These four automated machine learning tools will help you build ML models quickly for your Data Science projects.
Image by pch.vector from Freepik
With the increase in the data of the more prominent organization, people showed to understand the pattern of sales, marketing, etc., which was being formulated by the vast data being available in the particular organization, people were more and more inclined to learn Machine learning and Data analysis than ever before and this urge is going to retain even post-pandemic.
While going through Data handling and hyperparameter tuning for Machine Learning projects, you would have felt the thirst for an automated method that would have saved your time from the conceivable exhaustive process of tuning the billion of parameters followed by trying and testing the different models, which would adequately fit your training dataset.
The answer to this need is YES; in today’s world, many such tools would not only automate the data handling stage but helps in choosing the relevant model for predictive analysis on the testing dataset.
Image by Analytics Vidhya
Therefore, the is a need for Automated Machine learning (AutoML).
So, in this article, I will give you a brief idea about AutoML in the present times.
In simple terms, you can think of Automated machine learning as applying Machine Learning (ML) models to real-world problems by only initiating the process by running specific commands. Eventually, the rest of the work, pipelines, will be taken care of. Specifically, this process automates several steps in the general machine-learning pipeline, such as choosing the best model for our dataset, hyperparameter tuning using cross-validation, etc. Suppose we are curious about thinking of internal working. In that case, the tool will create different pipelines by choosing the different values of hyperparameters and then selecting the pipeline that provides better evaluation metrics on the test dataset.
Open-source and enterprise AutoML solutions differ significantly: open-source solutions can only automate algorithm selection and hyperparameter tuning, whereas enterprise solutions can do much more (see section “What can we expect from an AutoML tool”). Furthermore, the results obtained with open-source solutions are far inferior to those obtained with enterprise solutions.
Google Cloud AutoML, Microsoft Azure AutoML, H2O.ai, and TPOT are popular automated machine learning (AutoML) tools that provide an easier way to build and deploy machine learning models without requiring coding and data science expertise. However, each tool has its strengths and limitations.
H20, in my opinion, is the best open-source platform for democratizing machine learning. Its comprehensive scope and the H2O Flow web-based interface place it first among open-source solutions. I created a machine learning project for customer churn from the ground up without writing a single line of code.
H20 Driverless AI is the most comprehensive, customizable, and agnostic enterprise solution. While maintaining high control and understanding of the modeling, I quickly generated a model for customer churn that was better than the one from H20-3.
In conclusion, I hope you have enjoyed this article and found it informative. If you have any suggestions or feedback, please contact me via LinkedIn.
Aryan Garg is a B.Tech. Electrical Engineering student, currently in the final year of his undergrad. His interest lies in the field of Web Development and Machine Learning. He have pursued this interest and am eager to work more in these directions.
By subscribing you accept KDnuggets Privacy Policy
Get the FREE ebook ‘The Great Big Natural Language Processing Primer’ and ‘The Complete Collection of Data Science Cheat Sheets’ along with the leading newsletter on Data Science, Machine Learning, AI & Analytics straight to your inbox.
By subscribing you accept KDnuggets Privacy Policy
Subscribe To Our Newsletter
(Get The Complete Collection of Data Science Cheat Sheets & Great Big NLP Primer ebook)
Get the FREE ebook ‘The Great Big Natural Language Processing Primer’ and ‘The Complete Collection of Data Science Cheat Sheets’ along with the leading newsletter on Data Science, Machine Learning, AI & Analytics straight to your inbox.
By subscribing you accept KDnuggets Privacy Policy
Get the FREE ebook ‘The Great Big Natural Language Processing Primer’ and ‘The Complete Collection of Data Science Cheat Sheets’ along with the leading newsletter on Data Science, Machine Learning, AI & Analytics straight to your inbox.
By subscribing you accept KDnuggets Privacy Policy