AI That Delivers Value, Not Vanity.

Helping ambitious organisations discover, design, deploy, and integrate AI initiatives that solve real business challenges.

Book a Strategy Session

Integrating AI is both the greatest opportunity, and the toughest challenge

AI is on every boardroom agenda. But ambition alone doesn’t deliver results. Most organisations stall because they lack the foundations to turn AI experiments into real business impact.

$4.4T

Estimated global productivity gains from corporate AI use cases.

$4.4T

Estimated global productivity gains from corporate AI use cases.

$4.4T

Estimated global productivity gains from corporate AI use cases.

1.5X

Early adopters of AI are growing revenue 1.5x faster than peers.

1.5X

Early adopters of AI are growing revenue 1.5x faster than peers.

1.5X

Early adopters of AI are growing revenue 1.5x faster than peers.

Faster adoption of AI than desktop internet.

Of organizations consider themselves “mature” in AI, with systems fully integrated and delivering at scale.

Why Us

Making AI Work For Business

AI has the power to transform industries, but real impact requires more than technology alone. At Orbital Studio, we bring together the precision of data and engineering with the insight of business and industry expertise.

Our approach starts with understanding the problem - where AI can deliver measurable value, how it will integrate, and what success looks like in practice. From there, our team designs and delivers solutions that are secure, compliant, and built to scale.

The result is not just pilots or experiments, but AI that drives efficiency, fuels innovation, and strengthens long-term competitiveness.

About Us

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What We Do

Solutions Tailored For Impact

From strategy to scale, we deliver AI solutions that turn ambition into measurable business value.

Strategic Agentic Assessment

We identify the highest-value agentic opportunities, validate ROI, and build adoption roadmaps that align AI with business outcomes.

Analyzing ROI

Value Mapping

Data Readiness

Workflow Bottlenecks

Adoption Feasibility

Compliance and Risks

Analyzing ROI

Value Mapping

Data Readiness

Workflow Bottlenecks

Adoption Feasibility

Compliance and Risks

Agentic Ready Data Engineering

We build agent-ready data infrastructure, real-time pipelines, vector stores, and knowledge graphs, that enable AI agents to observe, learn, and act autonomously.

Data Lake

Ingesting 1.2M records/hr

ETL Pipeline

Last run 2 mins ago

Warehouse

Query latency 120ms

Data Lake

Ingesting 1.2M records/hr

ETL Pipeline

Last run 2 mins ago

Warehouse

Query latency 120ms

Model Fine-Tuning & Optimization

We fine-tune LLMs for precision and efficiency - training models on your data for better accuracy, deploying smaller fine-tuned models to cut costs, and optimizing performance for your specific use cases.

import mlflow, numpy as np, pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, precision_score, recall_score
from sklearn.inspection import permutation_importance
from mlflow.models.signature import infer_signature

def train_and_register(df, num, cat, target, model_name, experiment):
np.random.seed(42); mlflow.set_experiment(experiment)
assert set(num+cat+[target]).issubset(df.columns), "Schema mismatch"
X, y = df[num+cat], df[target]
pre = ColumnTransformer([
("num", Pipeline([("imp", SimpleImputer()), ("sc", StandardScaler())]), num),
("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")),
("oh", OneHotEncoder(handle_unknown="ignore", sparse_output=False))]), cat)
] )

base = LogisticRegression(max_iter=2000, class_weight="balanced", n_jobs=None)
pipe = Pipeline([("prep", pre), ("clf", base)])
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
search = RandomizedSearchCV(
pipe,
{"clf__C": np.logspace(-3, 2, 30), "clf__penalty": ["l2"], "clf__solver": ["lbfgs"]},
n_iter=12, scoring="roc_auc", cv=skf, n_jobs=-1, refit=True, verbose=0
)

with mlflow.start_run(tags={"stage": "train"}):
search.fit(Xtr, ytr); best = search.best_estimator_
proba = best.predict_proba(Xte)[:, 1]
thresholds = np.clip(np.quantile(proba, [0.3,0.4,0.5,0.6,0.7]), 0, 1)
scores = {t: f1_score(yte, (proba>t).astype(int)) for t in thresholds}
thr = max(scores, key=scores.get); yhat = (proba>thr).astype(int)
metrics = {
"auc": roc_auc_score(yte, proba),
"pr_auc": average_precision_score(yte, proba),
"f1": f1_score(yte, yhat),
"precision": precision_score(yte, yhat),
"recall": recall_score(yte, yhat),
"threshold": float(thr)
}

