Neptune.ai
概述
总部
波兰
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成立年份
2017
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公司类型
私营公司
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收入
< $10m
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员工人数
51 - 200
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网站
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公司介绍
Neptune 是一款实验追踪器,适用于那些在调试和重现实验、共享结果以及混乱的模型交接方面遇到困难的机器学习团队。
它提供了一个单一的地方来跟踪、比较、存储和协作实验,以便数据科学家可以更快地开发可用于生产的模型,而机器学习工程师可以立即访问模型工件以便将它们部署到生产中。
物联网应用简介
Neptune.ai 是分析与建模, 基础设施即服务 (iaas), 应用基础设施与中间件, 机器人, 平台即服务 (paas), 和 传感器等工业物联网科技方面的供应商。同时致力于建筑物, 水泥, 建筑与基础设施, 消费品, 教育, 设备与机械, 石油和天然气, 和 零售等行业。
技术
用例
功能区
行业
服务
技术栈
Neptune.ai的技术栈描绘了Neptune.ai在分析与建模, 基础设施即服务 (iaas), 应用基础设施与中间件, 机器人, 平台即服务 (paas), 和 传感器等物联网技术方面的实践。
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设备层
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边缘层
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云层
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应用层
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配套技术
技术能力:
无
弱
中等
强
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实例探究.
Case Study
Brainly's Integration with Amazon SageMaker and Neptune for Enhanced Machine Learning Capabilities
Brainly, a leading global learning platform, faced a challenge with their machine learning-powered feature, Snap to Solve. The feature allows users to upload a photo of a problem, which the system then detects and provides solutions for. The Visual Search team, responsible for the Visual Content Extraction (VICE) system of Snap to Solve, used Amazon SageMaker to run their computing workloads and serve their models. However, as the number of training runs on their large compute architectures increased, they found that their logs from Amazon SageMaker needed to be trackable and manageable to avoid workflow bottlenecks. They needed a tool that could scale regardless of the experiment volume. While they tried using SageMaker Experiments for tracking, they found the tracking UX and Python client unsatisfactory.
Case Study
Theta Tech AI: Enhancing Healthcare AI Systems with Neptune
Theta Tech AI, a company that builds customized artificial intelligence algorithms and front-end user interfaces for large-scale healthcare AI systems, faced several challenges in developing generalizable medical AI systems. The team had to manage thousands of experiments for large-scale parallel training workflows, which were run on GPU servers in AWS. However, they found that AWS CloudWatch Logs, their initial choice for monitoring the jobs, was inadequate for managing experiment logs. The team was unable to get experiment-relevant metrics from AWS CloudWatch Logs, debug problems with training jobs and experiments, integrate Optuna for hyperparameter optimization, and communicate the results of ML models to clients effectively.
Case Study
Optimizing Infrastructure Design with Continuum Industries' Optioneer Engine and Neptune
Continuum Industries, a company in the infrastructure industry, developed a product called Optioneer to automate and optimize the design of linear infrastructure assets. However, operating the Optioneer engine presented several challenges. The engine needed to be robust enough to handle different problems fed by different customers. Moreover, the company needed to ensure that the solutions provided by the engine were accurate and agreed upon by civil engineers. The team also had to constantly improve the optimization engine without breaking the algorithm. The nature of the problem they were trying to solve presented additional challenges. They could not automatically tell whether an algorithm output was correct or not. They needed a set of example problems that was representative of the kind of problem that the algorithm would be asked to solve in production. The team initially developed a custom solution to these problems, but it proved to be extremely clunky and complex to maintain.
Case Study
Leveraging Machine Learning to Analyze Impact of Promotional Campaigns on Sales
deepsense.ai, an AI-focused software services company, was tasked with a project for a leading Central and Eastern European food company. The project involved using machine learning to analyze the impact of promotional campaigns on sales. The food company runs various promotional campaigns for different products and wanted to create a model that predicts the number of sales per day for a given product on a promotional campaign. The challenge was the complexity of the data involving a large corpus of data sources, hundreds of different products, contractors, thousands of contractors’ clients, different promotion types, various promotion periods, overlapping promotions, and actions of the competition. It was also difficult to determine whether the sales increase was caused by any of the promotions applied, by the synergy between them, or it took place regardless of any campaigns.
