Discover the top automated machine learning platforms for 2024 that are revolutionizing the way businesses leverage AI.
Table of Content
Introduction
1. Auto-SKLearn
2. Google AutoML Cloud
3. Auto-Keras
4. TransmogrifAI
Conclusion
Introduction
With the rapid growth in the digital world, organizations are implementing Automated Machine Learning (AutoML) that helps data scientists and MLOps teams automate the training, tuning, and deployment of machine learning (ML) models. This technology will save time and resources for the data scientists and MLOps teams, which will accelerate research on ML and solve specific problems related to ML models.
For instance, some AutoML tools focus on optimizing ML models for a given dataset, while others focus on finding the best model for specific tasks, such as picking the appropriate ML algorithm for a given situation, preprocessing the data, and optimizing the model’s hyperparameters, aiding different industries to predict customer behavior, detect fraud, and improve supply chain efficiency.
Therefore, AutoML is a powerful mechanism that makes ML models more accessible and efficient; however, to create a model, execute stratified cross-validation, and evaluate classification metrics, data scientists and MLOps teams need the right set of AutoML tools or platforms.
In today’s AI TechPark article, we will introduce you to the top four AutoML tools and platforms that simplify using ML algorithms.
1. Auto-SKLearn
Auto-SKLearn is an AutoML toolkit that is available as an open-source software library that can automate the process of developing and selecting the correct ML models using the Python programming language. The software package includes attributes that are used in engineering methods such as One-Hot, digital feature standardization, and PCA. It improvises the model and operates SKLearn estimators to process classification and regression problems. Furthermore, Auto-SKLearn builds a pipeline and utilizes Bayes search to optimize that channel, adding two components for hyper-parameter tuning using Bayesian reasoning: The tools also have an inbuilt meta-learning feature that is used to format optimizers using Bayes and assess the auto-collection structure of the arrangement during the optimization process.
2. Google AutoML Cloud
The Google Cloud AutoML suite is designed to make it easiest for data scientists and MLops teams to apply ML-specific tasks such as image and speech recognition, natural language processing, and language translation in business. The platform accelerates the process of building custom AI solutions with a variety of open-source tools and proprietary technology that Google has evolved over the last decade. AutoML supports homegrown TensorFlow and offers partially pre-trained features for designing custom solutions using smaller data sets.
3. Auto-Keras
Auto-Keras is an open-source software library for AutoML developed by DATA Lab and helps data scientists create a deep learning (DL) framework. The platform provides processes to automatically search for the architecture and hyper-parameters of DL models. Auto-Keras offers superior-level APIs such as TextClassifier and ImageClassifier that solve any ML problem with just a few codes. For instance, Auto-Keras simplifies the ML models by using automatic Neural Architecture Search (NAS) algorithms; these NAS algorithms automatically adjust models to replace DL engineers.
4. TransmogrifAI
The most famous open-source library in AutoML is TransmogrifAI, which is built on Scala and SparkML, aiding data scientists to rapidly produce data-efficient models for heterogeneous structured data on a large scale. With a few codes, data professionals could easily automate the data cleansing process, use feature engineering in designing new ML models, and select the right model to further explore and iterate the datasets.
Conclusion
In this competitive economy, organizations are looking for AI, ML, and DL solutions that will help them transform big data into actionable insights, reach a target audience, improve decision-making, and streamline business processes. However, the whole process of implementing these solutions can be automated with the help of the above AutoML platforms. These AutoML platforms can automate repetitive tasks for data scientists and MLops teams, allowing them to spend more time-solving other business problems.
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