KEY INSIGHT
Iteration velocity in local AI products depends on your ability to test new models against existing user history—without possessing that history. Synthetic benchmarking and staged rollouts become your primary iteration tools.
Cloud products iterate quickly because they have live user traffic to test against. Local products must simulate this environment through benchmarks, telemetry from willing users, and gradual rollout strategies that minimize blast radius when experiments fail.
```python
# iteration_manager.py
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Callable
from datetime import datetime, timedelta
import random
@dataclass
class ExperimentConfig:
experiment_id: str
name: str
description: str
model_a: str # Control
model_b: str # Variant
rollout_percentage: float # 0.0 - 1.0
metrics_to_track: List[str]
min_sample_size: int
duration_days: int
@dataclass
class ExperimentResult:
experiment_id: str
variant: str # 'a' or 'b'
sample_size: int
metric_name: str
mean_value: float
std_dev: float
confidence_interval: tuple
p_value: Optional[float] = None
recommendation: str = ""
@dataclass
class RolloutPlan:
rollout_id: str
model_id: str
target_cohort_percentage: float
current_percentage: float
canary_duration_days: int
automatic_promotion: bool
rollback_trigger_pct: float # Error rate threshold
class LocalModelIterationManager:
def __init__(self, benchmark_suite):
self.benchmark_suite = benchmark_suite
self.active_experiments: List[ExperimentConfig] = []
self.experiment_results: Dict[str, List[ExperimentResult]] = {}
self.rollout_plans: List[RolloutPlan] = []
def run_benchmark_comparison(self, model_a: str, model_b: str,
benchmark_tasks: List[str]) -> Dict:
"""
Compare two models using your benchmark suite.
Results inform which model to prioritize.
"""
results_a = self.benchmark_suite.run_suite(model_a, benchmark_tasks)
results_b = self.benchmark_suite.run_suite(model_b, benchmark_tasks)
comparison = {}
for task in benchmark_tasks:
comparison[task] = {
"model_a": results_a.get(task, {}),
"model_b": results_b.get(task, {}),
"delta": self._calculate_delta(
results_a.get(task, {}).get('score', 0),
results_b.get(task, {}).get('score', 0)
),
"recommendation": self._determine_recommendation(
results_a.get(task, {}).get('score', 0),
results_b.get(task, {}).get('score', 0)
)
}
return comparison
def _calculate_delta(self, score_a: float, score_b: float) -> float:
return ((score_b - score_a) / score_a * 100) if score_a > 0 else 0
def _determine_recommendation(self, score_a: float, score_b: float) -> str:
delta_pct = abs(self._calculate_delta(score_a, score_b))
if delta_pct < 2:
return "equivalent"
elif score_b > score_a:
return "model_b_preferred"
else:
return "model_a_preferred"
def create_experiment(self, name: str, model_a: str, model_b: str,
rollout_pct: float = 0.1,
metrics: Dict[str, List[str]] = None) -> ExperimentConfig:
"""
Set up A/B experiment for gradual rollout.
"""
experiment = ExperimentConfig(
experiment_id=f"exp_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
name=name,
description=f"Compare {model_a} vs {model_b}",
model_a=model_a,
model_b=model_b,
rollout_percentage=rollout_pct,
metrics_to_track=metrics.get('primary', ['rating', 'latency']),
min_sample_size=100,
duration_days=7
)
self.active_experiments.append(experiment)
return experiment
def assign_variant(self, user_id: str, experiment: ExperimentConfig) -> str:
"""
Deterministic assignment ensures same user gets same variant.
"""
assignment_seed = f"{experiment.experiment_id}:{user_id}"
hash_value = sum(ord(c) for c in assignment_seed)
variant = 'a' if hash_value % 100 < experiment.rollout_percentage * 100 else 'b'
return variant
def record_experiment_metric(self, experiment_id: str, variant: str,
metric_name: str, value: float) -> None:
"""Record metric value during experiment."""
result = ExperimentResult(
experiment_id=experiment_id,
variant=variant,
sample_size=1,
metric_name=metric_name,
mean_value=self._running_mean(value),
std_dev=0, # Simplified
confidence_interval=(0, 0)
)
if experiment_id not in self.experiment_results:
self.experiment_results[experiment_id] = []
self.experiment_results[experiment_id].append(result)
def _running_mean(self, new_value: float) -> float:
"""Calculate running mean of metric."""
# Placeholder—implement proper running statistics
return new_value
def analyze_experiment(self, experiment_id: str) -> Dict:
"""
Analyze experiment results when completed.
"""
results = self.experiment_results.get(experiment_id, [])
if not results:
return {"status": "no_data"}
# Aggregate by variant
variant_a = [r for r in results if r.variant == 'a']
variant_b = [r for r in results if r.variant == 'b']
return {
"experiment_id": experiment_id,
"variant_a_samples": len(variant_a),
"variant_b_samples": len(variant_b),
"variant_a_avg_rating": self._aggregate_metric(variant_a, 'rating'),
"variant_b_avg_rating": self._aggregate_metric(variant_b, 'rating'),
"recommendation": "promote_b" if self._aggregate_metric(variant_a, 'rating') < self._aggregate_metric(variant_b, 'rating') else "promote_a"
}
def _aggregate_metric(self, results: List[ExperimentResult],
metric_name: str) -> Optional[float]:
filtered = [r for r in results if r.metric_name == metric_name]
if not filtered:
return None
return sum(r.mean_value for r in filtered) / len(filtered)
def create_rollout_plan(self, model_id: str, canary_pct: float = 0.05,
auto_promote: bool = True) -> RolloutPlan:
"""Create gradual rollout plan for new model."""
plan = RolloutPlan(
rollout_id=f"rollout_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
model_id=model_id,
target_cohort_percentage=1.0,
current_percentage=0.0,
canary_duration_days=7,
automatic_promotion=auto_promote,
target_cohort_percentage=canary_pct
)
self.rollout_plans.append(plan)
return plan
def progress_rollout(self, rollout_id: str) -> RolloutPlan:
"""Progress rollout to next stage if metrics are healthy."""
plan = next((p for p in self.rollout_plans if p.rollout_id == rollout_id), None)
if not plan:
raise ValueError(f"Unknown rollout: {rollout_id}")
# Calculate next stage
stages = [0.01, 0.05, 0.25, 0.50, 1.0]
current_stage_idx = next(
(i for i, s in enumerate(stages) if plan.current_percentage <= s),
len(stages) - 1
)
if current_stage_idx < len(stages) - 1:
plan.current_percentage = stages[current_stage_idx + 1]
return plan
```
Benchmark quality determines iteration quality. Invest in benchmark suites that map to real user tasks. Generic benchmarks (MMLU, HumanEval) tell you about general capability—domain-specific benchmarks tell you about your users' actual needs.