SFL Scientific and Iguazio Partner to Speed Up Custom AI Development for Fortune 1000 Companies
- Top tier consultancy partners with a leading data science platform company to simplify and expedite development and deployment of AI for enterprises across industries: Finance, insurance, healthcare, retail, manufacturing, gaming, AdTech, etc.
- Enterprises can now easily incorporate AI and MLOps automation to create business impact through endless applications such as: Predictive maintenance, real time recommendations, KYC (know your client) and fraud prevention
- Partnership will speed up deployment of AI services at lower cost
TEL AVIV, Israel & NEW YORK–(BUSINESS WIRE)–#AI—Iguazio, the data science platform for real-time machine learning applications, today announced a strategic partnership with SFL Scientific, a leading data science consulting firm. The partnership will enable both companies to extend their offerings to enterprises of all industries looking to apply AI to real life applications, regardless of the size or skill set of their internal teams.
In today’s economic environment, enterprises across industries are looking to develop viable AI solutions that help them cut costs, work more efficiently, and develop new services and products for customers.
However, as enterprises navigate the journey to develop viable AI solutions and derive the business benefits from analyzing big data, they must transform not only their workforce, standard processes, and operating models, but also modernize critical applications in their infrastructure and architecture.
This is often a tremendous task, and one that requires expert support to navigate the transformation effectively and efficiently. With the new partnership, SFL Scientific will offer data strategy and support for algorithm development across data centers and cloud infrastructures, while Iguazio provides its data science platform which saves time and cost on getting AI to production.
“We are thrilled to partner with such a well-respected player in the AI professional services arena,” said Asaf Somekh, CEO and co-founder of Iguazio. “Until now, AI solutions at scale—harnessing data from millions of end points or running multiple models simultaneously—were a luxury only afforded by tech giants. This partnership will empower enterprises to equally take advantage of the promise of AI.”
“As data science consultants working on state-of-the-art AI solutions, SFL Scientific utilizes best in class tools to serve our clients,” said Dr. Michael Segala, CEO and co-founder of SFL Scientific. “The partnership with Iguazio is valuable for accelerating client deployment and production objectives in a highly efficient, cost-effective manner.”
Both companies have an extensive network of high-profile partners, including NVIDIA, NetApp, AWS, Microsoft, Intel and Dell EMC.
Recognized as NVIDIA Partner Network (NPN) Service Partner of the Year for 2018 and 2019, SFL Scientific accelerates the adoption of GPU-enabled systems, and leverages NVIDIA DGX systems to create high-performance AI solutions for enterprises.
Iguazio is one of the first partners in the NVIDIA DGX-Ready Software partner program, allowing enterprises to industrialize AI development workflow and realize the benefits of MLOps on NVIDIA DGX systems.
“Enterprises want to infuse their business with AI innovation but often struggle with access to expertise, platforms and infrastructure that can help them deploy more of their models in production,” said Tony Paikeday, senior director of DGX Systems at NVIDIA. “The partnership between SFL Scientific and Iguazio provides access to these key elements, as well as enterprise-grade MLOps software for accelerating insights and ROI.”
Companies working with both Iguazio and SFL Scientific can benefit from this strategic partnership immediately, by contacting either company.
About SFL Scientific
SFL Scientific is a US-based data science professional services firm focused on strategy, technology, and solving business & operational challenges with Artificial Intelligence (AI). Working with clients of all sizes, industries, and AI maturity levels, our capabilities range from developing corporate AI strategy to building custom AI applications at scale. With a globally connected network of technology and cloud partners, SFL Scientific’s core services include leading cross-functional efforts across business, IT, and operations. Hundreds of clients—including S&P100 enterprises, fastest-growing startups, and government agencies—trust SFL Scientific to create and accelerate AI initiatives. For more information on SFL Scientific, please visit: https://sflscientific.com/.
The Iguazio Data Science Platform enables enterprises to develop, deploy and manage AI applications at scale. With Iguazio, enterprises can run AI models in real time, deploy them anywhere (multi-cloud, on-prem or edge), and bring to life their most ambitious AI-driven strategies. Enterprises spanning a wide range of verticals, including financial services, manufacturing, smart mobility and telecoms use Iguazio to automate MLOps and create business impact through a multitude of real-time use cases such as fraud prevention, self-healing networks and location-based recommendations. Iguazio brings data science to life. Find out more on www.iguazio.com
Notes to editors:
- MLOps – MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle. Similar to the DevOps or DataOps approaches, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. While MLOps also started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle – from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.