H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models.
Table of Content
What is Automated Machine Learning?
Machine learning is the fundamental element of artificial intelligence (AI) and uses algorithms to detect and extract patterns in data to predict certain outcomes based on that analysis.
Automated Machine Learning ( AutoML ) systematically addresses multiple phases of the data science lifecycle, with automation designed to reduce complexity in tasks and allow data scientists to implement AI projects more efficiently with greater accuracy empowers for. AutoML improves access to machine learning capabilities for those who do not have expertise in data science by providing a user-friendly interface that can be used by anyone with beginner technical knowledge, business users and IT professionals enables you to easily implement machine learning into your daily workflow.
H2O Driverless AI
Designed specifically to use AI to create AI delivers industry-leading AutoML capabilities, including key functionals such as data visualization, feature engineering, model Web development and validation, model documentation, machine learning interpretation and more Areas include data science best practices.
Intelligent feature changes
H2O driverless AI to detect relevant features in a given dataset, find interactions within those features, handle missing values, derive new features from data, compare existing and newly generated features and show the respective significance Automates the entire facility engineering process. Each of these features. Features are converted into meaningful values that machine learning algorithms can easily consume.
Automated model development
Reducing the time it takes to develop accurate, production-ready models is critical to mass distributing AI. H2O Driverless AI automates time-consuming data science tasks including advanced feature engineering, model selection, hyperparameter tuning, model stacking, and creates an easy-to-deploy, low-latency scoring pipeline. With high-performance computing using both CPU and GPU, H2O Driverless AI compares thousands of combinations and iterations to find the best model in a matter of minutes or hours.
Comprehensive interpretability toolkit
H2O Driverless AI uses an AI wizard that examines your data, provides recommendations based on your business needs and instructs you on appropriate machine learning techniques to select based on your unique data and use case requirements is. AI Wizard’s built-in recommendations are based on data science best practices from various disciplines to ensure that the custom model being created leverages your data effectively and aligns with your business needs.
Expert Recommendation System
H2O Driverless AI uses an AI wizard that examines your data, provides recommendations based on your business needs and instructs you on appropriate machine learning techniques to select based on your unique data and use case requirements is. AI Wizard’s built-in recommendations are based on data science best practices from various disciplines to ensure that the custom model being created leverages your data effectively and aligns with your business needs.
Conclusion
Everyone can benefit from the power of artificial intelligence, but only those with certain skills have the ability to create and develop solutions with the machine learning tools available in today’s technology market. However, it is important for people in different disciplines to create and use machine learning applications as AI has become more pervasive across all organizational functions. Automated machine learning has removed barriers to AI adoption by wrapping the expertise needed to build models in a guided approach to data science that allows users to access the power of machine learning without having to write any code .
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