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HomeArtificial Intelligence and Machine LearningLeveraging artificial intelligence and machine learning at Parsons with AWS DeepRacer

Leveraging artificial intelligence and machine learning at Parsons with AWS DeepRacer

This post is co-written with Jennifer Bergstrom, Sr. Technical Director, ParsonsX.

Parsons Corporation (NYSE:PSN) is a leading disruptive technology company in critical infrastructure, national defense, space, intelligence, and security markets providing solutions across the globe to help make the world safer, healthier, and more connected. Parsons provides services and capabilities across cybersecurity, missile defense, space ground station technology, transportation, environmental remediation, and water/wastewater treatment to name a few.

Parsons is a builder community and invests heavily in employee development programs and upskilling. With programs such as ParsonsX, Parsons’s digital transformation initiative, and – ‘The Guild,’ – an employee-focused community, Parsons strives to be an employer of choice and engages its employees in career development programs year-round to create a workforce of the future.

In this post, we show you how Parsons is building its next generation workforce by using machine learning (ML) and artificial intelligence (AI) with AWS DeepRacer in a fun and collaborative way.

As Parsons’ footprint moves into the cloud, their leadership recognized the need for a change in culture and a fundamental requirement to educate their engineering task force on the new cloud operating model, tools, and technologies. Parsons is observing industry trends that make it imperative to incorporate AI and ML capabilities into the organization’s strategic and tactical decision-making processes. To best serve the customer’s needs, Parsons must upskill its workforce across the board in AI/ML tooling and how to scale it in an enterprise organization. Parsons is on a mission to make AI/ML a foundation of business across the company.

Parsons chose AWS DeepRacer because it’s a fun, interactive, and exciting challenge that appealed to their broader range of employees and didn’t mandate a significant level of expertise to compete. Parsons found that AWS has many dedicated AWS DeepRacer experts in the field who would help plan, setup and run a series of AI/ML events and challenges. Parsons realized success of this event would be driven by efficient mechanisms and processes the AWS DeepRacer community has in place.

Parsons’ goal was to upskill their employees in an enjoyable and competitive way, with virtual leagues among peer groups and an in-person event for the top racers. The education initiative in partnership with AWS was comprised of four phases.

First, Parsons hosted a virtual live workshop with AWS experts in the AI/ML and DeepRacer community. The workshop taught the basics of reinforcement learning, reward functions, hyperparameter tuning, and accessing the AWS DeepRacer console to train and submit a model.

In the next phase, they hosted a virtual community league race for all participating Parsons employees. Models were optimized, submitted, and raced, and winners were announced at the end of racing. Participants in the virtual leagues were comprised of individual contributors and frontline managers from various job roles across Parsons, including civil engineers, bridge engineers, systems and software engineers, data analysts, project managers, and program managers. Joining them as participants in the league were business unit presidents, SVPs, VPs, senior directors, and directors.

In the third phase, an in-person league was held in Maryland. The top four participants from the virtual leagues saw their models loaded into and raced in physical AWS DeepRacer cars on a track built onsite. The top four competitors at this event included a market CTO, a project signaling engineer, a project engineer, and an engineer intern.

The fourth and final phase of the event had each of the four competitors provide a technical walkthrough of the techniques used to develop, train, and test their models. Through AWS DeepRacer, Parsons not only showcased the impact this event was able to make globally across all the divisions, but also that they were able to create a memorable experience for participants.

Over 500 employees registered from various business units and service organizations across Parsons worldwide right after an announcement of the AWS DeepRacer challenge was published internally. The AWS DeepRacer workshop saw unprecedented interest with over 470 Parsons employees joining the initial workshop. The virtual workshop generated significant engagement – 245 active users developed over 1,500+ models and spent over 500 hours training these models on the AWS DeepRacer console. The virtual league was a resounding success, with 185 racers from across the country participating and submitting 1415 models into the competition!

The virtual AWS DeepRacer league at Parsons provided a fun and inviting environment with lots of iterations, learning, and experimentation. Parsons’ Market CTO, John Statuli, who was one of the top four contenders at the race said, “It was a lot of fun to participate at the AWS DeepRacer event. I have not done any programming in a long time, but the combination of the AWS DeepRacer virtual workshop and the AWS DeepRacer program provided an easy way by which I could participate and compete for the top spot.”

At the final race held in Maryland, Parsons broadcasted a companywide virtual event that showcased a tough competition between their top four competitors from three different business units. Parsons top leadership joined the event, including CTO Rico Lorenzo, D&I CTO Ryan Gabrielle, President of Connected Communities Peter Torrellas, CDO Tim LaChapelle, and ParsonsX Sr. Director Jennifer Bergstrom. At the event, Parsons hosted a webinar with over 100 attendees and a winner’s walkthrough of their models.

With such an overwhelming response from employees across the globe and an interest in AI/ML learning, Parsons is now planning several additional events to continue growing their employees’ knowledgebase. To continue to upskill and educate their workforce, Parsons intends to run more AWS DeepRacer events and workshops focused on object avoidance, an Amazon SageMaker deep dive workshop, and an AWS DeepRacer head-to-head race. Parsons continues to engage with AWS on AI/ML services to build world-class solutions in the fields of critical infrastructure, national defense, space, and cybersecurity.

Whether your organization is new to machine learning or ready to build on existing skills, AWS DeepRacer can help you get there. To learn more visit Getting Started with AWS DeepRacer.

About the Authors

Jenn Bergstrom is a Parsons Fellow and Senior Technical Director. She is passionate about innovative technological solutions and strategies and enjoys designing well-architected cloud solutions for programs across all of Parsons’s domains. When not driving innovation at Parsons, she loves exploring the world with her husband and daughters, and mentoring diverse individuals transitioning into the tech industry. You can reach her on LinkedIn.

Deval Parikh is a Sr. Enterprise Solutions Architect at Amazon Web Services. She is passionate about helping enterprises reimagine their businesses in the cloud by leading them with strategic architectural guidance and building prototypes as an AWS expert. She is also an active board member of the Women at AWS affinity group where she oversees university programs to educate students on cloud technology and careers. She is also an avid hiker and a painter of oil on canvas. You can see many of her paintings at www.devalparikh.com. You can reach her on LinkedIn.

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