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cnvrg.io > Case Studies > AI-Powered Recruitment Transformation: PandoLogic's Journey with Automated MLOps Pipelines
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AI-Powered Recruitment Transformation: PandoLogic's Journey with Automated MLOps Pipelines

Technology Category
  • Analytics & Modeling - Machine Learning
  • Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
  • Cement
  • Construction & Infrastructure
Applicable Functions
  • Sales & Marketing
Use Cases
  • Construction Management
  • Infrastructure Inspection
Services
  • Cloud Planning, Design & Implementation Services
  • System Integration
The Challenge

PandoLogic, an AI-based programmatic job advertising platform, was facing challenges in operationalizing machine learning (ML) on premises due to lack of DevOps or infrastructure. Despite having a lean team of data scientists, their productivity was constantly interrupted with DevOps tasks and infrastructure challenges. They lacked the resources and infrastructure to operationalize their impressive models to achieve real business results. They were limited to on-premise deployment which caused technical challenges and DevOps overhead. They required dynamic Spark Clusters to handle terabytes of data, which wasted weeks of set up time, and caused major overhead costs to maintain. The PandoLogic team wanted a way to train and deploy on premise and in multiple clouds, without being locked into a single cloud. They needed an easy way to leverage open source tools and the compute resources they already had.

About The Customer

PandoLogic is an AI-based programmatic job advertising platform that intelligently automates and optimizes job advertising spend. Their platform, pandoIQ, provides an end-to-end job advertising solution that delivers a significant increase in job ad performance without any wasteful spending. PandoIQ’s AI-enabled algorithms use over 200+ job attributes and more than 200 billion historical job performance data points to predict the optimal job advertising campaign. The models are continuously being retrained automatically to production. PandoLogic serves customers like Fedex, Dominos, Postmates and other Fortune 500 organizations.

The Solution

PandoLogic adopted cnvrg.io, an enterprise-level ML infrastructure platform, to overcome their challenges. cnvrg.io provided one-click integration with AWS data lakes, Kubernetes deployments, Spark Clusters, and cloud compute all with little overhead. It delivered a resource management system that can leverage on-prem resources and automatically bursts to the cloud and terminates with one click. PandoLogic leveraged cnvrg.io’s Flow UI tool to build custom ML pipelines that allows each task to run on a different cloud simultaneously. They were able to take their Jupyter Notebooks to process the data, save metadata and analyze the flow, while using Python to run experiments, and serve their models with Kubernetes, AWS and on-premise servers. Using the AI library, PandoLogic built custom drag and drop pipelines pulled from their GitHub repo, and used their proprietary algorithms as well as XGBoost, LightGBM, Ensemble Methods and other open source ML and DL frameworks like PyTorch with the cnvrg.io platform.

Operational Impact

  • With cnvrg.io, PandoLogic was able to quickly adopt an entire enterprise-level ML infrastructure platform from zero to production. This would have otherwise taken a team of specialized ML engineers, and months of DevOps time to build. cnvrg.io’s MLOps solution has accelerated workflows drastically, allowing spinning up a powerful AI environment in one click without manual DevOps. PandoLogic can now version control data, experiments and monitor models in a single place, something their previous version control system was not comprehensive enough to handle. This has allowed PandoLogic to focus on building and delivering models rather than building their own infrastructure, thus adding real business value.

Quantitative Benefit

  • not mentioned

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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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