
GCP AI platform is a tool made by google, the only company in the world right now who might use AI and ML in almost in all their products and services.
AI Platform makes it easy for developers, data scientists, and data engineers to streamline their ML workflows. Whether it is point-and-click data science using AutoML(Automatic Machine learning) or advanced model optimization, the AI platform helps all users take their projects from idea to application in real.
Azure Machine learning
As per the company Microsoft, it is enterprise- grade machine learning service to build and deploy machine learning models faster. It empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster.
The tool is much popular for its visual no code interface. Also possible to code in the same interface like Jupyter Notebook.
Supports many open source frameworks like PyTorch, TensorFlow, and scikit-learn, or the open and interoperable ONNX format.
Many more
There are many more cloud services for machine learning, but the above mentioned four are the most popular.
Few others we can mentioned here:
FloydHub– A deep learning platform.
Paperspace– Cloud Machine Learning, AI, and effortless GPU infrastructure.
etc..
Conclusion
Many companies nowadays moving towards cloud services for machine learning as data is getting bigger in size and velocity. Computing these huge data locally getting more expensive in buying hardware. On the other hand pay-as-you-go payment strategy on cloud service makes it inexpensive and faster.
As cloud is getting more popular, it’s better to have hands on experiences on one or two above mentioned services to have competitive advantages.
If you have hands on experiences on any another services then please comment below.
(All the images are from the respective company web sites)
