Top 10 AutoML Frameworks for 2022
Machine learning and artificial intelligence have been the two most important topics in the past 20 years. Machine learning and data science engineers must do a lot of hard work and research to be able to run their models efficiently.
They may vary depending on the individual, but they all include the following:
- Data Acquisition
- Data Exploration
- Data preparation
- Feature Engineering
- Model Selection
- Model Training
- Hyper Parameter Tuning
- Predictions
Although 8 steps might seem small when it comes to building a machine-learning model, each step will take a lot of time!
This problem is made worse when machine learning professionals who are not experts go through the steps for the first time. The process will typically take more time and require more resources, and the final result might not be what you expected.
AutoML automates a large portion of the model creation process, for experts and non-experts alike.
What Is Automated Machine Learning (AutoML)?
Automatic Machine Learning is also known and commonly referred to as AutoML. It’s machine learning made simpler. AutoML makes machine learning easier for non-machine learners by using automated processing with given frameworks.
It is focused on speeding up the research and improvement of machine learning models.
The traditional machine learning process is focused on the 8 steps mentioned previously, but AutoML covers 2 steps.
Data acquisition is the act of collecting, filtering, and data cleaning before it is stored in a data warehouse.
Predictions are the output of a model. A well-trained model will most probably return accurate predictions.
The other six steps will be covered by frameworks for data exploration and preparation, feature engineering, feature engineering, model selection, and training, as well as tuning the final model.
AutoML’s Advantages
- Improves Work Efficiency
- Superior Final Results
- Minimizing Errors
- Out-Scales Machine Learning
Top 10 AutoML Frameworks for 2022
After we have discussed AutoML and outlined some of its benefits, we will now discuss the top 10 AutoML frameworks and where they can be found. We’ll also cover their functionalities.
1. Google AutoML
Google AutoML is the most well-known framework, earning it the number one position on our list. Google has released many AutoML frameworks, including Google AutoML vision and Google AutoML Natural Language.
2. Auto SKLearn
SKLearn is a name that may be familiar to users who have tried machine learning before. This add-on was created for the scikit-learn library. Auto SKLearn is an open-source machine-learning framework that handles automated machine-learning tasks.
Auto Sklearn Framework is capable of performing model selection and hyperparameter tuning. This unique feature makes the Auto SKlearn framework stand out.
By performing its model selection, Auto SKlearn will automatically find the best algorithm that can handle the user’s given problem.
We now move on to Auto SKlearn’s second functionality: hyperparameter tuning. Users should determine the optimal parameters for any machine or deep-learning model as this is the last step of any machine. This task is time-consuming and can be automated with such frameworks.
Auto SKlearn’s ability to automatically characterize is the most important and last benefit. Characterization refers to the transformation of raw data into usable information.
Originally published on The Tech Trend
