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
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.
- Improves Work Efficiency
- Superior Final Results
- Minimizing Errors
- Out-Scales Machine Learning
Top 10 AutoML Frameworks for 2023
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.
TPot is also known as Tree Pipeline Optimizer Tool. It is an open-source python autoML software package. It is focused on optimizing machine-learning pipelines through genetic programming.
TPOT’s main purpose is to automate building ML pipelines. This is done by combining a flexible expression-tree representation of pipelines with stochastic searching algorithms like genetic programming.
Note that TPOT works on top, You should first install the scikit-learn library.
AutoKeras is an Open-Source Library that can be used for AutoML and deep learning models. It was originally created by DATA Lab.
Auto Keras makes it easy for non-expert deep learning and machine engineers to train and run their models efficiently. Auto Keras, which aims to make machine learning accessible to all, is an excellent tool for beginners.
Ludwig is a free open-source autoML framework that focuses on building and training deep learning models with a simple configuration file.
The Ludwig framework can use the configuration files of its users to define the inputs and outputs of a model along with the respective data types. This data will be used by the Ludwig framework in order to build its deep learning model that is based on the attributes mentioned.
MLBOX has been on the rise and is quickly becoming one of the most popular automated machine-learning framework tools.
It offers the following benefits, according to MLBOX documentation:
- Fast reading and distributed data preprocessing/cleaning/formatting.
- The selection of robust features and leak detection is key.
- High-dimensional optimization with hyperparameter accuracy
- Predictive models that are state-of-the-art for classification and regression (Deep Learning and Stacking, LightGBM, and many more).
- Prediction using model interpretation
Also read: Top 10 Machine Learning Platforms in 2022
AutoGloun is intended for both experts and non-experts in machine learning. It focuses on automated stack assembly, deep learning, and real-world applications that span image, text, and tabular data.
AutoGLoun, according to the online documentation of AutoGloun, allows a user to:
- With just a few lines of code, quickly prototype classical ML and deep learning solutions for raw data.
- Automate the use of state-of-the-art techniques (where applicable) without needing expert knowledge.
- Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing.
- AutoGluon can be used to customize AutoGluon for specific uses or improve/tune existing models and data pipelines.
8. Microsoft Neural Network Intelligence (NNI)
Microsoft Neural Network Intelligence (also known as NNI) is a toolkit that automates feature engineering, neural architecture search, and hyperparameter tuning. It also allows for model compression for deep learning.
The NNI tool supports frameworks like PyTorch and TensorFlow, Scikitearn, and Scikit-learn. XGBoost, LightGBM. The Microsoft Neural Network’s main advantage is its neural architecture search. The NNI tool supports both Multi-trail (grid searching, regularized evolution, etc.) and One-shot (DARTS ENAS FBNet, etc.
This tool provides several hyper-parameter tuning algorithms, such as Bayesian optimization and exhaustive search. For more information about what this tool can do, see the readme file for the NNI on Github.
TransmogrifAI was created to aid developers in increasing their machine learning productivity. Apache Spark is the backbone of TransmogrifAI.
As briefly mentioned in the GitHub Readme file on Transmogrif, “Through Automation, it achieves accuracy close to hand-tuned model with almost 100x reductions in time.”
Similar to the other autoML Frameworks the TransmogrifAI tool can choose the best algorithm for any user’s dataset.
10. H2O AutoML
H2O AutoML is an open-source framework tool developed by H2O that supports both R programming and Python programming.
It supports all the most popular statistical and machine-learning algorithms, including gradient-boosted machines and generalized linear models.
H2O autoML interface allows new machine-learning users to be accommodated by asking for as little as possible. The H2O tool’s primary task is for the user to give the data.
GitHub Repositories for Auto Machine Learning
With advances in machine learning and deep learning, There has been a significant increase in demand for machine-learning experts.
This is where automation of machine-learning tools and techniques can be very useful, that allows new users to build highly optimized functional models with greater ease than ever.
In short, When searching for the best automation machine-learning tool You should be focused on the goals you have for your model and the parts of the machine-learning process that you want to automate. We recommend that you try a few of the AutoML tools mentioned above and then only keep with those that are efficient and easy to use.