What is an ML Framework?
A Machine Learning Framework is defined as “an interface, library or tool which allows developers to more easily and quickly build machine learning models, without getting into the nitty-gritty of the underlying algorithms.”
Machine Learning Platform
A Machine Learning platform is tasked with “automating and accelerating the delivery lifecycle of predictive applications;” it is capable of processing big data quantities using machine learning. The platform incorporates ML framework and tools for efficiency and ease for other software integration.
Top 10 Machine Learning Platforms are as follows:
- TensorFlow
- Alteryx Analytics
- H2O.ai
- KNIME Analytics Platform
- RapidMiner
- SAS
- MathWorks’ MATLAB and Simulink
- Databricks Unified Analytics Platform
- Microsoft’s Azure ML Studio
- AWS SageMaker
Assess and evaluate the business use case and apply the Machine Learning framework and/or platform of your choice.
ML Frameworks are great for citizen data scientists and business analysts who understand the business domain and want to tinker with data to see the output for their business use case.
You already have the domain knowledge so you are one step ahead in the game.
We recommend doing some crash courses around ML to understand what and how to apply ML to your data. Here is a good one from Google that is free: https://developers.google.com/machine-learning/crash-course/ml-intro
I’m glad that these platforms help citizen data scientists to be part of the data science/ML world, as it is easy to use with a friendly interface.
In Conclusion
If you have not started your data science/ML journey and are scared as you don’t have a Ph.D. then this is your tool. Play with it, master it, enjoy it.
I’m positive and confident that you will be able to master the knowledge and enjoy a successful career change.
