What Are AutoML Solutions?
AutoML (automated machine learning) is a set of algorithms and tools that automate the process of applying machine learning to a specific problem.
AutoML solutions are designed to make it easier for people who are not experts in machine learning to use the technology, by automating many of the steps involved in building and training a machine learning model. This can include tasks such as selecting the appropriate machine learning algorithm for a given problem, preprocessing the data, and optimizing the model's hyperparameters.
AutoML solutions can help build machine learning models that can be used for a wide range of tasks, such as predicting customer behavior, detecting fraud, and improving supply chain efficiency.
In this article:
Top AutoML Platforms
1. Run:ai
Run:ai is a proprietary platform for automating machine learning infrastructure. In terms of AutoML the platform offers controls for automating resource management, as well as workload orchestration for your entire machine learning infrastructure.
You can use Run:ai to pool GPU compute resources, set up GPU quotas, and continuously change resource allocation. These features enable you to actually optimize your compute resources, and ensure even highly intensive deep learning models consume resources at scale.
2. Google Cloud AutoML
The Google Cloud AutoML suite includes a number of different tools and services, such as AutoML Vision, AutoML Natural Language, and AutoML Translation, which are designed to make it easier for businesses to apply machine learning to specific tasks, such as image and speech recognition, natural language processing, and language translation.
Google developed these tools using reinforcement learning, a type of machine learning that involves the use of algorithms to enable a system to learn from its environment by interacting with it and receiving feedback.
3. Auto-SKLearn
Auto-SKLearn is an open-source software library that automates the process of building and selecting machine learning models using the Python programming language. It is based on the popular scikit-learn machine learning library, and it is designed to make it easier for users to apply machine learning to their data without requiring extensive knowledge of the underlying algorithms and techniques.
Auto-SKLearn includes a range of different features and capabilities, such as automatic model selection and hyperparameter optimization, which can help users to quickly and easily build high-quality machine learning models. Auto-SKLearn is available under the BSD3 license, and it can be used for a wide range of applications, such as predictive modeling, classification, and clustering.
4. MLBox
MLBox is an open-source software library for automating the process of machine learning. It is written in the Python programming language, and it is designed to make it easier for users to build and train machine learning models, without requiring extensive expertise in the field.
MLBox includes a range of different features and capabilities, such as automatic feature engineering, model selection, and hyperparameter optimization, which can help users to quickly and easily build high-quality machine learning models for a wide range of applications. MLBox is available under the BSD3 license, and it can be used for tasks such as regression, classification, and clustering.
5. Auto-Keras
Auto-Keras is based on the popular Keras deep learning library. It is designed to be easy to use, even for users who are not experts in machine learning. It can be used for tasks such as regression, classification, and clustering. It offers high-level APIs like TextClassifier and ImageClassifier that help solve ML problems with just a few lines. It also provides building blocks for performing architecture search.
6. H2O AutoML
H2O AutoML automates many of the steps involved in building and training machine learning models, such as preprocessing the data, selecting the appropriate algorithms, and optimizing the model's hyperparameters. H2O AutoML is part of the broader H2O.ai platform, which offers a range of tools and services for applying machine learning and artificial intelligence to a wide range of tasks.
7. Amazon Lex
Amazon Lex is a service offered by Amazon Web Services (AWS) that allows developers to build conversational interfaces for applications using voice and text. It is based on the same technology that powers Amazon's Alexa virtual assistant, and it allows developers to easily create chatbots and other interfaces that can be integrated with applications such as messaging platforms, mobile apps, and websites.
Amazon Lex includes a range of features and capabilities, such as automatic speech recognition and natural language understanding, which enable developers to quickly and easily create sophisticated interfaces that can be used for a wide range of applications, such as customer service, information gathering, and eCommerce.
8. Azure AutoML
Azure AutoML is a suite of tools and services for automated machine learning, offered by Microsoft as part of the Azure cloud computing platform. It is designed to make it easier for businesses and organizations to build and deploy custom machine learning models. Azure AutoML is useful for supervised learning and time series forecasting.
Related content: Read our guide to AutoML tools (coming soon)
Choosing an AutoML Solution: 5 Key Considerations
When choosing an AutoML software, there are several factors to consider:
- Self-built vs. commercial: If you have the expertise and resources to build your own AutoML software, you may be able to create a solution that is tailored to your specific needs and requirements. However, if you don't have the expertise or resources to build your own AutoML software, you may want to consider using a commercial solution. Commercial AutoML solutions are typically developed by companies that specialize in machine learning and have a team of experts who can help you get started and provide support.
- Augmented vs. cloud: Augmented AutoML solutions are designed to work with existing machine learning infrastructure, such as your own data center or on-premises hardware. They typically provide a range of tools and features that can be used to automate the machine learning workflow, but you are responsible for managing and maintaining the infrastructure. Cloud-based AutoML solutions, on the other hand, are hosted in the cloud and are typically managed by the provider. This means that you don't have to worry about managing and maintaining the infrastructure, but you may have less control over the underlying technology and how it is used.
- Features and tools: Some AutoML solutions may provide a wider range of algorithms and hyperparameters to choose from, while others may have more advanced tools for data preprocessing or model evaluation.
- Support and documentation: Some solutions may provide extensive documentation and support resources, while others may have more limited support options. Consider your needs and the level of support you will require when choosing an AutoML solution.
- Cost: Some solutions may be free or open source, while others may be commercial and require a subscription or licensing fee. Consider your budget and the value the solution provides when choosing an AutoML solution.
AutoML Solutions with Run:ai
In today’s highly competitive economy, enterprises are looking to Artificial Intelligence, Machine and Deep Learning in particular, to transform big data into actionable insights that can help them better address their target audiences, improve their decision-making processes, and streamline their supply chains and production processes, to mention just a few of the many use cases out there. In order to stay ahead of the curve and capture the full value of ML, companies must strategically embrace MLOps.
Run:ai’s AI/ML virtualization platform is an important enabler for Machine Learning Operations teams. Focusing on deep learning neural network models that are particularly compute-intensive, Run:ai creates a pool of shared GPU and other compute resources that are provisioned dynamically to meet the needs of jobs in process. By abstracting workloads from the underlying infrastructure, organizations can embrace MLOps and allow data scientists to focus on models, while letting IT teams gain control and real-time visibility of compute resources across multiple sites, both on-premises and in the cloud.
See for yourself how Run:ai can operationalize your data science projects, accelerating their journey from research to production.
