What Is AutoML? and Best AutoML Tools

What Is AutoML?

AutoML is a type of machine learning technology that helps improve the performance of machine learning tools. It is a subset of AI designed for automating tasks that are traditionally done by experts.

One such framework for AutoML is called TensorFlow. TensorFlow was developed by Google and it is open-source and can be used to develop more complex models than some other frameworks. There are many use cases of machine learning tools such as image recognition, machine translation, and natural language processing.

Another AutoML framework called ONNX can also help with this task and has endorse by Facebook’s AI Research group. It helps to translate between different frameworks which makes it easier to port models from one system to another.

Why Is Auto Machine Learning Needed?

Machine Learning is a revolutionary technology that has the potential to change the world. This type of AI is using to build statistical models that learn from data, apply algorithms to predict future states, and make decisions based on those predictions. These are algorithms that can learn from experience without being programmed to do so explicitly.

Advantages of Auto Machine Learning:

• Machine Learning is one of the most exciting fields in the world of Artificial Intelligence and it is what we use to teach computers how to learn and think like humans. It has led to incredible technological advancements in areas such as language understanding and computer vision, which have ramifications across many industries.

Best AutoML Tools list:

1. Autokeras
2. MLBox
3. POT
4. H2O AutoML
5. TransmogrifAI

Autokeras:

• Auto-Keras is an open-source resource that enables automated machine learning. It does this by walking you through the entire training process. It also allows you to search for architecture and hyperparameters automatically, saving significant time in the process.
• Auto-Keras follows the Scikit-learn API design and is super easy to use. You can also save time with feedback on settings during deep learning.
• Auto-Keras only use NAS algorithms and this saves lots of time and effort. It means you can focus on what is important for your business like finding the right customers.

MLBox:

• MLBox is a machine-learning library written in Python. It provides both powerful machine learning algorithms and easy-to-use APIs for quick starts with deep learning. MLBox utilizes by several large-scale applications including the development of self-driving cars, automated drug discovery, and even running ten million models per day on Spotify’s data pipeline for generating personalized music recommendations.

Includes the following features:

• It’s easy to read and distribute. Formatting is also not a problem.
• Their recent improvements have made them more robust, and leaky-free, and offer hyper-parameter tuning to help minimize overfitting.
• These cutting-edge predictive models use to classify data. If you have a large number of data points, for instance, if your business sells products across the world, our DLT algorithms are able to use Deep Learning neural networks & stacking algorithms to accurately predict trends in demand. With the help of MLBox, we’ve been able to predict and interpret complex equations successfully.

MLBox Architecture:

MLBox contains 3 packages:

• Pre-processing: reading and pre-processing data to produce content in a time-efficient manner
• Optimization: testing or optimizing a wide range of learners.
• The prediction will be one of the core parts of machine learning. The goal is to get training data to correctly tell us the future, so it should only be natural that prediction will play a major role in all aspects of artificial intelligence and machine learning.

TPOT:

• TPOT is a tree-based pipeline optimization tool that explores the possibilities of machine-learning pipelines through genetic algorithms. TPOT is built on top of scikit-learn and uses its own regression solver to improve the performance of machine learning pipelines. TPOT makes it easier for data scientists to tune hyperparameters and explore their effects on model performance.

H2O AutoML:

• H2O is an open-source and distributed in-memory machine learning platform. H2O holds strong support for both R and Python, connecting them with the most widely used statistical and machine-learning techniques.
• H2O includes a Machine Learning Automation module that can create pipelines for you. It uses an exhaustive search for optimization methods for both features and model hyper-parameters to try to give you the best model possible.
• H2O makes dealing with complicated data science tasks like these easy. You can use it to automatically engineer features or evaluate models, for example, and all the tools are extremely accessible.

TransmogrifAI

An ML platform from Salesforce called Einstein is powered by TransmogrifAI. This library uses Apache Spark to run Scala-powered ML software on feature analysis, selection, and creation.

TransmogrifAI can be extremely helpful in a variety of situations:

• Train quality machine learning models QUICKLY and with MINIMAL manual adjustment.
• Build modular, reusable, and strongly-typed machine learning workflows.

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