AutoML is a platform that simplifies each step in the machine learning process, from working with the raw dataset to deploying machine learning models. In traditional practices, the entire process of developing and deploying machine learning models is very manual and each step in the process must be handled separately.

As credit unions are starting to advance their usage of methods to get to insights and take action on them, they are facing several challenges when it comes to the successful implementation and adoption of ML solutions.

Here are some of the capabilities of AutoML solutions to explore when you are looking to use them.

• Functionality – What steps of the machine learning model development can be partially or fully automated? How much control do you have over automated steps? Does the solution offer APIs to programmatically interact with the solution?
• ML Methods – What ML methods such as classification, regression, etc. are included in the solution?
• Explainability and Transparency – What capabilities are available for users to better understand and trust the results of the models?

Here are some of the top solutions to consider.

2. Amazon Sagemaker

For creating automatic machine learning models that too with full visibility, one can go with Amazon Sagemaker Autopilot. It can be used when some data is missing. It automatically infers the type of predictions that best suits the data. It was launched in 2019 to implement different steps of ML workflow.

Highlights:

• With automatic data pre-processing and automatic feature engineering features, Sagemaker offers complete visibility and control for non ML and ML experts. 
• Versatility: It is best for handling classification and linear regression type of ML problems. For tackling forecasting problems, one can use Sagemaker’s hyper-parameter tuning service.
• Flexibility: It provides statistical insights and flexibility to explain custom alerting schemes for model monitoring. 
• It is cloud based solution and fully scalable. 
• Transparency: One can check the original code, used to develop each candidate mode to provide full transparency.

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3. Data Robot

Launched in 2015, Data Robot’s AutoML allows automation of the entire data science lifecycle from raw data to value. It also empowers in moving beyond automated machine learning and building innovative models from the diverse type of data. Furthermore, speedy deployment and ease of use are always at the core of Data Robot’s AutoML. Those looking for a fully explainable AI through human-friendly visual insights can go with Data Robot.

Highlights:

• Flexibility: One can reap the benefits with end-to-end acceleration. Besides this, one can easily control the various parameters of modeling processes. 
• Contains a library of open-source and proprietary models to simplify the use. 
• Ease of Use: With human-friendly explanations, it offers GUI for users to interact with solutions. Python client API is also provided with Data Robot, and one can install it on-premises, as well as most major cloud platforms.
• It is versatile in terms of tackling classification, regression, and forecasting types of ML problems.
• Transparency: Several types of explanations including feature impact, feature effect, feature fit, and variable effects are provided for the ease of users. 

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4. Driverless AI

It is preferred for faster and efficient automation of projects. It came into being in 2017 as an enterprise solution by H2O. It deploys a library of algorithms and feature transformations to easily engineer new and high-value features for the dataset. It is capable to automate some of the difficult data science and machine learning workflows including Feature Engineering, Model Validation, Model Tuning, Model Selection, and Model Deployment among others.

Highlights: 

• Functionality: Built with state-of-the-art techniques, it is capable to fully or partially automate the ML workflow steps. 
• Driverless AI: It has driverless AI that helps in detecting basic data types and data visualization.
• It is Versatile enough to handle classification, regression, and forecasting types of problems involving structured and unstructured data.
• It is Flexible, as it can be customized through expert settings as well as the "Bring Your Own Recipe" feature. This way, it empowers users to define custom data sources. 
• Transparency: An auto-generated report is provided containing all the details and steps taken by driverless AI to develop the final scoring.

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5. Google AutoML

It was introduced in 2018 to offer AutoML capabilities to non-ML experts. Google AutoML is one of the best creations of Google to design neural nets. One can opt for it to build a custom machine learning model in minutes. It is a cost-effective solution and gives reliable performance for all sorts of complex data processing.

Highlights

1. 1. It has an easy-to-use graphical interface, provides the functionality of a data labeling service to ease the data preparation steps of the ML workflow. 
   2. Its Versatilityfeature can be used for handling classification and regression problems involving unstructured text, image, and video data in addition to structured data. It is capable enough to manage models with confidence.
   3. It is Flexible to prepare and store datasets. Also, offers customization options for model development and optimization process.
   4. Transparency: One can look for two pre-prediction explanations that are feature attribution types for ensuring trust. 

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6. H2O Auto ML

Introduced in 2017, H2O AutoML is a brilliant tool to automate the machine learning workflow. It offers several model explainability methods that apply to AutoML objects and individual models. It is good for advanced users as it reduces the number of code lines thus helping the user to focus on other aspects. 

Highlights

• Ease of Use: With a unified interface, it empowers in automating model selection, learning, and finalizing the steps of the ML workflow. It also performs model learning and hyper-parameter tuning of base models.
• It generates models automatically and handles classification & regression types of ML problems. 
• It is an open-source platform with the flexibility to control the AutoML optimization process through a set of parameters. 
• Installation: It can be easily installed on all major cloud platforms.
• Transparency: One can check for documentation, and references to developed base models to enhance their explainability.

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7. Microsoft Azure AutoML

Introduced in 2018, it empowers the user to quickly customize the models and apply control settings to iterations, validations, and other experiment criteria. Microsoft launched it with a target to serve non ML experts for rapid ML model development and deployment. Also, preview and statistics features are there to help in validating data before developing models.

Highlights:

• It has in-built capabilities for common machine learning tasks like classification and regression.
• Feature Engineering: For automatically handling unstructured text data, feature engineering is really helpful. Besides, it uses no-code UI and SDK for model building.
• With easy data exploration and responsible ML solution, it offers customization facility through advanced settings, model validation approach, model trials, and more.
• Transparency: Both local and global feature types of explanations are available for ensuring transparency. besides, documentation is provided including how-to guides and more.

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So, these are some of the major AutoML technologies that are well-positioned to benefit users as these solutions can automate lower value tasks of data prep, feature engineering, model selection, learning, finalization, validation, packaging, deployment, and management. The demand for AutoML solutions has increased over the past few years. Businesses are aiming to reap the benefits from it. Successful implementation of machine learning requires addressing the three “ Ts” of ML; time, talent, and trust. With AutoML solutions, challenges with these three “Ts” can also be alleviated as it is expected to play an important role in the years to come.