ML Observability for mission-critical 'AI' is complex!

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ML Monitoring needs more than simple dashboards

While ML observability monitors 'models' in production, general purposes tools only focus on surface-level issues, lacking the depth required for mission-critical use cases. Issues like too many generic alerts and lack of clear visibility into root cause issues are critical.

ML monitoring vs ML observability. Illustration of monitoring ml models in production

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Tough to build an acceptable  framework between data science and user teams

Today, data science and user(business/product) teams spend many hours discussing the details of the training data, model target, performance and associated AI usage risks. It requires both teams to work exhaustively in translating technical metrics to map with business metrics & vice versa.

Any uncertainty in user minds leads to a very long testing time, and delays in production rollouts, eventually shutting off the project.

Building acceptable machine learning model

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Model Auditing is methodological

Auditing ML models helps to identify gaps and ensure adherence to necessary guidelines! But gathering the artefacts, defining the scope and running the cadences during pre-prod and prod is chaotic! Capturing case-wise audit is also critical for traceability.

Executing such repeated audits during pre-prod, prod and every time during a model update will be resource intensive.

ML and AI audit

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Providing the right and quick explanations/ evidence

With stringent regulations & societal impact of these use cases, builders (data science team) need to be ready to explain how the model arrived at a decision and provide any additional evidence supporting model functioning. These explanations define the acceptance of the solution by all stakeholders.

Wrong explanations or insufficient evidence can lead to mistrust and failure of the system.

Challenges with ML and AI explainability

AryaXAI - Full stack ML Observability

It offers multiple components organisations require more than simple ML monitoring tools

AryaXAI ML observability components - AI explainability, ML model monitoring, Ml and AI audit, AI governance and AI risk control

Explainable AI

Understand 'Why' & 'How' your model worked on that prediction

Accurate Explanations

Provide quick and accurate explanations for all stakeholders.

Similar Cases

Provide references as explanations for a comparative study

ML Monitoring

Always stay updated on your model performance in production.

Root cause analysis

Diven into the root causes behind Data and Model drift for faster resolution

Bias Monitoring

Tackle unwanted bias and improve the model to deliver better outcomes

ML Audit

Ensure regulatory compliance, prevent unwanted changes and create audit trails

Meet governance and control objectives

Ensured adherence to complicated and security

Templatize and automate

Use the artefacts to create cadences

Policy Controls

Enforce policies on 'AI' for mission-critical functions

Easy to use GUI

Customizable Policy controls

Grain regulatory relevance

Add/edit/modify policies on drop and model outcome

Integrates with
your ML Stack

H2O.ai

XGboost

Pytorch

AWS sagemaker

databricks

MLflow

jupyter

Google ML Cloud

Sci-Kit Learn

Colab

weights and biases

Tensorflow

Keras

ONNX

Azure ML

datarobot

AWS sagemaker

Pytorch

XGboost

H2O.ai

jupyter

MLflow

databricks

Colab

Sci-Kit Learn

Google ML Cloud

weights and biases

Tensorflow

Keras

datarobot

Azure ML

ONNX

AWS sagemaker

databricks

MLflow

jupyter

ONNX

Azure ML

datarobot

H2O.ai

XGboost

Pytorch

weights and biases

Tensorflow

Keras

Google ML Cloud

Sci-Kit Learn

Colab

datarobot

H2O.ai

Colab

ONNX

Pytorch

Keras

jupyter

databricks

AWS sagemaker

XGboost

MLflow

Azure ML

weights and biases

Tensorflow

Sci-Kit Learn

Google ML Cloud

Enterprise ready form day one!

Full stack ML Observability

It offers all key observability components in one place. Allows easy participation and sharing of information across stages and stakeholders.

Get Started in few mins

Using our APIs & SDKs, it is quite easy to get started with AryaXAI. With an easy-to-use GUI, users can go live in a jiff. DIY rocks!

Troubleshoot quickly and precisely

With state-of-the-art ML monitoring tools, you can precisely identify the issues in your models and get insights on resolutions.

Highly scalable in your preferred environment

It is highly scalable and flexible and can be scaled to millions of predictions in the preferred environment.

Operations Across Industries

ML Observability is critical to various use cases and various stakeholders.

Banking

Use case: Credit Underwriting for secure/unsecured loans

Insurance

Use Case: Life & Health Insurance Underwriting

Financial Services

Use Case: Identifying fraud/suspicious transactions

General

Use Case: Product recomendation

Manufacturing

Use Case: Failure prediction in continuous manufacturing

Autonomous Cars

Use Case: Autonomous Cars on road

Resources

Unlock more resources on AI Governance

Access the resource repository that includes blogs, research papers, white papers and events related to the latest developments in AI Governance, AI audit, ML monitoring, Explainable AI and much more.

Deep dive into Explainable AI: Current methods and challenges

As organizations scale their AI and ML efforts, they are now reaching an impasse - explaining and justifying the decisions by AI models.

Let's talk.

Schedule a demo on how AryaXAI can deliver AI Governance, transparency and acceptance of AI solutions to scale with confidence.

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