What are the Advantages of Automated Machine Learning Tools?

AutoML (Automated Machine Learning) helps organizations deploy Machine Learning (ML) models faster, by making the ML pipeline process more efficient and less error-prone. If you’re getting started with AutoML, this article will take you through the first steps you need to find a tool and get started. If you’re at an advanced stage, it will help you validate you’re on the right track. After reading this article, you’ll have a good understanding of the benefits of AutoML, the available tools and how to choose the right tool for your industry.

What is Automated Machine Learning?

Automated Machine Learning (AutoML) is the automation of part of or all of the ML pipeline, in order to increase productivity and accuracy and eliminate organizational silos. ML pipeline stages that can be automated include model selection, the selection and parametrization of the ML algorithm for model training, data preprocessing, feature engineering, hyperparameter tuning and deployment. Tasks that can be automated include the selection of appropriate algorithms and feature sets, scaling numerical features, handling missing values, encoding categorical variables, testing and hypertuning parameters.

By automating the complex and time-consuming tasks and activities required for building and deploying ML models, machine learning automation creates standardization. This makes these expert-created processes accessible to a wider range of users and increases efficiency. As a result, AutoML accelerates the development and deployment of ML models and makes them useful for business use cases.

What are the Advantages of Automated Machine Learning Tools?

AutoML tools have become increasingly popular in recent years due to their ability to accelerate the ML development process, automate the ML workflow, reduce the barriers to entry for non-experts and let more organizations get business value from ML. Some of the advantages of AutoML tools include:

Faster Deployment and Time-to-market

AutoML platforms reduce the time it takes to develop and deploy ML models, enabling businesses to quickly deploy solutions to meet business requirements and customer demands. Instead of hours or even days or months, processes take minutes.

Increased Productivity and Efficiency

By automating the repetitive and/or complicated and time-consuming tasks involved in ML, AutoML tools enable greater efficiency. This is done by speeding up the process of bringing models to production, reducing the chance of errors and allowing data scientists and developers to focus on higher-level tasks.

Improved Accuracyֿ

AutoML tools improve the accuracy of ML models by automating the selection of models, algorithms, feature sets and hyperparameters. As a result of eliminating or reducing the manual aspects of the pipeline, the process is less error-prone, can scale more easily and it is optimized.

Enhanced Accessibility

AutoML tools broaden the range of users that can take part in the ML pipeline, by reducing the need for a cutting-edge technological skills set, like in-depth knowledge of the underlying algorithms and techniques. In addition, AutoML makes employee onboarding much easier; new hires don’t have to be trained on all models, but rather on the AutoML tool. This also democratizes AI, making it more accessible to all.

Cost Savings

By reducing the amount of time and resources required to build and deploy machine learning models, AutoML tools can help reduce costs and increase the return on investment for businesses.

Scalability

AutoML enables scalability in two ways. First, scaling of data, by automating tasks related to large datasets. Second, scaling of the entire process, through its automation and acceleration.

Automated Machine Learning Tools and Applications for Every Industry

There are several AutoML tools available for use, both open-source and commercial. They can be used in a wide range of industries. A few of these tools are:

• Auto-sklearn: A popular open-source AutoML tool that uses Bayesian optimization, meta-learning and ensemble construction to automate the machine learning pipeline.
• H2O.ai:  An open-source platform that supports ML algorithms like gradient boosted machines, generalized linear models, deep learning and more. There is also a commercial version.
• Google Cloud AutoML: A suite of AutoML tools offered by Google Cloud, including the tools Vertex AI, AutoML Tabular, AutoML Image and AutoML Text.
• TPOT: A Python AutoML tool that uses genetic programming to automatically search for the best ML pipeline.
• Microsoft Azure AutoML: A suite of AutoML tools offered by Microsoft Azure.
• Amazon SageMaker Autopilot: An AutoML service offered by Amazon SageMaker for building, training and tuning ML models.
• Keras Tuner: An open-source AutoML library for hyperparameter search. It supports Bayesian Optimization, Hyperband and Random Search algorithms .
• MLJAR: An open-source Python package for AutoML on tabular data.

Any industry that deals with large amounts of data is relevant for AutoML use cases and can benefit from AutoML tools, including:

• Finance - For analyzing financial data, identifying patterns and trends, identifying fraud and making predictions about future market conditions.
• Healthcare - For analyzing medical data, identifying disease risk factors, subscribing medicine and developing predictive models to improve patient outcomes.
• Retail - For analyzing customer data, predicting customer behavior, optimizing pricing and managing inventory strategies.
• Manufacturing - For analyzing sensor data from manufacturing equipment, predicting equipment failures and optimizing maintenance schedules.
• Transportation - For analyzing traffic data, optimizing logistics operations and predictive maintenance for vehicles.
• Energy - For analyzing energy usage data, predicting energy demand and optimizing energy production and distribution.
• Marketing - For analyzing customer data, developing personalized marketing strategies and optimizing advertising campaigns.
• Insurance - For analyzing insurance data, identifying fraud, automating underwriting and developing risk prediction models.

How to Choose Your Automated Machine Learning Tool

There are many AutoML tools to choose from, which can make choosing one challenging. To help you choose an AutoML tool, consider these factors:

1. Type of problem: The type of ML challenge you are trying to solve will dictate the type of AutoML tool you need. For example, if you are working with image data, you may need an AutoML tool that is optimized for computer vision tasks.
2. Size and complexity of the dataset: Large and complex datasets require more powerful AutoML tools that can handle large amounts of data and perform more complex modeling tasks.
3. Level of automation: Some AutoML tools provide fully automated machine learning pipelines, while others require more human input. Consider the level of automation you need and whether you have the internal skillset to provide the necessary input.
4. Integrations: Consider whether the AutoML tool integrates with the other tools and systems you are using. For example, if you are working with data stored in a cloud platform, you may need an AutoML tool that can integrate with that platform.
5. Cost: AutoML tools can range from free and open-source to expensive commercial products. Consider your budget and whether the cost is justified through the value the AutoML tool provides.
6. Ease of use: Consider whether the AutoML tool has a friendly UI, especially if you do not have extensive experience with machine learning.
7. Support and community: Take into account the tool’s support community and how responsive it is to user feedback and bug reports. If it’s open-source, consider if you have the resources to contribute as well.
8. Customizability: Think about the tool’s customization capabilities and whether you have the expertise to be able to take advantage of that customization.

AutoML has multiple advantages. Like any automation, it drives productivity and efficiency. In the data science case, it can help more organizations benefit from ML models by helping deploy them to production faster. If you’re a data professional, it’s recommended you have AutoML knowledge and experience under your belt. To learn more about the ML pipeline, here are plenty of resources to get you started.

AutoML to AutoMLOps

AutoML enables optimizing ML processes by automating stages in the ML pipeline. However, to automate and deploy the entire ML pipeline in a seamless and efficient manner, we need to take a production-first approach. This is where AutoMLOps comes in.

AutoMLOps is the automation of the engineering tasks across the entire pipeline so that code is ready for production and models can bring business value. AutoMLOps enables actions like injecting parameters and code into tasks, integrating with CI/CD pipelines, Git and reporting systems, distributing workloads, passing data to and from cloud resources and databases, security hardening, scaling and much more.

AutoMLOps can be implemented with open source tools, like MLRun and Nuclio. MLRun enables MLOps orchestration. Nuclio supports automation of workload deployment for tracking and running the entire process.

Get more AutoMLOps resources here.