Automated machine learning, also known as AutoML, is the process of automating the end-to-end process of building machine learning models. This includes tasks such as data preprocessing, feature engineering, model selection, and hyperparameter tuning.
The goal of AutoML is to make it easier for non-experts to develop machine learning models, by providing a simple, user-friendly interface for training and deploying models. This can help to democratize machine learning and make it more accessible to a wider range of people, including those with little or no experience in data science.
For data scientists and MLOps teams, AutoML can reduce manual labor and simplify routine tasks, while allowing other parts of the organization to participate in the process of creating and deploying machine learning models.
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AutoML makes it easier for non-experts to develop machine learning models. This is important because machine learning has the potential to solve a wide range of problems, from image recognition to natural language processing. However, building machine learning models requires a significant amount of expertise in data science, including knowledge of algorithms, statistics, and programming. This can be a barrier for many people, including those who have the domain knowledge to identify valuable problems that could be solved with machine learning, but lack the technical skills to build the models themselves.
AutoML helps to overcome this barrier by automating the process of building machine learning models, making it easier for anyone to get started with machine learning. This can help to democratize machine learning and make it more widely accessible, which could have many benefits, including driving innovation and enabling businesses to solve complex problems more efficiently.
The AutoML process typically involves the following steps:
Supervised machine learning models create outputs by making predictions based on input data. During training, the model learns the relationship between the input data and the correct outputs, and uses that knowledge to make new predictions.
The quality of the input data is important for machine learning models because it directly affects the accuracy and performance of the model. If the data is of poor quality, the model will learn incorrect or misleading relationships between the input and output.
Hyperparameters are the settings or parameters that control the behavior of the model, such as the learning rate, the number of hidden layers in a neural network, or the regularization strength. These parameters are typically set before training the model, and they can have a significant impact on the model's performance. However, optimizing hyperparameters can be a time-consuming and difficult task that requires a significant amount of expertise and experience.
AutoML systems optimize hyperparameters by automatically searching for the best combination of hyperparameters for a given machine learning model. This is done by training the model on the data using different combinations of hyperparameters, and then evaluating the performance of each combination.
Google Cloud AutoML is a suite of machine learning tools and services provided by Google Cloud. It includes a range of tools and services that make it easier for developers and businesses to develop, train, and deploy machine learning models. These services include:
Learn more in our detailed guide to Google AutoML
Auto-Sklearn is an open-source Python library for automated machine learning. It is built on top of the popular scikit-learn library, and provides a simple, user-friendly interface for training and deploying machine learning models. Auto-Sklearn uses Bayesian search to do the following:
GitHub repo: https://automl.github.io/auto-sklearn/master/
Learn more in our detailed guide to AutoML Sklearn (coming soon)
AutoKeras is an open-source Python library for automated machine learning. It is built on top of the popular Keras deep learning library. AutoKeras automatically searches for the best neural network architecture for a given dataset and task. This can help to improve the performance of the model, and can make it easier for users to develop high-quality deep learning models without needing to have extensive knowledge of neural network architecture design.
GitHub repo: https://autokeras.com/
Learn more in our detailed guide to AutoML Keras (coming soon)
Amazon Lexis a service provided by Amazon Web Services (AWS) that allows developers to build natural language interfaces for applications and services. It is based on the same technology that powers Amazon's virtual assistant, Alexa, and allows developers to create chatbots and other conversational interfaces that can understand and respond to natural language input from users.
Some of the features of Amazon Lex include:
H2O is a suite of machine learning tools and services provided by H2O.ai. It includes a range of tools and services that make it easier for developers and businesses to develop, train, and deploy machine learning models.
Tools and features provided by H2O include:
GitHub repo: https://github.com/h2oai/h2o-3
Learn more in our detailed guides to:
In today’s highly competitive economy, enterprises are looking to Artificial Intelligence in general and 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, however, 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.
