In today’s technological landscape, artificial intelligence (AI) and machine learning (ML) are becoming increasingly accessible, enabling builders of all skill levels to harness their power. As more companies adopt AI solutions, there’s a growing need to upskill both technical and non-technical teams in responsibly expanding AI usage. Getting hands-on experience is crucial for understanding and applying ML concepts to automate tasks like content generation, language translation, and image classification. And that’s where AWS DeepRacer comes into play—a fun and exciting way to learn ML fundamentals.
Launched in 2019, DeepRacer is a fully managed service that enables builders of all skill levels to learn and perform model training and evaluation tasks such as defining a reward function, setting up the training parameters, and configuring a training job that can be evaluated and monitored for model performance in a simulated environment. By exploring the AWS DeepRacer ML training lifecycle, you’ll practice model training, evaluation, and deployment of ML models onto a 1/18th scale autonomous race car, using a human-in-the-loop experience. The model training and evaluation experience enables builders to familiarize themselves with similar concepts applicable in training and fine-tuning foundation models (FMs) that power generative AI applications.
AWS DeepRacer also offers a global racing league for competing alongside a community of ML enthusiasts, earning rewards and recognition while showcasing your ML skills. Through the AWS DeepRacer League, we have educated over 550,000 developers, crowned five AWS DeepRacer champions, recognized over 100 monthly virtual circuit winners, and rewarded over 10,000 participants worldwide with Amazon gift cards, cash prizes, and paid trips to AWS re:Invent to compete for the annual AWS DeepRacer Championship Cup.
The excitement around AWS DeepRacer extends far beyond just individual learners. To celebrate Women’s History Month, JPMorgan Chase & Co. recently hosted the “World’s Largest Global Women’s AWS DeepRacer League,” providing employees with a thrilling opportunity to gain hands-on ML experience through virtual autonomous vehicle racing. This event not only fostered a spirit of friendly competition but also celebrated empowerment and innovation in AI and ML. By embracing AWS DeepRacer, JPMorgan Chase showcased its commitment to democratizing ML knowledge and nurturing a culture of continuous learning, empowering its talented teams to drive the company’s AI transformation.
“I am super proud of the group, the firm and the TIF (Take it Forward) team. . . I couldn’t be more proud of a group of individuals being so self-motivated. The sky is the limit from here! Deep Racer is proof that learning can be fun.”
– Ebele Kemery, Head of JPMorgan Chase Tech, Data and AI Learning.
Initiatives like these demonstrate the far-reaching impact of AWS DeepRacer in bringing ML education to the forefront, inspiring learners of all backgrounds to embrace the future of intelligent technologies.
Whether you’re a seasoned developer or curious business professional, AWS DeepRacer provides a fun and exciting way to get started with AI. You’ll gain practical skills applicable to real-world ML and generative AI use cases. So get rolling with machine learning today!
About the authors
Ange Krueger is a principal AWS technologist. She leads product portfolio advancements and technological agility within the global financial sector. Utilizing over 200 AWS cloud services including leading AWS Artificial Intelligence, Machine Learning and Generative AI offerings, she delivers innovation, transformation, and scalable solutions that precisely address the complex demands of our global customers. Through a collaborative approach and a laser focus on customer-centric outcomes, Ange enhances customer experiences to achieve optimized business performance. Her commitment to continual improvement and customer obsession is unwavering, as she works to empower our clients with resilient, cloud-based financial services solutions.
Source link
lol
