BreakPoint AI is a robust library designed to prevent unforeseen issues in AI model deployments before they reach production. This tool serves as a critical quality gate in the machine learning workflow, allowing users to effectively compare the outputs of different model versions against a predefined baseline.

Core Features

• Deterministic Policy Evaluation: Assess model outputs with clear decision-making statuses: ALLOW, WARN, and BLOCK, based on how they deviate from the expected output.
• Local Execution: Operate the evaluation process locally using saved artifacts, ensuring that no external dependencies interfere with results.
• Seamless Integration: Designed to fit easily into continuous integration (CI) workflows, facilitating early detection of regressions, including cost anomalies and personally identifiable information (PII) leaks.

How It Works

Using BreakPoint AI is straightforward. It compares a candidate output to a baseline, enabling quick identification of changes that could lead to production issues. For example:

echo '{"output": "hello world"}' > baseline.json
echo '{"output": "HELLO WORLD!"}' > candidate.json
breakpoint evaluate baseline.json candidate.json

The evaluation will yield a status, detailing any reasons for a BLOCK, such as a cost increase or the presence of sensitive information.

Modes of Operation

1. Lite Mode: This is the default, optimized for local, zero-config setup with built-in policies to manage cost, PII, and output drift.
2. Full Mode: For users requiring configurable policies, this mode allows them to customize their evaluation metrics and thresholds.

Example Scenarios

1. Cost Increases: Detect when a model change unexpectedly raises the cost of operations.
2. Output Structure Changes: Ensure that the format of output remains consistent with the baseline to avoid disrupting downstream processes.
3. PII Detection: Identify and block outputs containing sensitive personal information.

Command-Line Interface (CLI)

BreakPoint AI includes a robust CLI for evaluating JSON outputs:

breakpoint evaluate baseline.json candidate.json

It also supports JSON output for seamless integration with CI tools:

breakpoint evaluate baseline.json candidate.json --json

Integration with CI Systems

BreakPoint AI can be integrated directly into CI workflows, enhancing the quality assurance process by ensuring that any model changes that pose a risk are flagged before deployment. An example CI configuration is provided for easy setup.

Python API

Utilizing the library within a Python environment is also facilitated:

from breakpoint import evaluate

decision = evaluate(baseline_output="hello", candidate_output="hello there", metadata={"baseline_tokens": 100, "candidate_tokens": 140})
print(decision.status)
print(decision.reasons)

With BreakPoint AI, confidence in AI model deployments is significantly improved, ensuring that models meet defined quality thresholds and do not introduce unexpected behavior.