for k,v in metrics.items(): mlflow.log_metric(k, float(v))
mlflow.log_params(search.best_params_); mlflow.log_dict(scores, "threshold_f1.json")
imp = permutation_importance(best, Xte, yte, n_repeats=5, random_state=42)
fi = pd.Series(imp.importances_mean, index=best.named_steps["prep"].get_feature_names_out()).nlargest(15).to_dict()
mlflow.log_dict({"top_features": fi}, "feature_importance.json")
sig = infer_signature(Xtr.sample(min(200, len(Xtr))), best.predict_proba(Xtr.sample(min(200, len(Xtr))))[:,1])
mlflow.sklearn.log_model(best, "model", signature=sig, registered_model_name=model_name)
import mlflow, numpy as np, pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, precision_score, recall_score
from sklearn.inspection import permutation_importance
from mlflow.models.signature import infer_signature

def train_and_register(df, num, cat, target, model_name, experiment):
np.random.seed(42); mlflow.set_experiment(experiment)
assert set(num+cat+[target]).issubset(df.columns), "Schema mismatch"
X, y = df[num+cat], df[target]
pre = ColumnTransformer([
("num", Pipeline([("imp", SimpleImputer()), ("sc", StandardScaler())]), num),
("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")),
("oh", OneHotEncoder(handle_unknown="ignore", sparse_output=False))]), cat)
] )

base = LogisticRegression(max_iter=2000, class_weight="balanced", n_jobs=None)
pipe = Pipeline([("prep", pre), ("clf", base)])
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
search = RandomizedSearchCV(
pipe,
{"clf__C": np.logspace(-3, 2, 30), "clf__penalty": ["l2"], "clf__solver": ["lbfgs"]},
n_iter=12, scoring="roc_auc", cv=skf, n_jobs=-1, refit=True, verbose=0
)

with mlflow.start_run(tags={"stage": "train"}):
search.fit(Xtr, ytr); best = search.best_estimator_
proba = best.predict_proba(Xte)[:, 1]
thresholds = np.clip(np.quantile(proba, [0.3,0.4,0.5,0.6,0.7]), 0, 1)
scores = {t: f1_score(yte, (proba>t).astype(int)) for t in thresholds}
thr = max(scores, key=scores.get); yhat = (proba>thr).astype(int)
metrics = {
"auc": roc_auc_score(yte, proba),
"pr_auc": average_precision_score(yte, proba),
"f1": f1_score(yte, yhat),
"precision": precision_score(yte, yhat),
"recall": recall_score(yte, yhat),
"threshold": float(thr)
}

for k,v in metrics.items(): mlflow.log_metric(k, float(v))
mlflow.log_params(search.best_params_); mlflow.log_dict(scores, "threshold_f1.json")
imp = permutation_importance(best, Xte, yte, n_repeats=5, random_state=42)
fi = pd.Series(imp.importances_mean, index=best.named_steps["prep"].get_feature_names_out()).nlargest(15).to_dict()
mlflow.log_dict({"top_features": fi}, "feature_importance.json")
sig = infer_signature(Xtr.sample(min(200, len(Xtr))), best.predict_proba(Xtr.sample(min(200, len(Xtr))))[:,1])
mlflow.sklearn.log_model(best, "model", signature=sig, registered_model_name=model_name)

import mlflow, numpy as np, pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, precision_score, recall_score
from sklearn.inspection import permutation_importance
from mlflow.models.signature import infer_signature

def train_and_register(df, num, cat, target, model_name, experiment):
np.random.seed(42); mlflow.set_experiment(experiment)
assert set(num+cat+[target]).issubset(df.columns), "Schema mismatch"
X, y = df[num+cat], df[target]
pre = ColumnTransformer([
("num", Pipeline([("imp", SimpleImputer()), ("sc", StandardScaler())]), num),
("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")),
("oh", OneHotEncoder(handle_unknown="ignore", sparse_output=False))]), cat)
] )

base = LogisticRegression(max_iter=2000, class_weight="balanced", n_jobs=None)
pipe = Pipeline([("prep", pre), ("clf", base)])
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
search = RandomizedSearchCV(
pipe,
{"clf__C": np.logspace(-3, 2, 30), "clf__penalty": ["l2"], "clf__solver": ["lbfgs"]},
n_iter=12, scoring="roc_auc", cv=skf, n_jobs=-1, refit=True, verbose=0
)

