ML Frameworks to the rescue for Citizen Data Scientists

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.”

ML Framework

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:

  1. TensorFlow
  2. Alteryx Analytics
  3. H2O.ai
  4. KNIME Analytics Platform
  5. RapidMiner
  6. SAS
  7. MathWorks’ MATLAB and Simulink
  8. Databricks Unified Analytics Platform
  9. Microsoft’s Azure ML Studio
  10. 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.

Munira Gandhi is a data & analytics practice manager at Miracle Software Systems, with over 16+ years of as Enterprise Information/Data architect focused on all data aspects (data ingestion, integration, analytics). She is AWS cloud architect certified and is working on Google GCP Data Engineer certification.

Specialties :
- Big Data Architecture and Strategy (Hadoop ecosystem; Google Bigquery)
- Data Science and Analytics (Python)
- Cloud
- Business Intelligence (Power BI, Tableau, Spotfire)
- Oil & Gas domain knowledge expert

About the author

Munira Gandhi
Munira Gandhi

Munira Gandhi is a data & analytics practice manager at Miracle Software Systems, with over 16+ years of as Enterprise Information/Data architect focused on all data aspects (data ingestion, integration, analytics). She is AWS cloud architect certified and is working on Google GCP Data Engineer certification.

Specialties :
- Big Data Architecture and Strategy (Hadoop ecosystem; Google Bigquery)
- Data Science and Analytics (Python)
- Cloud
- Business Intelligence (Power BI, Tableau, Spotfire)
- Oil & Gas domain knowledge expert

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Munira Gandhi By Munira Gandhi
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