KNIME on Amazon Web Services Now Available to Productionize AI/ML
KNIME, a unified software platform for creating and productionizing data science, announced the availability of KNIME on AWS, its commercial offering for productionizing artificial intelligence (AI)/machine learning (ML) solutions on Amazon Web Services (AWS). KNIME on AWS is designed to allow customers to assemble and deploy ML solutions across the enterprise at scale and securely on AWS and to gain tangible value quickly. The offering is now featured in AWS Marketplace, including free trials.
Many enterprises seek to create value by deploying ML and AI solutions but can lack the data scientists, data platform engineers, experience, money and time necessary to make a meaningful impact quickly. The result is that teams and individuals lacking this set of highly technical skills are left out of the innovation loop and are unable to realize the potential that their data offers. Further, there are many steps in the process of bringing an AI/ML solution into production that require a transfer of context and knowledge from data preparation to analysis and modeling to deployment.
KNIME on AWS is a visual data workflow editor that allows customers of all skill levels to extract and prepare their data from Amazon Simple Storage Service (Amazon S3), Amazon Redshift, or other sources; utilize AWS AI/ML services along with custom data science to build an impactful model; and deploy this solution “as a service” or to an analytics application. In each step, the solution is underpinned by the storage, compute, security and scale of AWS. This end-to-end solution from data to deployment can be realized with no coding required, and scheduling/automation can be employed in order to create a continuous stream of insights or decisions with minimal manual effort required.
Eli Feldman, CTO of Advanced Technology at EPAM, explained, “As an Advanced Consulting Partner in the AWS Partner Network (APN) that has developed KNIME connectors for seamless data integration and visualization, EPAM is uniquely positioned to help enterprise customers realize the full benefits of KNIME on AWS. As more companies leverage next-gen technologies like AI and ML for better decision-making, we’re proud to work with AWS and KNIME to help our customers gain data-driven insights to achieve greater agility in driving innovation.”
Added Paul Treichler, vice president of global partnerships at KNIME, “Enterprises across all sectors are collecting and storing vast amounts of data in order to make better decisions, but few are realizing its full potential. Our relationship with AWS fills the gap for customers looking for a no-code data analytics solution that can make it easier for the community to productionize data science on enterprise-scale using AWS services. Top-tier APN Partners like EPAM bring their domain expertise to drive impact with these solutions for the largest companies in the world.”
KNIME on AWS covers a broad array of industries and use cases and is being utilized today by customers in such industries as manufacturing, high tech, media, education and life sciences as well as use cases from predictive analytics to image and text analysis to time series analysis. The transparency, flexibility and speed of the platform support functions from rapid prototyping to governance, audit and scaling, including compute, execution or across a big data environment. KNIME on AWS is utilized by enterprises to encapsulate, automate and scale the delivery of data-driven insights to the business to allow teams to focus on execution and optimization.
KNIME on AWS is designed to represent a meaningful improvement in speed to value for customers with technical capability constraints who wish to utilize AWS AI/ML services. As of the KNIME Analytics Platform 4.1 release, Amazon Translate, Amazon Comprehend, and Amazon Personalize are available as native nodes. Other AWS services are immediately accessible using the Boto3 Python library integration with KNIME.