with mlflow.start_run(tags={"stage": "train"}):
search.fit(Xtr, ytr); best = search.best_estimator_
proba = best.predict_proba(Xte)[:, 1]
thresholds = np.clip(np.quantile(proba, [0.3,0.4,0.5,0.6,0.7]), 0, 1)
scores = {t: f1_score(yte, (proba>t).astype(int)) for t in thresholds}
thr = max(scores, key=scores.get); yhat = (proba>thr).astype(int)
metrics = {
"auc": roc_auc_score(yte, proba),
"pr_auc": average_precision_score(yte, proba),
"f1": f1_score(yte, yhat),
"precision": precision_score(yte, yhat),
"recall": recall_score(yte, yhat),
"threshold": float(thr)
}

for k,v in metrics.items(): mlflow.log_metric(k, float(v))
mlflow.log_params(search.best_params_); mlflow.log_dict(scores, "threshold_f1.json")
imp = permutation_importance(best, Xte, yte, n_repeats=5, random_state=42)
fi = pd.Series(imp.importances_mean, index=best.named_steps["prep"].get_feature_names_out()).nlargest(15).to_dict()
mlflow.log_dict({"top_features": fi}, "feature_importance.json")
sig = infer_signature(Xtr.sample(min(200, len(Xtr))), best.predict_proba(Xtr.sample(min(200, len(Xtr))))[:,1])
mlflow.sklearn.log_model(best, "model", signature=sig, registered_model_name=model_name)
import mlflow, numpy as np, pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, precision_score, recall_score
from sklearn.inspection import permutation_importance
from mlflow.models.signature import infer_signature

def train_and_register(df, num, cat, target, model_name, experiment):
np.random.seed(42); mlflow.set_experiment(experiment)
assert set(num+cat+[target]).issubset(df.columns), "Schema mismatch"
X, y = df[num+cat], df[target]
pre = ColumnTransformer([
("num", Pipeline([("imp", SimpleImputer()), ("sc", StandardScaler())]), num),
("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")),
("oh", OneHotEncoder(handle_unknown="ignore", sparse_output=False))]), cat)
] )

base = LogisticRegression(max_iter=2000, class_weight="balanced", n_jobs=None)
pipe = Pipeline([("prep", pre), ("clf", base)])
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
search = RandomizedSearchCV(
pipe,
{"clf__C": np.logspace(-3, 2, 30), "clf__penalty": ["l2"], "clf__solver": ["lbfgs"]},
n_iter=12, scoring="roc_auc", cv=skf, n_jobs=-1, refit=True, verbose=0
)

with mlflow.start_run(tags={"stage": "train"}):
search.fit(Xtr, ytr); best = search.best_estimator_
proba = best.predict_proba(Xte)[:, 1]
thresholds = np.clip(np.quantile(proba, [0.3,0.4,0.5,0.6,0.7]), 0, 1)
scores = {t: f1_score(yte, (proba>t).astype(int)) for t in thresholds}
thr = max(scores, key=scores.get); yhat = (proba>thr).astype(int)
metrics = {
"auc": roc_auc_score(yte, proba),
"pr_auc": average_precision_score(yte, proba),
"f1": f1_score(yte, yhat),
"precision": precision_score(yte, yhat),
"recall": recall_score(yte, yhat),
"threshold": float(thr)
}

for k,v in metrics.items(): mlflow.log_metric(k, float(v))
mlflow.log_params(search.best_params_); mlflow.log_dict(scores, "threshold_f1.json")
imp = permutation_importance(best, Xte, yte, n_repeats=5, random_state=42)
fi = pd.Series(imp.importances_mean, index=best.named_steps["prep"].get_feature_names_out()).nlargest(15).to_dict()
mlflow.log_dict({"top_features": fi}, "feature_importance.json")
sig = infer_signature(Xtr.sample(min(200, len(Xtr))), best.predict_proba(Xtr.sample(min(200, len(Xtr))))[:,1])
mlflow.sklearn.log_model(best, "model", signature=sig, registered_model_name=model_name)

AI Agents Deployment

We design and deploy custom AI agents and agentic workflows that integrate with your data and tools, automate complex tasks, make intelligent decisions, and deliver measurable business impact.

Our Expertise

Combining industry insight with technical depth to deliver AI solutions that work in the real world.

