7 Automatic Machine Learning Frameworks
In this article, take a look at seven automatic machine learning frameworks.
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The usage of ML is increasingly rising these years. Businesses are impresses with a range of opportunities ML enables for them. However, they’re still struggling to deploy ML models because of the long duration and complexity of the process.
When a business has to come up with the prediction of a particular data set, the traditional approach includes the performance of the following actions:
- Process data
- Define technical characteristics
- Choose a model
- Optimize hyperparameters
- Training of these parameters
There is no algorithm suitable for all tasks, and data analysts have to select and configure the algorithm for each specific task.
Moreover, to prepare data, one needs to:
- Determine the type of columns, the semantic content
- Detect cluster allocation and its ranking
In general, it all appears time and money consuming process that for businesses is never an advantage.
Here, auto ML frameworks are coming into power.
Auto Machine Learning Frameworks
These frameworks are to automate all or almost all steps and provide businesses with accurate predictions as a result.
The biggest benefit of an Auto ML is the possibility of releasing businesses and data analysts from long routine tasks mentioned above and giving them more time for the creative side of a project instead.
The data from the Gartner report says that by 2020, 40% of data specialists will be replaced in the machine learning applications’ prediction by AutoML. This creates a need for us to go deeper to the automatic machine learning frameworks to choose the best model and configure needed parameters.
ML Box is a data Python-based library that is providing the following features:
- Read, preprocess, clean and format data
- The possibility to choose specific features and detect a leak
- Optimize hyperparameters
- Classify and regress State-of-the-art models for predictions
- Making predictions and model interpreting
From the cons, it’s more suitable for the Linux operating system while Windows and Mac users can experience some difficulties while installing.
Auto Sklearn is an auto machine learning framework based on Bayesian optimization, meta-learning, and ensemble construction to find similar data pieces.
The package includes 15 classification algorithms and 14 for feature preprocessing to define the right algorithm and optimize its parameters with an accuracy of more than 0.98. Auto Sklean works well for small and medium datasets, however, it doesn’t provide enough scalability for large.
In August of 2018, TPOT was put in the list of the most popular auto-machine learning frameworks on GitHub. This framework uses genetic programming to search for a model for specific task implementation. It can analyze thousands of pipelines and provide one with the best option of Python code.
In comparison to Auto Sklearn, TPOT offers its own regression and classification algorithms. But since it is a built on genetic programming, the model can give you different results for the same task every time you run it.
H2O AutoML framework is the best choice for those who are searching for deep learning mechanisms. It can perform many tasks that require many lines of code at the same time.
H2O uses statistical and ML algorithms with gradient boosted machines and complex learning systems.
It an open-source deep learning framework built on network morphism to boost Bayesian optimization. The framework can automatically search for architecture and hyperparameters for complex models. It conducts searches through Neural Architecture Search (NAS) algorithms while eliminating a need for deep learning engineers.
Google Cloud Auto ML
Google Auto ML is a Google-based framework with neural network architecture. The graphical user interface (GUI) is simple to use for processing models that make Google Cloud Auto ML fully applicable for developers with limited ML knowledge to process models needed for the business needs.
However, Google Cloud Auto ML is not an open-source library like other frameworks so one needs to pay for the usage. The cost depends on the time spent on training the models and the number of images to send to predict. Research is free of charge.
It is a library from Salesforce based on the Apache Spark framework that works with structured data written in Scala.
It can help to achieve accurate predictions for deep learning models while reducing the process 100x in time. The framework supports the processing of data sets, which consists of millions of rows and is able to work with clustered virtual machines on Scala.
It leaves us with no doubts that automatic ML is an essential tool for businesses that are striving to boost their performance and predict models x times faster.
Now, having learned about the top 7 automatic machine learning frameworks, one can choose the one according to the business needs and scale of operations and automate the repetitive tasks.
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