MetaSpore Alternatives

MetaSpore reviews and mentions

• Quickly develop risk control algorithms in business scenarios based on MetaSpore
  1 project | /r/learnmachinelearning | 15 Jun 2022

  The evaluation problems related to financial loans are mainly based on tabular data, so the importance of feature engineering is self-evident. The common features in the dataset include ID type, Categorical type, and continuous number type, which require common data handling such as EDA, missing value completion, outlier processing, normalization, feature binning, and importance assessment. The process can reference the GitHub codebase: https://github.com/meta-soul/MetaSpore/blob/main/demo/dataset, which part about tianchi_loan instructions.

  1 project | dev.to | 15 Jun 2022

  DMetaSoul uses MetaSpore on AlphaIDE to quickly implement a loan default rate prediction model on an open-source dataset and build a scorecard based on this model. Based on the Demo system of this version, the methods of feature derivation, binning, and screening can be more delicate, which often determines the upper limit of the performance of the risk control system. Finally, give the address of the code base and the AlphaIDE trial link (AlphaIDE tutorial): Default rate forecast: https://github.com/meta-soul/MetaSpore/tree/main/demo/riskmodels/loan_default MetaSpore's one-stop machine learning development platform: https://github.com/meta-soul/MetaSpore AlphaIDE trial link: https://registry-alphaide.dmetasoul.com

• A New One-stop AI development and production platform, AlphaIDE
  2 projects | dev.to | 15 Jun 2022

  I’ve posted about LakeSoul, an open-source framework for unified streaming and batch table storage, and MetaSpore, an open-source platform for machine learning.

• Usage Guide：Quickly deploy an intelligent data platform with the One-stop AI development and production platform, AlphaIDE
  1 project | dev.to | 14 Jun 2022

  AlphaIDE is already integrated with MetaSpore. You can test MetaSpore’s introductory tutorial Notebook: https://github.com/meta-soul/MetaSpore/blob/main/tutorials/metaspore-getting-started.ipynb.

• [P]MMML | Deploy HuggingFace training model rapidly based on MetaSpore
  1 project | /r/MachineLearning | 1 Jun 2022

  Presented here on the lot code: https://github.com/meta-soul/MetaSpore/compare/add_python_preprocessor

• MMML | Deploy HuggingFace training model rapidly based on MetaSpore
  1 project | /r/learnmachinelearning | 1 Jun 2022

  DMetaSoul aims at the above technical pain points, abstracting and uniting many links such as model training optimization, online reasoning, and algorithm experiment, forming a set of solutions that can quickly apply offline pre-training model to online. This paper will introduce how to use the HuggingFace community pre-training model to conduct online reasoning and algorithm experiments based on MetaSpore technology ecology so that the benefits of the pre-training model can be fully released to the specific business or industry and small and medium-sized enterprises. And we will give the text search text and text search graph two multimodal retrieval demonstration examples for your reference.

• MMML | Deployment HuggingFace training model rapidly based on MetaSpore
  2 projects | dev.to | 11 May 2022

  A few days ago, HuggingFace announced a $100 million Series C funding round, which was big news in open source machine learning and could be a sign of where the industry is headed. Two days before the HuggingFace funding announcement, open-source machine learning platform MetaSpore released a demo based on the HuggingFace Rapid deployment pre-training model.

• The design concept of an almighty Opensource project about machine learning platform
  1 project | dev.to | 30 Apr 2022

  2.5 [MetaSpore](https://github.com/meta-soul/MetaSpore**) online algorithm application framework** Offline training frameworks and online Serving services are now available. Then, an algorithm in the business scene landing is still a final step: an online algorithm experiment. In a service scenario, to verify the validity of an algorithm model, a baseline needs to be established and compared with the new algorithm model. Therefore, an online experimental framework is needed which can easily define algorithm experiments, read online features, and call model prediction services. In addition, multiple experiments can be traffic segmented to achieve ABTest effect comparison. A configuration center is also needed to quickly carry out multiple experimental iterations, which can dynamically load refresh experiments and cut flow configurations, support hot loading of experimental parameters, and various debugging and trace functions. This link also directly determines whether the AI model can be finally implemented into practical business applications.

• Almighty Opensource project about machine learning you should try out
  1 project | dev.to | 12 Apr 2022

  MetaSpore, it has to be said, is a new machine learning platform with transcendent qualities that can solve problems that other products cannot. However, as a new open source project, it still has a lot to go, and I'll be keeping an eye on MetaSpore and sharing and reposting more information.

• A new machine learning platform that helps you quickly build industrial-grade recommendation systems
  1 project | /r/machinelearningnews | 9 Apr 2022

  MetaSpore is an open-source one-stop machine learning development platform produced by DMetaSoul, providing the whole process framework and development interface from data preprocessing, model training, offline experiment, and online prediction to online experiment bucket ABTest. It is hoped that users can quickly build industrial-grade AI systems with distributed machine learning training, high-performance model reasoning, high availability AB experimental framework, and other capabilities in a low-code way based on MetaSpore.

• A note from our sponsor - InfluxDB
  www.influxdata.com | 18 Apr 2024

  Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality. Learn more →