Ease of Deployment, Built-in ML Health and Production Model Governance Key Factors in the Selection Process
SUNNYVALE, Calif., October 24, 2018Â â€“ ParallelM, a rapidly growing company pioneering the machine learning operationalization (MLOps) space, today announced that Wisely, a provider of AI-powered fundraising solutions for not-for-profits, has selected ParallelMâ€™s MCenter to operationalize its machine learning. With MCenter, Wisely can quickly deploy and optimize new ML applications without needing additional resources to scale its fundraising productivity tool, which uses machine learning algorithms to produce real-time insights that are essential to successful not-for-profit fundraising.
â€śThe integration of ParallelMâ€™s MLOps solution into our platform allows us to provide better machine learning-based predictions, ensure pipeline health and increase the productivity of our customersâ€™ fundraising efforts,â€ť said Artiom Komarov, CEO and Co-Founder of Wisely. â€śData garnered from machine learning and artificial intelligence can truly have a positive impact on not-for-profit fundraising, but the challenge has been scaling up the number of models inside of our application. ParallelMâ€™s MCenter helps us not only deploy more machine learning into production but also manage and optimize its performance in real-world use cases.â€ť
MCenter delivers a unique approach to MLOps, addressing ML production issues head-on by automating ML-optimized continuous deployment and integration, ensuring the quality and performance of live ML applications, and empowering data science and operations teams with innovative visualizations to manage ML applications over time. Using MCenter, business teams can mitigate risk, ensure compliance, assess and optimize the ROI of their AI initiatives. By providing a single, unified software solution for the full ML production lifecycle, MCenter enables companies to move confidently into the critical phase of realizing and scaling ML business value.
â€śWeâ€™re excited to work with Wisely to help them provide their not-for-profit customers with the most accurate data and insights from its machine learning and artificial intelligence innovations,â€ť said Sivan Metzger, CEO of ParallelM. â€śIn other industries, weâ€™ve seen the major role machine learning plays in improving outcomes and it is no different in the not-for-profit space.â€ť
ParallelM is the first and only company completely focused on delivering machine learning operationalization (MLOps) at scale. ParallelMâ€™s breakthrough MCenterâ„˘ solution is built specifically to power the deployment, optimization, and governance of machine learning pipelines in production so that companies can scale machine learning across their business applications. ParallelMâ€™s approach is that of a single, unified MLOps solution that embeds best practice processes in technology, enabling all ML stakeholders to unlock the business value of AI. Please visitÂ www.parallelm.comÂ or email us atÂ [email protected].
ParallelM and MCenter are trademarks of Parallel Machines, Inc. All other trademarks are the property of their respective registered owners. Trademark use is for identification only and does not imply sponsorship, affiliation, or endorsement.
Wisely is changing the way not-for-profits fundraise. Using AI/machine learning, the Wisely solution empowers fundraisers to own their pipelines by telling them which donors will give, when donors will give, and the right ask amount. Complementing an organizationâ€™s existing CRM, Wisely uses client data to power algorithms that provide up to date insights in real time. Unique in the not-for-profit space, Wisely uses an agency model to offer dedicated client support throughout the client relationship. Wisely is backed by industry experts in the fields of fundraising, technology, AI and product development. For more information, visitÂ www.fundraisewisely.com
MLOps (a compound of â€śmachine learningâ€ť and â€śoperationalizationâ€ť) is the practice of operationalizing and managing the lifecycle of ML in production. MLOps establishes a culture and environment where ML technologies can generate business benefits by optimizing the ML lifecycle to automate and scale ML initiatives and optimized business return of ML in production. MLOps enables collaboration across diverse users (such as Data Scientists, Data Engineers, Business Analysts and ITOps) on ML operations and enables a data-driven continuous optimization of ML operationsâ€™ impact or ROI (Return on Investment) to business applications. For more information, visitÂ MLOps.org.
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