Get in Touch

Strategic Agentic Assessment

AI Capability Enhancement

AI Strategy & Transformation

Rapid Prototyping & Validation

AI Capability Enhancement

AI Strategy & Transformation

Rapid Prototyping & Validation

AI Capability Enhancement

AI Strategy & Transformation

Rapid Prototyping & Validation

Agentic Ready Data Engineering

Real-Time Data Pipelines

Vector Stores & Knowledge Graphs

Agent-Native Infrastructure

Real-Time Data Pipelines

Vector Stores & Knowledge Graphs

Agent-Native Infrastructure

Real-Time Data Pipelines

Vector Stores & Knowledge Graphs

Agent-Native Infrastructure

Model Fine-Tuning & Optimisation

Custom Model Development

Efficient Model Optimisation

Performance Enhancement & Validation

MLOps for Fine-Tuned Models

Custom Model Development

Efficient Model Optimisation

Performance Enhancement & Validation

MLOps for Fine-Tuned Models

Custom Model Development

Efficient Model Optimisation

Performance Enhancement & Validation

MLOps for Fine-Tuned Models

AI Agents Deployment

Autonomous Agents & Workflow Orchestration

Enterprise Agent Integration and MCPs

Impact-Driven Optimisation

Autonomous Agents & Workflow Orchestration

Enterprise Agent Integration and MCPs

Impact-Driven Optimisation

Autonomous Agents & Workflow Orchestration

Enterprise Agent Integration and MCPs

Impact-Driven Optimisation

Our Approach

Approach That Works

We connect business insight with technical excellence, guiding AI from idea to enterprise-ready solution.

Findings

High Impact / Quick Wins

Manual Data Processing
High time cost identified
Customer Service Queries
40% could be automated
Compliance Checks
Delayed by manual review
Reporting Bottlenecks
Reports delivered with 3-day lag
Revenue Leakage
Missed upsell signals detected

Findings

High Impact / Quick Wins

Manual Data Processing
High time cost identified
Customer Service Queries
40% could be automated
Compliance Checks
Delayed by manual review
Reporting Bottlenecks
Reports delivered with 3-day lag
Revenue Leakage
Missed upsell signals detected

Findings

High Impact / Quick Wins

Manual Data Processing
High time cost identified
Customer Service Queries
40% could be automated
Compliance Checks
Delayed by manual review
Reporting Bottlenecks
Reports delivered with 3-day lag
Revenue Leakage
Missed upsell signals detected

Diagnose & Prioritize

We identify the challenges and opportunities where AI can create the greatest impact and ROI.

Design & Prototype

We build lean prototypes to validate feasibility and de-risk investments before scaling.

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import mlflow, numpy as np, pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, precision_score, recall_score
from sklearn.inspection import permutation_importance
from mlflow.models.signature import infer_signature

def train_and_register(df, num, cat, target, model_name, experiment):
np.random.seed(42); mlflow.set_experiment(experiment)
assert set(num+cat+[target]).issubset(df.columns), "Schema mismatch"
X, y = df[num+cat], df[target]
pre = ColumnTransformer([
("num", Pipeline([("imp", SimpleImputer()), ("sc", StandardScaler())]), num),
("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")),
("oh", OneHotEncoder(handle_unknown="ignore", sparse_output=False))]), cat)
] )

base = LogisticRegression(max_iter=2000, class_weight="balanced", n_jobs=None)
pipe = Pipeline([("prep", pre), ("clf", base)])
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
search = RandomizedSearchCV(
pipe,
{"clf__C": np.logspace(-3, 2, 30), "clf__penalty": ["l2"], "clf__solver": ["lbfgs"]},
n_iter=12, scoring="roc_auc", cv=skf, n_jobs=-1, refit=True, verbose=0
)

with mlflow.start_run(tags={"stage": "train"}):
search.fit(Xtr, ytr); best = search.best_estimator_
proba = best.predict_proba(Xte)[:, 1]
thresholds = np.clip(np.quantile(proba, [0.3,0.4,0.5,0.6,0.7]), 0, 1)
scores = {t: f1_score(yte, (proba>t).astype(int)) for t in thresholds}
thr = max(scores, key=scores.get); yhat = (proba>thr).astype(int)
metrics = {
"auc": roc_auc_score(yte, proba),
"pr_auc": average_precision_score(yte, proba),
"f1": f1_score(yte, yhat),
"precision": precision_score(yte, yhat),
"recall": recall_score(yte, yhat),
"threshold": float(thr)
}

