The Detroit Lions today announced the franchise has selected Wallaroo.AI as its official artificial intelligence (AI) and machine learning (ML) partner. The Lions will work with Wallaroo to scale the use of AI across many different business objectives on and off the field. The first phase of the partnership will integrate Wallaroo technology into the newly established Trace3 Analytics War Room at the Lions home at Ford Field in downtown Detroit, where they will work directly with the team's analytics group during games to improve the fan experience across various elements such as ticketing, parking, concessions, retail, and fan sentiment. "Our organization continues to look for innovative ways to improve our business and be a leader in the use of analytics throughout the NFL," says Ashton Mullinix, senior vice president of strategy and analytics for the Detroit Lions. "With the help of Wallaroo, we are looking to scale our use of AI to leverage real-time data to impact decision making."

The Space Force's innovation arm, SpaceWERX, has tapped Wallaroo Labs to explore and demonstrate how machine learning models can be deployed to advance multiple efforts associated with on-orbit servicing, assembly, and manufacturing (OSAM) missions for the newest U.S. military branch. The company was selected for a Phase I Small Business Innovation Research (SBIR) project to help the Space Force fully unleash machine learning within its OSAM-aligned production environments, according to an announcement published Tuesday. OSAM enables the building or repair of systems and components at the operational edge, on orbit. While predictive algorithms to support such processes can be built virtually anywhere on Earth, operationalizing those machine learning models in space is necessary to maximize their value. "The whole point of this for them is to get to outcomes faster across a whole range of use cases," Wallaroo CEO and founder Vid Jain told FedScoop in an interview on Monday prior to the announcement.

Reach out to us at deployML@wallaroo.ai for a free evaluation. The emergence of Big Data in decision-making to achieve strategic business objectives has led to machine learning (ML) becoming a key enabler for driving growth, achieving operational excellence, and bringing innovative products to market. This shift has come about as the primary obstacles for ML are being overcome: data engineering at scale and model development are no longer daunting to enterprises given the many efficient and simple solutions provided by cloud or 3rd-party vendors. As a result, ML went from something only the bleeding edge innovators (such as Netflix and Amazon) were doing, to now a strategic enabler for organizations in the "early majority" stage of adoption. However, enterprises soon find that building a machine learning model isn't the end of the road but just the beginning of a new set of challenges: Because this is all so new, most enterprises do not have a pre-defined set of parameters to evaluate the different solutions for operationalizing ML models. As a result, they are not sure which attributes will allow their AI-enabled products and operations to scale in the long term as they add more models, use more data, or build more complex models.

Enterprises are increasingly looking to AI for opportunities to boost revenue as their operations move online. According to a 2021 PricewaterhouseCoopers survey, a quarter of companies report widespread adoption of AI in their organizations -- up from 18% in 2020. But AI projects are at risk of stalling due to the many roadblocks businesses encounter on the pathway to implementation. In a Gartner report, analysts estimate that 85% of AI projects will deliver erroneous outcomes -- whether due to bias in the data and algorithms or the teams managing them. An emerging discipline called machine learning operations, or MLops, aims to prevent these failures by combining machine learning, devops, and data engineering to facilitate the deployment and maintenance of AI models.

Suppose your social network for chinchilla owners has taken off. Your flagship app contains an embedded chat client where community members discuss chinchilla-related topics in real-time. As your user base grows, so does its value as a target for advertising. You now have a spam problem on your hands, and your small team of engineers has only so much time they can dedicate to this arms race. Here's how Wallaroo can help.