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Machine Learning for All: the Democratizing of a Technology

Our friends over at H2O.ai have produced a short new eBook “Machine learning for all: the democratizing of a technology” which covers machine learning features and automatic AI solutions, and how organizations can benefit from using them.

H2O.ai has dedicated itself to democratizing all aspects of AI, including machine learning. H2O Driverless AI is a machine learning solution that automates AI for nontechnical users. So-called “AutoML” solutions like H2O Driverless AI are rising in popularity for enterprises across a wide range of industries. With it, users can build robust, fast, and accurate machine learning solutions. It also includes visualization and interpretability features that explain the data modeling results in plain English, fostering further adoption and trust in AI.

Data is proliferating exponentially, and with it, the potential to draw businesse-enhancing, problem-solving insights using artificial intelligence (AI) and machine learning (ML). However, three big challenges—talent, time, and trust— often prevent organizations from adopting enterprise-level AI. Conventional, enterprise-level AI/ML efforts are complex and usually require data scientists. Projects can involve months of data preparation followed by a process of designing, developing, testing, tuning, and deploying a machine learning model. Many available solutions are difficult to trust. They consist either of complex, non-linear models and ensembles that are almost impossible to interpret by humans, or pre-built models that provide no insight into how the predictions are being made.

However, solutions are emerging that place the power of advanced machine learning within reach—even for companies with minimal data science experience. The democratization of AI/ML has begun.

Download the new eBook, courtesy of H2O.ai, “Machine Learning for All: the Democratizing of a Technology,” which details how AutoML can empower data scientists to work on projects faster and more efficiently by using automation to accomplish key machine learning tasks in just minutes or hours, not months.

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