Machineroad is an app designed for cricketers, enabling coaches, aspiring athletes and professionals to capture data, analyse progress, and measure players against others in the community – all from their smartphones.
Designed to detect the fluorescent colour of a pink Kookaburra cricket ball, the app captures a variety of performance statistics including bowling speed and ball length and can report everything via a handy pitch map.
With the goal of putting a speed gun in everyone’s pocket, Machineroad enables users to capture and analyse the performance data of cricketers, helping finetune skills and improving the odds of achieving sporting excellence.
Change in cloud provider
Whilst already hosted in the cloud, Machineroad had taken a strategic long-term decision to migrate to the AWS cloud.
Pushing the boundaries for mobile technology
To capture ball trajectories and speeds, the Machineroad interface is overlaid on top of cricket wickets all around the world. The app is built on a series of complex AI (artificial intelligence), AR (augmented reality) and ML (machine learning) algorithms and pushes the boundaries of what can be delivered by a smartphone. This needs to be balanced against delivering a stable and consistent performance for end users.
Better user experience
With an objective of global expansion and growing their user base, Machineroad wanted to prioritise delivering a better user experience with a focus on performance speed, accuracy of results and uptime whilst introducing new device connectivity.
Cloud migration to AWS
Our team of full stack experts carried out a lift and shift migration, moving the Machineroad architecture to the AWS cloud. Using our proven processes and thorough planning this was done quickly and without disruption.
Expert cloud architecture
Having migrated the Machineroad application to AWS, we proposed a best practice cloud architecture. One that would optimise the environment for improved performance, enabling a better user experience, enhanced levels of security and cost rationalisation. Our solution leverages ECS (elastic container service), RDS (relational database service), bastion hosts and NAT (network address translation) gateways. In addition, we leveraged the AWS CDK (infrastructure as code) to enable infrastructure to be programmatically deployed to reduce infrastructure drift between environments.
New app version available via App Store and Google Play
The team at Machineroad has been able to release a new free version of their product. Hosted on the AWS cloud, this version has clocked up thousands of downloads from the App Store and Google Play with 5,000+ downloads coming in just three days.
Serverless solution for performance at scale
The solution we’ve implemented has allowed Machineroad to go serverless by implementing AWS’s ECS (elastic container service), a highly secure, reliable and scalable way to run containers. This removes the need to provision and manage servers whilst delivering performance at scale.
Secure and reliable
The new Machineroad app is more secure and more reliable than ever before. We have built in application isolation, configured a NAT gateway - to enable instances in a private subnet to connect to the internet whilst preventing the internet from initiating a connection with those instances, and is protected within a VPC (virtual private cloud). With 69 availability zones across 22 regions, our solution is backed by the AWS compute SLA which guarantees a monthly uptime percentage of at least 99.99%.
Optimised for cost
Having delivered a highly scalable solution, Machineroad can specify and pay for just the resources their application needs to deliver a consistent and accurate service. When there is a peak in users, the solution can automatically scale up and when user numbers decline, the solution can scale down, meaning the team aren’t paying for resources that are sitting idle.
Platform for evolving features
With more and more users joining the Machineroad community, the app is being used by cricketers all around the world. With the app being exposed to new environments it’s vital that its algorithms can learn in order to deliver a consistent and accurate service. A cricket wicket outside in Abu Dhabi is vastly different in appearance to an indoor net in New Zealand for example. Our solution enables the use of Amazon SageMaker, a fully managed service that provides Machineroad with the ability to easily build, train, and deploy ML.
Amazon Web Services
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