Skip to main content

Full stack ML Observability with AryaXAI

Project description

AryaXAI: ML Observability for mission-critical ‘AI’

AryaXAI is a full-stack ML Observability platform that integrates with your MLOPs platform to Explain, Monitor, Audit and Improve your ML models.

AryaXAI has multiple components to address the complex observability required for mission-critical ‘AI’.

  1. ML Explainability: AryaXAI offers diverse explainability options like- Bactrace(Specialized for deep learning models), SHAPE, Decision View, Observations (New way to correlate expert functioning vs model functioning) and Similar Cases (reference as explanations).
  2. ML Monitoring: Monitor your models for drifts, performance & bias. The tool offers diverse options for drift (data/model) like - PSI, KL Divergence, Chi-square test,
  3. Synthetic ‘AI’: Deploy advanced synthetic ‘AI’ techniques like GPT-2 & GANs on your tabular data to generate high-quality synthetic datasets. Test the quality and privacy of these data sets using our Anonymity tests, column tests etc.
  4. ML Risk policies: Define advanced risk policies on your models.
  5. AutoML: AryaXAI also provides fully low-code and no-code options to build ML models on your data. For advanced users, it also provides more options to fine-tune it.

AryaXAI also acts as a common workflow and provides insights acceptable by all stakeholders - Data Science, IT, Risk, Operations and compliance teams, making the rollout and maintenance of AI/ML models seamless and clutter-free.

Quickstart:

Get started with AryaXAI with a few easy steps:

  1. Sign up and log in to your new AryaXAI account.
  2. After logging in, generate an Access Token for your user account.
  3. Set the environment variable XAI_ACCESS_TOKEN with the generated value.

Once you've completed these steps, you're all set! Now, you can easily log in and start using the AryaXAI SDK:

  1. Log in by importing the "xai" object instance from the "arya_xai" package.
  2. Call the "login" method. This method automatically takes the access token value from the "XAI_ACCESS_TOKEN" environment variable and stores the JWT in the object instance. This means that all your future SDK operations will be authorized automatically, making it simple and hassle-free!
from aryaxai import xai as aryaxai

## login() function authenticates user using token that can be generated in app.aryaxai.com/sdk


aryaxai.login()


Enter your Arya XAI Access Token: ··········
Authenticated successfully.

Cookbook:

In this section, you can review the examples of implementation of AryaXAI-SDK.

  1. Full features overview of AryaXAI
  2. Using AryaXAI in Loan Underwriting (Coming Soon)

Contribution guidelines:

At AryaXAI, we're passionate about open source and value community contributions! Explore our contribution guide for insights into the development workflow and AryaXAI library internals. For bug reports or feature requests, head to GitHub Issues or reach out to us at support@aryaxai.com.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

aryaxai-0.0.74.tar.gz (40.2 kB view details)

Uploaded Source

Built Distribution

aryaxai-0.0.74-py3-none-any.whl (42.7 kB view details)

Uploaded Python 3

File details

Details for the file aryaxai-0.0.74.tar.gz.

File metadata

  • Download URL: aryaxai-0.0.74.tar.gz
  • Upload date:
  • Size: 40.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.12

File hashes

Hashes for aryaxai-0.0.74.tar.gz
Algorithm Hash digest
SHA256 de54ba612f1ead54881970eae31eb11e7c5215f7f47fd4cb04d54cef312c39a6
MD5 79b9eb2cc74e0305c8daeab1a4bda683
BLAKE2b-256 19299f58abff9e2038d2fc3c5200b1cd465ad2b6e527423d1508419ffa9782bf

See more details on using hashes here.

File details

Details for the file aryaxai-0.0.74-py3-none-any.whl.

File metadata

  • Download URL: aryaxai-0.0.74-py3-none-any.whl
  • Upload date:
  • Size: 42.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.12

File hashes

Hashes for aryaxai-0.0.74-py3-none-any.whl
Algorithm Hash digest
SHA256 1002882678cf4056e97bc4f4790172806da5a35ef2c5fd2d24e5c394413149db
MD5 79dad8594da90fa0e002717ce981c62a
BLAKE2b-256 3edbd1f0abb7be4f18338996485308f442e30fa998c20baa38366a4a081a3e3b

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page