The artificial intelligence industry is unique in that it has evolved to the point of making dramatic impacts across other markets. AI is used to help businesses scale, improve customer experiences, decrease waste and streamline cumbersome tasks. Ventana Research sees the need for AI innovation for the future of technology and the many other industries it influences, but making such tools available to everyone at fairly low costs can be difficult. It is no surprise that nearly two-thirds (62%) of organizations report using machine learning today, and nearly three-quarters (72%) of organizations participating in our research say they plan to increase ML use.
H2O.ai is a vendor on the forefront of democratizing AI, making AI accessible for everyone through its open-source ML and data science platform to simplify the process of building smarter applications. H2O provides support for marketing, insurance, healthcare, retail, telecommunications, manufacturing and financial services. H2O’s AutoML makes the process of evaluating and training ML models easier, reducing the need for expertise in machine learning and boosting data scalability. H2O has a variety of products to simplify procedures, increasing efficiency via AI models and applications through H2O AI Hybrid Cloud, which provides developers or data scientists an automated engineering process and supplies data connectors to ingest data from various stores such as databases, Apache Hadoop and object storage services. H2O’s approach to training the best models in the shortest amount of time with its open-source platform highlights the increase of reproducibility, thereby establishing a foundation for research or applications. These products attempt to help data scientists accelerate the process of building models by utilizing automatic algorithm selection, model validation, feature engineering and hyper-parameter tuning. H2O’s dedication to democratizing AI is a significant contribution to the transformation of the market, which in turn expands the accessibility of such technologies as a force multiplier in every industry.
Our analysis finds that AI will be at the forefront of the majority of technological developments over the course of the next decade. By advancing the distribution of such tools, H2O.ai is helping to surpass current demand and set the tone for future technological business ventures. H2O’s approach of implementing AI tools through various products into a variety of enterprises expands the range of its influence through an AI cloud dataset trainer, advanced natural language processing models and publication of AI apps. H2O’s AI AppStore allows organizations to choose from a selection of pre-built software or to rapidly build and share their AI applications across the enterprise. Organizations can leverage the data science technologies to develop easily customizable apps across all aspects of business. H2O’s AI Hybrid Cloud platform accelerates the creation of models with advanced features, allowing organizations to gather valuable data within tabular and structured data forms to help identify customers, detect fraud, predict failures and much more.
Organizations seeking a simple, more accessible and improved way to train large datasets or deploy state-of-the-art AI models should examine H2O.ai’s wide array of products in the search for solutions. H2O.ai needs to continue developing more models to include within its pre-built application platform to ensure easy access to a range of solutions for customers. By 2023, more than three-quarters of analytics processes will be enhanced by AI/ML to streamline operations and increase the value derived from data. By enhancing interaction constraints, improving documentation programs and building upon current features within products, H2O’s solution sets would be applicable to any organization in need.
Organizations across industries can use H2O.ai’s products to effectively implement AI models and advanced data analytic tools, empowering operating plans. Image and video data, security improvements, automatic feature engineering, time-series issues, development frameworks, sophisticated AI application development, machine learning operations technologies and monitoring and prediction are just a few ways in which newly accessible AI tools can be used to boost efficiency and productivity.