for k,v in metrics.items(): mlflow.log_metric(k, float(v))
mlflow.log_params(search.best_params_); mlflow.log_dict(scores, "threshold_f1.json")
imp = permutation_importance(best, Xte, yte, n_repeats=5, random_state=42)
fi = pd.Series(imp.importances_mean, index=best.named_steps["prep"].get_feature_names_out()).nlargest(15).to_dict()
mlflow.log_dict({"top_features": fi}, "feature_importance.json")
sig = infer_signature(Xtr.sample(min(200, len(Xtr))), best.predict_proba(Xtr.sample(min(200, len(Xtr))))[:,1])
mlflow.sklearn.log_model(best, "model", signature=sig, registered_model_name=model_name)
import mlflow, numpy as np, pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, precision_score, recall_score
from sklearn.inspection import permutation_importance
from mlflow.models.signature import infer_signature

def train_and_register(df, num, cat, target, model_name, experiment):
np.random.seed(42); mlflow.set_experiment(experiment)
assert set(num+cat+[target]).issubset(df.columns), "Schema mismatch"
X, y = df[num+cat], df[target]
pre = ColumnTransformer([
("num", Pipeline([("imp", SimpleImputer()), ("sc", StandardScaler())]), num),
("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")),
("oh", OneHotEncoder(handle_unknown="ignore", sparse_output=False))]), cat)
] )

base = LogisticRegression(max_iter=2000, class_weight="balanced", n_jobs=None)
pipe = Pipeline([("prep", pre), ("clf", base)])
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
search = RandomizedSearchCV(
pipe,
{"clf__C": np.logspace(-3, 2, 30), "clf__penalty": ["l2"], "clf__solver": ["lbfgs"]},
n_iter=12, scoring="roc_auc", cv=skf, n_jobs=-1, refit=True, verbose=0
)

with mlflow.start_run(tags={"stage": "train"}):
search.fit(Xtr, ytr); best = search.best_estimator_
proba = best.predict_proba(Xte)[:, 1]
thresholds = np.clip(np.quantile(proba, [0.3,0.4,0.5,0.6,0.7]), 0, 1)
scores = {t: f1_score(yte, (proba>t).astype(int)) for t in thresholds}
thr = max(scores, key=scores.get); yhat = (proba>thr).astype(int)
metrics = {
"auc": roc_auc_score(yte, proba),
"pr_auc": average_precision_score(yte, proba),
"f1": f1_score(yte, yhat),
"precision": precision_score(yte, yhat),
"recall": recall_score(yte, yhat),
"threshold": float(thr)
}

for k,v in metrics.items(): mlflow.log_metric(k, float(v))
mlflow.log_params(search.best_params_); mlflow.log_dict(scores, "threshold_f1.json")
imp = permutation_importance(best, Xte, yte, n_repeats=5, random_state=42)
fi = pd.Series(imp.importances_mean, index=best.named_steps["prep"].get_feature_names_out()).nlargest(15).to_dict()
mlflow.log_dict({"top_features": fi}, "feature_importance.json")
sig = infer_signature(Xtr.sample(min(200, len(Xtr))), best.predict_proba(Xtr.sample(min(200, len(Xtr))))[:,1])
mlflow.sklearn.log_model(best, "model", signature=sig, registered_model_name=model_name)

import mlflow, numpy as np, pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, precision_score, recall_score
from sklearn.inspection import permutation_importance
from mlflow.models.signature import infer_signature

def train_and_register(df, num, cat, target, model_name, experiment):
np.random.seed(42); mlflow.set_experiment(experiment)
assert set(num+cat+[target]).issubset(df.columns), "Schema mismatch"
X, y = df[num+cat], df[target]
pre = ColumnTransformer([
("num", Pipeline([("imp", SimpleImputer()), ("sc", StandardScaler())]), num),
("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")),
("oh", OneHotEncoder(handle_unknown="ignore", sparse_output=False))]), cat)
] )

base = LogisticRegression(max_iter=2000, class_weight="balanced", n_jobs=None)
pipe = Pipeline([("prep", pre), ("clf", base)])
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
search = RandomizedSearchCV(
pipe,
{"clf__C": np.logspace(-3, 2, 30), "clf__penalty": ["l2"], "clf__solver": ["lbfgs"]},
n_iter=12, scoring="roc_auc", cv=skf, n_jobs=-1, refit=True, verbose=0
)

