Open Source Go Data Science Tools

Nuclio is an open source and managed serverless platform used to minimize development and maintenance overhead and automate the deployment of data-science-based applications. Real-time performance running up to 400,000 function invocations per second. Portable across low laptops, edge, on-prem and multi-cloud deployments. The first serverless platform supporting GPUs for optimized utilization and sharing. Automated deployment to production in a few clicks from Jupyter notebook. Deploy one of the example serverless functions or write your own. The dashboard, when running outside an orchestration platform (e.g. Kubernetes or Swarm), will simply be deployed to the local docker daemon. The Getting Started With Nuclio On Kubernetes guide has a complete step-by-step guide to using Nuclio serverless functions over Kubernetes.

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility. Average latency measured in milliseconds on trillion vector datasets. Rich APIs designed for data science workflows. Consistent user experience across laptop, local cluster, and cloud. Embed real-time search and analytics into virtually any application. Milvus’ built-in replication and failover/failback features ensure data and applications can maintain business continuity in the event of a disruption. Component-level scalability makes it possible to scale up and down on demand.

gophernotes is a Go kernel for Jupyter notebooks and nteract. It lets you use Go interactively in a browser-based notebook or desktop app. Use gophernotes to create and share documents that contain live Go code, equations, visualizations and explanatory text. These notebooks, with the live Go code, can then be shared with others via email, Dropbox, GitHub and the Jupyter Notebook Viewer. Go forth and do data science, or anything else interesting, with Go notebooks! This project utilizes a Go interpreter called gomacro under the hood to evaluate Go code interactively. The gophernotes logo was designed by the brilliant Marcus Olsson and was inspired by Renee French's original Go Gopher design. If you have the JUPYTER_PATH environmental variable set or if you are using an older version of Jupyter, you may need to copy this kernel config to another directory.