This paper focuses on the physical racetrack capabilities of AWS DeepRacer. Two separate experiments were conducted. The first experiment (Experiment I) focused on evaluating the impact of hyperparameters on the physical environment. Hyperparameters such as gradient descent batch size and loss type were changed systematically as well as training time settings. The second experiment (Experiment II) focused on exploring AWS DeepRacer object avoidance in the physical environment. It was uncovered that in the simulated environment, models with a higher gradient descent batch size had better performance than models with a lower gradient descent batch size. Alternatively, in the physical environment, a gradient descent batch size of 128 appears to be preferable. It was found that models using the loss type of Huber outperformed models that used the loss type of MSE in both the simulated and physical environments. Finally, object avoidance in the simulated environment appeared to be effective; however, when bringing these models to the physical environment, there was a pronounced challenge to avoid objects. Therefore, object avoidance in the physical environment remains an open challenge.

This post is co-written by Zdenko Estok, Cloud Architect at Accenture and Sakar Selimcan, DeepRacer SME at Accenture. The creation of a scalable and hassle-free data science environment is key. It can take a considerable amount of time to launch and configure an environment tailored for a specific use case and even harder to onboard colleagues to collaborate. According to Accenture, companies that manage to efficiently scale AI and ML can achieve nearly triple the return on their investments. Still, not all companies meet their expected returns on their AI/ML journey.

The AWS offers hands-on learning,scholarships,and mentorship for people underserved or underrepresented in tech through AI and ML scholarship. Data is everywhere,it drives the world the constant need to make data driven decisions cannot be ignored anymore. The need to get superior performance, deliver faster and accurate results is a deal breaker . It is a make and break point that can not be glossed over even more so because of the need to process an outstanding amount of data informs the decision to migrate to the Cloud. No longer is it business as usual as most legacy frameworks may be soon be discarded.

Only 9 days until AWS re:Invent 2021, and we're very excited to share some highlights you might enjoy this year. The AI/ML team has been working hard to serve up some amazing content and this year, we have more session types for you to enjoy. Back in person, we now have chalk talks, workshops, builders' sessions, and our traditional breakout sessions. Last year we hosted the first-ever machine learning (ML) keynote, and we are continuing the tradition. We also have more interactive and fun events happening with our AWS DeepRacer League and AWS BugBust Challenge.

In November 2018, Amazon launched AWS DeepRacer, a car about the size of a shoebox that runs on AI models trained in a virtual environment with reinforcement learning techniques. DeepRacer has expanded since then, with a women's league and new miniature race cars. Starting today, Amazon is making the DeepRacer device software available in open source. The pandemic has boosted automation and robotics in the enterprise. The global market for robots is expected to grow at a compound annual growth rate of around 26% to reach just under $210 billion by 2025, according to Statista.

With less than a week to re:Invent 2020, we are feeling the excitement and thrill, and looking forward to seeing you all at the world's premier cloud learning event. As always, artificial intelligence (AI) and machine learning (ML) continue to be on the list of top topics with our customers and partners. We're making it bigger and better this year with the first ever machine learning keynote, over 50 technical breakout sessions, live racing with the AWS DeepRacer League, and more. You'll hear from AWS experts as well as many of our customers including NASCAR, Intuit, McDonalds, Mobileye, NFL, Siemens Energy, and many others, across industries such as sports, finance, retail, autonomous vehicles, manufacturing, and more. To help you plan your agenda for the extravaganza, here are a few highlights from the artificial intelligence and machine learning track at re:Invent 2020.

AWS DeepRacer is a cloud-based 3D racing simulator, an autonomous 1/18th scale race car driven by reinforcement learning, and a global racing league. Reinforcement learning (RL), an advanced machine learning (ML) technique, enables models to learn complex behaviors without labeled training data and make short-term decisions while optimizing for longer-term goals. But as we humans can attest, learning something well takes time--and time is money. You can build and train a simple "all-wheels-on-track" model in the AWS DeepRacer console in just a couple of hours. However, if you're building complex models involving multiple parameters, a reward function using trigonometry, or generally diving deep into RL, there are steps you can take to optimize the cost of training.

First announced in November 2018, AWS DeepRacer is a fun and interesting way to get rolling with reinforcement learning (RL), literally, with the fully …

AWS DeepRacer is a fun and easy way for developers with no prior experience to get started with machine learning (ML). At the end of the 2019 season, the AWS DeepRacer League engaged the Amazon ML Solutions Lab to develop a new sports analytics feature for the AWS DeepRacer Championship Cup at re:Invent 2019. The purpose for these real-time analytics was to provide context and more in-depth experience with top competitors' strategies and tactics. This helped viewers tangibly interpret how specific model strategy translated to on-track performance, which further demystified ML development and demonstrated its real-world application. This enhancement enabled fans to monitor the performance and driving style of competitors from around the world.

Start date/time: May 1st, 10 AM PT / 1PM ET Description: Building machine learning-enabled products is hard for developers & data scientists; throw in a hardware component & the complexity increase exponentially. Lyndon Leggate introduces you to reinforcement learning & walks you through a step-by-step demonstration of how you developer can up level your RL skills through autonomous driving. Lyndon is a keen participant in the AWS DeepRacer league. Racing as Etaggel, he regularly positioned in the top 10 during the 2019 league, is featured in DeepRacer TV & in May 2019 established the AWS DeepRacer Community.