with mlflow.start_run(tags={"stage": "train"}):
search.fit(Xtr, ytr); best = search.best_estimator_
proba = best.predict_proba(Xte)[:, 1]
thresholds = np.clip(np.quantile(proba, [0.3,0.4,0.5,0.6,0.7]), 0, 1)
scores = {t: f1_score(yte, (proba>t).astype(int)) for t in thresholds}
thr = max(scores, key=scores.get); yhat = (proba>thr).astype(int)
metrics = {
"auc": roc_auc_score(yte, proba),
"pr_auc": average_precision_score(yte, proba),
"f1": f1_score(yte, yhat),
"precision": precision_score(yte, yhat),
"recall": recall_score(yte, yhat),
"threshold": float(thr)
}

for k,v in metrics.items(): mlflow.log_metric(k, float(v))
mlflow.log_params(search.best_params_); mlflow.log_dict(scores, "threshold_f1.json")
imp = permutation_importance(best, Xte, yte, n_repeats=5, random_state=42)
fi = pd.Series(imp.importances_mean, index=best.named_steps["prep"].get_feature_names_out()).nlargest(15).to_dict()
mlflow.log_dict({"top_features": fi}, "feature_importance.json")
sig = infer_signature(Xtr.sample(min(200, len(Xtr))), best.predict_proba(Xtr.sample(min(200, len(Xtr))))[:,1])
mlflow.sklearn.log_model(best, "model", signature=sig, registered_model_name=model_name)
import mlflow, numpy as np, pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, precision_score, recall_score
from sklearn.inspection import permutation_importance
from mlflow.models.signature import infer_signature

def train_and_register(df, num, cat, target, model_name, experiment):
np.random.seed(42); mlflow.set_experiment(experiment)
assert set(num+cat+[target]).issubset(df.columns), "Schema mismatch"
X, y = df[num+cat], df[target]
pre = ColumnTransformer([
("num", Pipeline([("imp", SimpleImputer()), ("sc", StandardScaler())]), num),
("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")),
("oh", OneHotEncoder(handle_unknown="ignore", sparse_output=False))]), cat)
] )

base = LogisticRegression(max_iter=2000, class_weight="balanced", n_jobs=None)
pipe = Pipeline([("prep", pre), ("clf", base)])
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
search = RandomizedSearchCV(
pipe,
{"clf__C": np.logspace(-3, 2, 30), "clf__penalty": ["l2"], "clf__solver": ["lbfgs"]},
n_iter=12, scoring="roc_auc", cv=skf, n_jobs=-1, refit=True, verbose=0
)

with mlflow.start_run(tags={"stage": "train"}):
search.fit(Xtr, ytr); best = search.best_estimator_
proba = best.predict_proba(Xte)[:, 1]
thresholds = np.clip(np.quantile(proba, [0.3,0.4,0.5,0.6,0.7]), 0, 1)
scores = {t: f1_score(yte, (proba>t).astype(int)) for t in thresholds}
thr = max(scores, key=scores.get); yhat = (proba>thr).astype(int)
metrics = {
"auc": roc_auc_score(yte, proba),
"pr_auc": average_precision_score(yte, proba),
"f1": f1_score(yte, yhat),
"precision": precision_score(yte, yhat),
"recall": recall_score(yte, yhat),
"threshold": float(thr)
}

for k,v in metrics.items(): mlflow.log_metric(k, float(v))
mlflow.log_params(search.best_params_); mlflow.log_dict(scores, "threshold_f1.json")
imp = permutation_importance(best, Xte, yte, n_repeats=5, random_state=42)
fi = pd.Series(imp.importances_mean, index=best.named_steps["prep"].get_feature_names_out()).nlargest(15).to_dict()
mlflow.log_dict({"top_features": fi}, "feature_importance.json")
sig = infer_signature(Xtr.sample(min(200, len(Xtr))), best.predict_proba(Xtr.sample(min(200, len(Xtr))))[:,1])
mlflow.sklearn.log_model(best, "model", signature=sig, registered_model_name=model_name)

import mlflow, numpy as np, pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, precision_score, recall_score
from sklearn.inspection import permutation_importance
from mlflow.models.signature import infer_signature

