Akash <> JZFS: Verifiable, Version Control Data Pipeline

[taoshengshi]

Introduction

There is a need to establish the end-to-end reasoning behind data driven decisions, and in particular reasoning incorporating data supplied by many parties.

Several trends make this need increasingly urgent.

First, the trend towards using public and open source data to substantiate decisions increases the utility of expressing these decisions transparently and verifiably.

Second, decisions are increasingly automated with IOT, smart contract and AI subsystems forming parts of the decision making pipeline. Explicitly recording this pipeline is necessary to substantiate trust in the end result.

Third, generative AI heightens the need to rigorously track the provenance of data to combat misinformation.

Fourth, decisions in critical areas such as environmental sustainability and AI ethics and safety rely on rapidly evolving research which carries an imperative to explicitly lay out methodologies so that they can be reproduced, challenged, improved, and rapidly applied.

Fifth, the introduction of more powerful and general zero knowledge systems increases the necessity of tracing decisions end-to-end so that proven claims relying on little revealed information can be put into context.

Version Control pipeline

The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most of the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering.

There are two categories of pipeline abstractions to be aware about:

The pipeline which is the most powerful object encapsulating all other pipelines.
Task-specific pipelines are available for audio, computer vision, natural language processing, and multimodal tasks.

Data Lineage is an interactive tool that facilitates a holistic view of how data flows through the JZFS and Akash.

With Data Lineage, you can:

• Easily find and discover datasets
  • Search to find datasets using Project, table, and column names
  • Click through JZFS UI to browse data
• Explore pipelines through a powerful interface
  • Expand or hide ancestors and descendants of datasets
  • View attributes of a group of tables at once
  • Visualize your pipeline through coloring (e.g. color out-of-date tables)
  • Drill into details about your data such as its schema, when it was last built, and the code that generated the data itself
• Collaborate with teammates
• Create pipeline snapshots to share with other users

Architecture

Data-driven decision making requires combining trusted information using arbitrary compute operations. Here, we introduce a protocol called Operad which allows end-to-end tracing of data provenance over computational pipelines. The protocol models pipelines as morphisms in symmetric monoidal categories (operad morphisms) anchored in content-addressed data types. This allows both reproducible and non-reproducible real-world processes to be modeled as wiring diagrams, in which elements of the process diagram relate to elements of data and provenance wiring diagrams with a corresponding structure. Together, data, transformations and provenance form a three layer system. This allows a rigorous examination of the flow of trust through complex and multi-party computational processes.

Data Layer

The data layer refers to the content itself, such as the input information provided by end users or API endpoints

Provenance Layer (JZFS)

The provenance layer validates the final outcome of the data based on community standards and provides a seal of authenticity

Transformation Layer (Akash)

The transformation layer shows how data is processed through code or an AI model

Use Cases

Collect data and prove where it came from

Effortlessly create forms and data streams with a provenance trail

Protect your data and code with a digital fingerprint

Everything you do in Operad.ai is content addressed through IPFS so you can detect the smallest changes

Use powerful AI models in a verifiable environment

Prove the origins of AI-generated content to attribute credit... or blame

Share on your terms, charge for every use or every API call

Keep your data and code private. Allow others to use it, and get paid whenever it is run or accessed

PS

GitData Labs

Embrace Data-Centric AI with GitData
https://gitdata.ai/

JZFS

An Git-like version control file system for data lineage & data collaboration.
https://github.com/GitDataAI/jiaozifs

[brewsterdrinkwater]

Thank you for opening up this discussion @taoshengshi .

Is there a direct ask from this discussion? Is this a flow that you have tested on the Akash Network yet?

[taoshengshi]

We have just opened source our version control file system, and here is a draft proposal that we are thinking it is very interesting to combine with Akash to build verifiable, version control data pipeline. Our direct ask is seeking feedback on whether there is enough value for Akash and then apply devgrants to build(did not test on Akash yet).