How Elevatus Uses Neptune to Check Experiment Results in 1 Minute
公司规模
SME
地区
- Middle East
国家
- Saudi Arabia
产品
- Neptune
- Google Cloud Storage
- PyTorch Lightning
- Optuna
- Kubernetes
技术栈
- Google Cloud
- Oracle Cloud
- Terraform
- Python
- FastAPI
实施规模
- Enterprise-wide Deployment
影响指标
- Digital Expertise
- Innovation Output
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 平台即服务 (PaaS) - 数据管理平台
适用行业
- Software
- Professional Service
适用功能
- 产品研发
- 商业运营
用例
- 机器状态监测
- 远程资产管理
服务
- 软件设计与工程服务
- 系统集成
关于客户
Elevatus is a talent management platform headquartered in Riyadh, Saudi Arabia, with a focus on covering the entire pre-HR cycle of recruitment, hiring, and onboarding. The company operates within the Software & Technology industry and employs between 11 to 50 people. Elevatus is known for its innovative approach to simplifying hiring processes through intelligent systems. The platform's Video Assessment product allows applicants to apply via a one-way interview, showcasing its commitment to reducing complexity in recruitment. The company's Innovation department plays a crucial role in developing systems that cater to client needs, emphasizing the importance of seamless integration and user-friendly solutions. Elevatus's dedication to innovation and efficiency is evident in its approach to managing machine learning models and optimizing recruitment processes.
挑战
The team at Elevatus faced significant challenges in managing their machine learning models due to the lack of an observability layer. This absence led to inefficiencies, such as the need to reanalyze data and reimplement workflows whenever evidence for language models was requested. The team recognized the necessity of a mature observability layer to facilitate large-scale training jobs and involve stakeholders in auditing AI systems. They sought a model tracking solution that could provide historical performance tracking and integrate well with their existing tools, such as PyTorch Lightning and Optuna. The goal was to enhance model performance tracking and optimize compute utilization, enabling quick iteration across experiments.
解决方案
To address the challenges faced, Elevatus integrated Neptune into their workflow as a model tracking solution. This integration allowed the team to monitor functional and operational processes post-training script execution. Neptune's configuration was hardcoded into their YAML manifest file, enabling seamless tracking of model performance over time. The solution provided a centralized view for the team, facilitating better model evaluation, compute utilization optimization, and rapid iteration across experiments. With Neptune, the team could scale experiment monitoring as dataset sizes grew, ensuring no computational bottlenecks. The customizable user interface offered by Neptune allowed the team to design their own metrics and dashboards, enhancing their ability to visualize training and conduct in-depth investigations. This flexibility empowered the team to achieve significantly better results, with Neptune serving as a crucial component in their machine learning operations.
运营影响
数量效益
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