Case Study
Waabi's Implementation of Neptune for Enhanced Experimentation Workflow and Resource Monitoring
Waabi, a company focused on developing the next generation of self-driving truck technology, faced a significant challenge in managing their large-scale experimentation workflow. Their Machine Learning teams, organized around different technical pillars, constantly launched experiments for different tasks, seeking model improvements by iteratively fine-tuning them and regularly comparing results against established benchmarks. The data involved in these experiments was diverse, including maps, LiDAR, camera, radar, inertial, and other sensor data. Keeping track of the data collected from these experiments and exporting it in an organized and shareable way became a challenge. The company also identified a lack of tooling for planning and building consistent benchmark datasets. They needed a solution that would allow them to share benchmark results in a constant place and format and retain data for later comparison after the end of a project.
Case Study
InstaDeep's BioAI Team Streamlines Experiment Management with Neptune
InstaDeep's BioAI team was faced with the challenge of managing numerous experiments for their DeepChain™ platform, a complex software for protein design. The team was dealing with scattered experiment logs, difficulty in sharing experiment results, and the burden of handling infrastructure and operations. The team needed a solution that was easy to use, could connect to TensorFlow and PyTorch logs, and was cost-effective. The challenge was to find a tool that could streamline their experiment management process, improve visibility of experiment logs, and facilitate collaboration among researchers and engineers.
Case Study
Optimizing Sports Data Analysis with IoT: A Case Study of ReSpo.Vision
ReSpo.Vision, a company specializing in sports data analysis, faced significant challenges in managing their machine learning (ML) pipelines at scale. The company uses computer vision and machine learning to extract 3D data from single-view camera sports broadcast videos, providing valuable insights to players, scouts, managers, clubs, and federations. Their ML team collects raw data, labels it, and adds new datasets to training and evaluation pipelines. However, as they scaled up the number of matches processed, the number of pipelines ran to build different models also increased, making it extremely hard to manage the workflow and debug pipeline failures. They had difficulty in debugging issues with the experiment results, figuring out if their pipelines all finished successfully, and comparing the results of each run to the previous runs. Additionally, it was problematic to know what dataset or parameters were used for each experiment run. They needed a better way to manage their pipeline runs and make the best use of their resources.
Case Study
Implementing Neptune for Efficient Machine Learning in Bioinformatics: A Case Study of ailslab
ailslab, a small bioinformatics research group, is dedicated to building machine learning models to predict cardiovascular disease development using clinical, imaging, and genetics data. The research process is intense, requiring a custom infrastructure to extract features from various data types, including Electronic Health Records (EHR), time-to-event data, images, structured data, and ECG. The goal is to create precise machine learning models for risk stratification for primary cardiovascular prevention. However, as the team grew, collaboration became more challenging, and new problems began to emerge. These included issues with data privacy, workflow standardization, feature and model selection, experiment management, and information logging.
Case Study
Hypefactors: Enhancing Media Intelligence with IoT and Machine Learning
Hypefactors, a technology company specializing in media intelligence and reputation tracking, faced a significant challenge in managing their data pipelines. These pipelines monitor a wide range of media, including social media, print, television, and radio, to analyze changes in their customers' brand reputation. The process involves gathering data from various sources and enriching it with machine learning (ML) features. However, as the company expanded its operations and started working on more complex ML problems, they encountered difficulties in tracking their experiments. Initially, the team used Slack for collaboration and personal notes/files for storing training metadata and model artifacts. However, as the number of models, features, and team members increased, this method became inefficient and created structural bottlenecks.
Case Study
Streamlining Research and Project Management in AI and ML with Neptune: A Case Study at TH Köln
TH Köln, Germany’s largest University of Applied Sciences in the Electrical Engineering Department, was facing significant challenges in managing large-scale research projects. The department, which focuses on meta-learning research with standard ML frameworks such as TensorFlow and PyTorch, was struggling with experiment-tracking across multiple servers. The team was manually creating CSV files to record details generated during the experiment run such as loss or f2 score. Additionally, there were separate files with hyperparameters and other configurations. This manual management of multiple files made the analysis of past experiments extremely challenging and prone to errors. The team was also facing issues with multi-server project management, access control management, result comparison and presentation, and loss of experiment history when students left.
Case Study
Zoined: Enhancing Retail and Hospitality Analytics with Neptune
Zoined, a company offering cloud-based Retail and Hospitality Analytics, faced a significant challenge in tracking and managing experiments, especially with a small team of scientists and engineers. The company's data scientist, Kha, was solely responsible for the forecasting pipeline, making experiment tracking a tedious manual task. Kha was dealing with large data frames with forecasts that needed to be logged alongside their experiments. He also needed a way to visualize results for complete and intermediate experiments to enhance efficiency. The team initially tried using Splunk for experiment tracking, but it proved to be intimidating, difficult for visualizing logged values, and expensive. The next solution, MLflow, presented issues with hosting options, was compute-intensive, and had problems with auto scaling. It also made collaboration difficult as sharing experiments was not straightforward.