Leading Global Diversified Industrial Solves Data Science Business Challenges with H2O Driverless AI
MOUNTAIN VIEW, Calif., June 26, 2018 /PRNewswire/ — H2O.ai, the open source leader in AI, announced today that Stanley Black & Decker, the global diversified industrial with business units focused on tools and storage, security, healthcare, and industrial products, is using H2O Driverless AI to develop AI-enabled manufacturing processes which have the potential to drastically reduce the amount of time required for product development. Since deploying H2O Driverless AI, Stanley Black & Decker has begun evaluating additional AI projects across the company where Driverless AI can help deliver greater insights and cost savings to the business.
“H2O Driverless AI helps us abstract a way of carrying out the testing process without needing the actual nuts and bolts”
Dr. Robert Coop
Artificial Intelligence and Machine Learning Manager at Stanley Black & Decker
“The platform’s feature engineering and scoring pipeline generation are better than anything we’ve seen out there right now. These features alone have provided us with a true competitive edge in agile manufacturing. The H2O.ai team has developed an easy-to-use product that is a massive time saver.”
Stanley Black & Decker’s investment in Artificial Intelligence is part of its CEO Jim Loree’scommitment to digital excellence, the animating force of which is the, Atlanta-based, Digital Accelerator (DA).
“Our Digital Accelerator focuses on Artificial Intelligence, Robotics, and Emerging Technology”
Robert W. Ross
who serves as the DA’s Vice President for Digital Strategy, AI, and Creative UX
“At Stanley Black & Decker, we are committed to converting the exponentially increasing disruption brought by digital into a sustainable competitive advantage, and H2O Driverless AI is one component of that broader strategy.”
Prior to deploying Driverless AI, Stanley Black & Decker explored AI and machine learning (ML) solutions that could supplement its existing data science team with the support and tools needed to create high quality, accurate models that not only scale with increasing data ingestion, but also generate important insights that can positively impact the organization’s bottom line. With automatic feature engineering and pipeline generation, H2O Driverless AI was the only solution on the market to meet this criteria.
“Stanley Black & Decker is empowering makers and creators of the world with their amazing products. We are excited to win their trust and to partner in their transformation with AI. With H2O’s Driverless AI, it is able to automate intelligent pipelines, build trust in AI and address the looming data science skills gap, revolutionizing supply chains in manufacturing with AI and driving both operational and commercial excellence”
Sri Ambati
CEO and founder at H2O.ai
“Data and AI is a culture of transformation and Stanley Black & Decker is building an empowering culture of innovation with purpose. H2O.ai is thrilled to be part of their beautiful journey towards the fourth industrial revolution.”
H2O.ai Automates AI with Driverless AI
H2O Driverless AI empowers data scientists or data analysts to work on projects faster and more efficiently by using automation and state-of-the-art computing power to accomplish tasks that can take humans months in just minutes or hours. By delivering automatic feature engineering, model validation, model tuning, model selection and deployment, machine learning interpretability, time-series and automatic pipeline generation for model scoring, H2O Driverless AI provides companies with a data science platform that addresses the needs of a variety of use cases for every enterprise in every industry. In addition, H2O.ai is a leader in the 2018 Gartner Machine Learning and Data Science Platforms, recognized for the most completeness of vision and highest customer satisfaction.
In June, at H2O World NYC, H2O.ai unveiled the latest version of Driverless AI. This release includes time series support aimed at improving predictions within transactional datasets and auto documentation, as well as updated Auto Visualizations, Machine Learning Interpretability (MLI) and Automatic Pipelines.
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