Create a report between 4 different cloud platform (aws,azure,firebase ml, dataiku) for ML task from data loading ,model creation to deployment for tabular data -- 2
Budget: ₹600 – ₹1,500 INR
There are following factors we can consider for choosing a machine learning (ML) platform for tablular data(no image or text only number and category):
{PRICING IS MOST IMPORTANT}
Data: The platform should be able to handle the size, format, and complexity of our data.
Algorithms and models: The platform should support the types of algorithms and models that we need to use.
Ease of use: The platform should be easy to use and have a user-friendly interface.
Performance: The platform should be able to handle the computational demands of our ML tasks in a timely manner.
Integration: The platform should be able to integrate with other tools and systems that we use.
Scalability: The platform should be able to handle increasing amounts of data and computational demands as our ML needs grow.
Cost: The platform should be affordable and offer a pricing model that meets our needs.
Support: The platform should offer good documentation and support resources, such as tutorials, forums, and customer service.
Community: The platform should have a strong community of users who can provide help and guidance.
Future development: The platform should be actively developed and supported, with a roadmap for future improvements.
Performance monitoring: The platform should have tools for monitoring and analyzing the performance of your ML models.
Some other points which are also important
Stable :The Platform should not give too issues itself for development
Flexibility: The platform should allow you to customize and fine-tune your ML models to suit your specific needs.
Collaboration: The platform should have features that support collaboration and teamwork, such as version control and project management tools.
Deployment: The platform should make it easy to deploy your ML models into production environments.
Security: The platform should have robust security features to protect your data and models.
i am looking these details for aws,azure,firebase ml, dataiku platform.
{PRICING IS MOST IMPORTANT}
Data: The platform should be able to handle the size, format, and complexity of our data.
Algorithms and models: The platform should support the types of algorithms and models that we need to use.
Ease of use: The platform should be easy to use and have a user-friendly interface.
Performance: The platform should be able to handle the computational demands of our ML tasks in a timely manner.
Integration: The platform should be able to integrate with other tools and systems that we use.
Scalability: The platform should be able to handle increasing amounts of data and computational demands as our ML needs grow.
Cost: The platform should be affordable and offer a pricing model that meets our needs.
Support: The platform should offer good documentation and support resources, such as tutorials, forums, and customer service.
Community: The platform should have a strong community of users who can provide help and guidance.
Future development: The platform should be actively developed and supported, with a roadmap for future improvements.
Performance monitoring: The platform should have tools for monitoring and analyzing the performance of your ML models.
Some other points which are also important
Stable :The Platform should not give too issues itself for development
Flexibility: The platform should allow you to customize and fine-tune your ML models to suit your specific needs.
Collaboration: The platform should have features that support collaboration and teamwork, such as version control and project management tools.
Deployment: The platform should make it easy to deploy your ML models into production environments.
Security: The platform should have robust security features to protect your data and models.
i am looking these details for aws,azure,firebase ml, dataiku platform.