What is AutoML, How it can help our Citizen Developers?

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What is AutoML?

Automated Machine Learning offers techniques and procedures to make Machine Learning accessible to those who are not specialists in it, to increase its effectiveness, and to quicken the pace of Machine Learning research.

Recently, machine learning (ML) has seen significant success, and an increasing variety of disciplines now use it. To achieve this result, the following tasks must be performed by human machine learning experts:

• Clean up and prepare the data.
• Choose and build appropriate features.
• Pick a suitable model family.
• Improve the model's hyperparameters.
• Create the neural network topology (if deep learning is used).
• Post-process models for machine learning.
• Critically evaluate the outcomes.

The complexity of these jobs is frequently beyond the capabilities of non-ML professionals; hence the quick development of machine learning applications has increased the demand for ready-to-use machine learning techniques. The ensuing field of study that focuses on progressive automation of machine learning is known as AutoML.

What all AutoML can do?

Various phases of the machine learning process can be targeted by automated machine learning. To automate, follow these steps:

1.??????Preparing and ingesting data (from raw data and miscellaneous formats)

• Identification of the column type, such as text, discrete numerical, continuous numerical, or Boolean
• Column intent detection, such as target/label, a field for stratification, a numerical feature, a text feature for categorical data, or a free text feature
• Task identification using techniques like binary classification, regression, clustering, or ranking

2.??????Feature engineering

• Feature selection
• Feature extraction
• Meta learning and transfer learning
• Skewed data and/or missing value detection and handling

3.??????Model selection is the process of selecting a machine learning method from among several competing software implementations.

4.??????Ensembling is a type of consensus where numerous models are used and frequently produce better results than a single model.

5.??????Feature customization and hyperparameter optimization of the learning algorithm

6.??????Choosing a pipeline with complexity, memory, and time limitations

7.??????Methods for choosing evaluation metrics and validation

8.??????Problem-solving

• Detection of leakage
• Misconfiguration

9.??????Analysis of the results attained

10.??Making visuals and user interfaces

How does the AutoML process work?

From processing a raw dataset to installing an effective machine learning model, AutoML is often a platform or open-source library that makes each step in the machine learning process simpler. In conventional machine learning, each stage of the process must be managed independently, and models are created by hand.

For a particular assignment, AutoML automatically finds and employs the best kind of machine learning algorithm. It accomplishes this using two ideas:

Neural network design is automated using neural architecture search. This aids AutoML models in finding novel architectures for challenging challenges.

Transfer learning is the process through which previously trained models use fresh data sets to apply their knowledge. AutoML uses transfer learning to adapt current structures to newly posed challenges.

The models can then be interacted with by users through a relatively straightforward coding language like Python, even if they have no experience with machine learning and deep learning.

Here are some of the machine learning steps that AutoML can automate in more detail, listed in the order that they happen in the process:

• Processing of raw data.
• Feature engineering and feature selection.
• Model selection.
• Hyperparameter and parameter optimization.
• Deployment taking into account technological and business restrictions.
• Evaluation metric selection.
• Monitoring and problem-solving.
• Analysis of findings.

Why is AutoML important?

Because it marks a turning point for machine learning and artificial intelligence, AutoML is significant (AI). The "black box" criticism of AI and machine learning refers to the fact that machine learning algorithms can be challenging to reverse engineer. Although they increase productivity and processing capacity to create results, it might be challenging to trace the exact path taken by the algorithm to get there. As a result, it might be challenging to predict a result if a model is a black box, which makes it challenging to select the best model for a specific situation.

By making machine learning more approachable, AutoML contributes to making it less of a mystery. Parts of the machine learning process that apply the algorithm to actual situations are automated by this method. Understanding the internal logic of the algorithm and how it relates to the real-world circumstances would be necessary for a human executing this activity. It picks actions that would take too much time or resources for people to efficiently carry out at scale. It also learns about learning.

AutoML has made it feasible to fine-tune the complete machine learning procedure, or machine learning pipeline, through meta learning.

Pros and cons of AutoML

The main benefits of AutoML are:

Efficiency: It shortens the training period for machine learning models and speeds up and simplifies the machine learning process.

Cost savings – A corporation can save money by allocating less of its cash to sustaining a faster, more effective machine learning process.

Accessibility — By using a less complicated procedure, businesses might spend less money on employing specialists or training new employees. Additionally, it opens up machine learning to a wider spectrum of businesses.

Performance: Compared to hand-coded models, AutoML techniques are frequently more effective.

The tendency to see AutoML as a substitute for human expertise is one of its key obstacles. Like most automation, AutoML is made to carry out repetitive operations accurately and effectively, freeing up workers to concentrate on more difficult or unusual jobs. Routine operations that can be sped up by automation include monitoring, analysis, and problem detection, all of which AutoML automates. Although it is no longer necessary for a human to actively participate in the machine learning process, they should still be included in the model's evaluation and supervision. Instead, than replacing data scientists and staff, AutoML should increase productivity.

Another difficulty is that some of the most well-liked tools in AutoML are still in the early stages of development.

AutoML tool features and Some popular AutoML platforms:

Google AutoML is a private, cloud-based platform for automatic machine learning.

An unique, cloud-based technology called Azure Automated Machine Learning.

The Texas A&M University's DATA lab created the open-source software library Auto Keras.

Scikit learn, an open source, commercially viable set of basic machine learning tools in Python, was evolved into and superseded by Auto-sklearn. It is accessible through GitHub.

Due to the fact that they often require fewer resources than the other two models, Auto-sklearn and Azure are regarded as being more affordable. They don't require the entire data set to function because they heavily rely on data and designs, they have already seen. To do this, they employ classification and regression algorithms.

In comparison, Google AutoML and AutoKeras are better at building new models, but they also consume more resources because they typically need the entire data set. They employ long short-term memory, convoluted neural networks, and recurrent neural networks (RNN) (LSTM).

Source:

automl.org

techtarget.com