The Top AutoML Frameworks You Should Consider in 2023
AutoML frameworks are powerful tool for data analysts and machine learning specialists that can automate data preprocessing, model selection, hyperparameter tuning, and even perform complex tasks like feature engineering.
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Artificial intelligence (AI) will transform our society in the future. The huge demand for AI specialists, data analysts, and data specialists means that organizations that wish to succeed in the competitive tech landscape must be as efficient as possible when allocating resources. With a shortage of AI professionals, it’s crucial for the exploration and prediction of data to be as automated as possible to allow specialists to focus on more important tasks.
Automatic machine learning (AutoML) frameworks do just this. By automating the selection, construction, and parameterization of data models, AutoML frameworks can free up time for data specialists to spend time on more complex aspects of analysis. This article will discuss the basics of AutoML frameworks, how they can help develop AI projects, and which AutoML frameworks are the best to use in 2023.
Why use AutoML frameworks?
Data sets allow analysts to create predictive models. While machine learning can take data and form predictive models, the process is often arduous and time-consuming. Traditional machine learning involves data processing, determining targeted data features, finding the most accurate learning model, refining necessary hyperparameters, and training your learning model with the best-performing parameters. When some or all of this process can be automated, results can be obtained much quicker.
AutoML and AI can open the doors to new possibilities, but they can also be dangerous weapons in the wrong hands. Automatic machine learning and AI are increasingly becoming a threat. Cyberattacks, identity theft, and credit card scams can all be carried out with the aid of AI or automatic machine learning because they offer the potential to quickly run through a series of data and find a match.
AutoML frameworks eliminate the tedious aspect of a data analyst’s job by handling routine sequences and eliminating manual testing of models. AutoML can automate the process of gathering and organizing data and help test out model hyperparameters. Here are the best AutoML frameworks to help data specialists accomplish more on their projects.
Created in the Scala language and based on the SparkML framework, TransmogrifAI automates five of the most important functions of machine learning. Optimizing hyperparameters, checking functions, selecting models, deriving functions, and transmogrification (hence its name) are all functions that the framework can automate. This can be very useful for data cleansing, model selection, and engineering features.
AutoGluon is an open-source library from AWS that is mostly geared toward machine learning application developers. This is an ideal AutoML framework for those who aren’t experts because it is very user-friendly yet provides access to powerful deep-learning methods. Forecasts can be achieved quickly and with a high level of accuracy. It can also be a great starting point for those looking to explore what you can achieve with a given dataset.
AutoGluon is great for automatic object recognition, tabular forecasting, and organizing data in the form of text and images. For more expert users, the AutoGluon AutoML framework provides a high level of insight into the programming behind the model parameters through its open library.
MLJAR is accessible through a browser and can be used as a platform for quickly creating and testing AutoML models. Compatible with NVIDIA’s CUDA, Python, and Tensorflow, it also provides Hyperfeit search functionality in an easy-to-use interface. Datasets can be easily downloaded to the site, and machine learning algorithms can be tried and tested to identify the optimal algorithm. Identifying and deploying the best predictive model is made a lot simpler with the MLJAR AutoML framework, and you can even share your results.
This is clearly one of the better AutoML frameworks, but it does come with a price. Users must pay a subscription to use all the features, or they are limited to the complimentary version, with a data limitation of 0.25 GB.
Perhaps the most easily recognizable name on the list, DataRobot is a popular AutoML framework for use among data analysts. Predictive analytics can be used for business purposes among those with no knowledge of programming, software development, or machine learning. In a simple process, organizations can create real-time predictive models made accurate by automated machine learning.
The workflow from this machine learning framework can be manipulated to serve the users’ needs. Business professionals can easily use it to receive accurate predictions based on the data they feed it, while experienced data scientists can play with the parameters to perfect their own predictive models.
5. Google Cloud AutoML
Google AutoML empowers developers with little machine learning knowledge to create highly accurate, custom models. It supports a large array of algorithms and ML techniques. Utilizing neural network architecture, Google AutoML simplifies the entire process with an easy-to-use interface.
However, Google AutoML comes at a high price tag, and the company does not offer a free or demo version unless you plan to use it for purely research purposes, in which case a restricted version is available.
Automated machine learning is rising in popularity, and the demand for specialists in this field is growing exponentially. There are so many important tasks that can only be completed by data scientists and analysts, such as managing model performance and analyzing the data resulting from predictive models.
Automating those routine tasks can free up a lot of time in a data analyst’s day so that they can focus on more integral responsibilities. This is why AutoML frameworks are so valuable and will continue to be so in the future.
Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed — among other intriguing things — to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.