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Provectus > Case Studies > EarthSnap: Transforming AI Image Identification with MLOps & Managed AI
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EarthSnap: Transforming AI Image Identification with MLOps & Managed AI

Technology Category
  • Analytics & Modeling - Machine Learning
  • Application Infrastructure & Middleware - Event-Driven Application
Applicable Industries
  • Cement
  • Construction & Infrastructure
Applicable Functions
  • Maintenance
  • Product Research & Development
Use Cases
  • Computer Vision
  • Construction Management
Services
  • Data Science Services
  • Training
The Challenge

Earth.com, a leading internet platform for environmental enthusiasts, aimed to accelerate the development and delivery of EarthSnap, an AI-powered image identification application. The goal was to modernize and automate the application’s machine learning (ML) infrastructure, simplify the deployment of new models, and reduce administrative costs. The company insisted on following best practices for end-to-end ML, DevOps, and app development. However, Earth.com lacked an in-house ML engineering team, which made it challenging to add new datasets, improve existing models, release new ones, and scale the ML solution. The models delivered by their previous partner were satisfactory in terms of accuracy but required manual sequential execution for data processing and model retraining. The deployment of endpoints also had to be done manually. Earth.com sought a new strategic partner to streamline the delivery of EarthSnap to market, and Provectus, an AWS Premier Consulting Partner, was chosen for the role.

About The Customer

Earth.com is a premier internet destination for users who are concerned about the planet and the environment and want to make a difference. The company developed EarthSnap, a first-of-its-kind application that enables users to identify all types of plant and animal species via a mobile phone camera. The application is powered by a custom-built, patent-pending AI machine learning solution, which identifies the subject and shares details like habitat, global population distribution, and known history of earth. Founded in 2016 by Eric Ralls, Earth.com Inc. is headquartered in Telluride, CO. The company is committed to providing a vast trove of information regarding life on our planet and functioning as a reference library to assist in research projects.

The Solution

Provectus addressed Earth.com’s challenges through a series of engagements. They evaluated the work done by the previous partner by investigating data, reproducing ML models, and examining EarthSnap’s backend components. Provectus built fully automated, end-to-end ML pipelines to facilitate the deployment of new model versions, following best practices for data, ML/MLOps, and CI/CD. They also initiated the provision of Managed AI Services, which includes ML infrastructure maintenance, model monitoring and retraining, data quality monitoring, and troubleshooting. Provectus automated the training and deployment of the models, enabling the release of new models without technical support. They also worked on a candidate model, which resulted in success. Provectus offered to enhance the solution with its Managed MLOps Platform, delivered as part of Managed AI Services. The offering included maintenance and support of the existing ML infrastructure, implementation of components for access management, cost monitoring, networking, and compliance support, and 24/7 troubleshooting.

Operational Impact

  • Provectus helped Earth.com to get EarthSnap back on track. In three months, they designed, built, and implemented fully automated, end-to-end ML pipelines for real-time CV models. The pipelines made ML workflows more observable and facilitated the deployment of new versions of the models, gradually improving and extending them by adding recognition of new species. The enhancements helped Earth.com to reduce engineering heavy lifting and minimize administrative costs. Now, with all components of EarthSnap modernized, Earth.com is ready for future expansion. As part of Managed AI Services, Provectus reduced the infrastructure management overhead, established well-defined SLA and processes, ensured 24/7 coverage and support, and increased overall infrastructure stability, including production workloads and critical releases. They initiated a series of enhancements to deliver their Managed MLOps Platform and improve ML engineering. By collaborating with Provectus, Earth.com was able to release the EarthSnap application on both the Appstore and Playstore ahead of schedule.

Quantitative Benefit

  • Fully automated, end-to-end ML pipelines delivered in 3 months

  • Flexible and easily customizable ML solution for faster, at-scale ML

  • Reduced heavy lifting and minimized administrative costs project-wide

Related Case Studies.

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The Zenitaka Corporation ('Zenitaka') has two major business areas: its architectural business focuses on structures such as government buildings, office buildings, and commercial facilities, while its civil engineering business is targeted at structures such as tunnels, bridges and dams. Within these areas, there presented two issues that have always persisted in regard to the construction of mountain tunnels. These issues are 'improving safety" and "reducing energy consumption". Mountain tunnels construction requires a massive amount of electricity. This is because there are many kinds of electrical equipment being used day and night, including construction machinery, construction lighting, and ventilating fan. Despite this, the amount of power consumption is generally not tightly managed. In many cases, the exact amount of power consumption is only ascertained when the bill from the power company becomes available. Sometimes, corporations install demand-monitoring equipment to help curb the maximum power demanded. However, even in these cases, the devices only allow the total volume of power consumption to be ascertained, or they may issue warnings to prevent the contracted volume of power from being exceeded. In order to tackle the issue of reducing power consumption, it was first necessary to obtain an accurate breakdown of how much power was being used in each particular area. In other words, we needed to be able to visualize the amount of power being consumed. Safety, was also not being managed very rigorously. Even now, tunnel construction sites often use a 'name label' system for managing entry into the work site. Specifically, red labels with white reverse sides that bear the workers' names on both sides are displayed at the tunnel work site entrance. The workers themselves then flip the name label to the appropriate side when entering or exiting from the work site to indicate whether or not they are working inside the tunnel at any given time. If a worker forgets to flip his or her name label when entering or exiting from the tunnel, management cannot be performed effectively. In order to tackle the challenges mentioned above, Zenitaka decided to build a system that could improve the safety of tunnel construction as well as reduce the amount of power consumed. In other words, this new system would facilitate a clear picture of which workers were working in each location at the mountain tunnel construction site, as well as which processes were being carried out at those respective locations at any given time. The system would maintain the safety of all workers while also carefully controlling the electrical equipment to reduce unnecessary power consumption. Having decided on the concept, our next concern was whether there existed any kind of robust hardware that would not break down at the construction work site, that could move freely in response to changes in the working environment, and that could accurately detect workers and vehicles using radio frequency identification (RFID). Given that this system would involve many components that were new to Zenitaka, we decided to enlist the cooperation of E.I.Sol Co., Ltd. ('E.I.Sol') as our joint development partner, as they had provided us with a highly practical proposal.
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