superduperdb VS mlops-python-package

Compare superduperdb vs mlops-python-package and see what are their differences.

superduperdb

🔮 SuperDuperDB: Bring AI to your database! Build, deploy and manage any AI application directly with your existing data infrastructure, without moving your data. Including streaming inference, scalable model training and vector search. (by SuperDuperDB)

mlops-python-package

Kickstart your MLOps initiative with a flexible, robust, and productive Python package. (by fmind)
InfluxDB - Power Real-Time Data Analytics at Scale
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.
www.influxdata.com
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superduperdb mlops-python-package
24 1
4,390 353
3.2% -
9.9 7.4
5 days ago about 2 months ago
Python Jupyter Notebook
Apache License 2.0 Creative Commons Attribution 4.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

superduperdb

Posts with mentions or reviews of superduperdb. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-12.

mlops-python-package

Posts with mentions or reviews of mlops-python-package. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-25.
  • When writing ML software - how do you use TDD?
    2 projects | /r/mlops | 25 Jun 2023
    I always use TDD when I work on serious AI/ML projects. Even if this practice is time-consuming in the short term, it's time efficient in the long run. I prefer to catch bugs as early as possible in my workflow. I recently worked on a MLOps Python package that provides examples to implement best practices like TDD, code coverage and more: https://github.com/fmind/mlops-python-package

What are some alternatives?

When comparing superduperdb and mlops-python-package you can also consider the following projects:

ds2 - Easiest way to use AI models without coding (Web UI & API support)

recs-at-resonable-scale - Recommendations at "Reasonable Scale": joining dataOps with recSys through dbt, Merlin and Metaflow

best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!

Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]

nyc_traffic_flask - Flask App with leaflet.js that can perform NYC Traffic Prediction

Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials - A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai

scikit-learn-ts - Powerful machine learning library for Node.js – uses Python's scikit-learn under the hood.