def train_and_register(df, num, cat, target, model_name, experiment):
np.random.seed(42); mlflow.set_experiment(experiment)
assert set(num+cat+[target]).issubset(df.columns), "Schema mismatch"
X, y = df[num+cat], df[target]
pre = ColumnTransformer([
("num", Pipeline([("imp", SimpleImputer()), ("sc", StandardScaler())]), num),
("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")),
("oh", OneHotEncoder(handle_unknown="ignore", sparse_output=False))]), cat)
] )

base = LogisticRegression(max_iter=2000, class_weight="balanced", n_jobs=None)
pipe = Pipeline([("prep", pre), ("clf", base)])
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
search = RandomizedSearchCV(
pipe,
{"clf__C": np.logspace(-3, 2, 30), "clf__penalty": ["l2"], "clf__solver": ["lbfgs"]},
n_iter=12, scoring="roc_auc", cv=skf, n_jobs=-1, refit=True, verbose=0
)

with mlflow.start_run(tags={"stage": "train"}):
search.fit(Xtr, ytr); best = search.best_estimator_
proba = best.predict_proba(Xte)[:, 1]
thresholds = np.clip(np.quantile(proba, [0.3,0.4,0.5,0.6,0.7]), 0, 1)
scores = {t: f1_score(yte, (proba>t).astype(int)) for t in thresholds}
thr = max(scores, key=scores.get); yhat = (proba>thr).astype(int)
metrics = {
"auc": roc_auc_score(yte, proba),
"pr_auc": average_precision_score(yte, proba),
"f1": f1_score(yte, yhat),
"precision": precision_score(yte, yhat),
"recall": recall_score(yte, yhat),
"threshold": float(thr)
}

for k,v in metrics.items(): mlflow.log_metric(k, float(v))
mlflow.log_params(search.best_params_); mlflow.log_dict(scores, "threshold_f1.json")
imp = permutation_importance(best, Xte, yte, n_repeats=5, random_state=42)
fi = pd.Series(imp.importances_mean, index=best.named_steps["prep"].get_feature_names_out()).nlargest(15).to_dict()
mlflow.log_dict({"top_features": fi}, "feature_importance.json")
sig = infer_signature(Xtr.sample(min(200, len(Xtr))), best.predict_proba(Xtr.sample(min(200, len(Xtr))))[:,1])
mlflow.sklearn.log_model(best, "model", signature=sig, registered_model_name=model_name)
import mlflow, numpy as np, pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, precision_score, recall_score
from sklearn.inspection import permutation_importance
from mlflow.models.signature import infer_signature

def train_and_register(df, num, cat, target, model_name, experiment):
np.random.seed(42); mlflow.set_experiment(experiment)
assert set(num+cat+[target]).issubset(df.columns), "Schema mismatch"
X, y = df[num+cat], df[target]
pre = ColumnTransformer([
("num", Pipeline([("imp", SimpleImputer()), ("sc", StandardScaler())]), num),
("cat", Pipeline([("imp", SimpleImputer(strategy="most_frequent")),
("oh", OneHotEncoder(handle_unknown="ignore", sparse_output=False))]), cat)
] )

base = LogisticRegression(max_iter=2000, class_weight="balanced", n_jobs=None)
pipe = Pipeline([("prep", pre), ("clf", base)])
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
search = RandomizedSearchCV(
pipe,
{"clf__C": np.logspace(-3, 2, 30), "clf__penalty": ["l2"], "clf__solver": ["lbfgs"]},
n_iter=12, scoring="roc_auc", cv=skf, n_jobs=-1, refit=True, verbose=0
)

with mlflow.start_run(tags={"stage": "train"}):
search.fit(Xtr, ytr); best = search.best_estimator_
proba = best.predict_proba(Xte)[:, 1]
thresholds = np.clip(np.quantile(proba, [0.3,0.4,0.5,0.6,0.7]), 0, 1)
scores = {t: f1_score(yte, (proba>t).astype(int)) for t in thresholds}
thr = max(scores, key=scores.get); yhat = (proba>thr).astype(int)
metrics = {
"auc": roc_auc_score(yte, proba),
"pr_auc": average_precision_score(yte, proba),
"f1": f1_score(yte, yhat),
"precision": precision_score(yte, yhat),
"recall": recall_score(yte, yhat),
"threshold": float(thr)
}

for k,v in metrics.items(): mlflow.log_metric(k, float(v))
mlflow.log_params(search.best_params_); mlflow.log_dict(scores, "threshold_f1.json")
imp = permutation_importance(best, Xte, yte, n_repeats=5, random_state=42)
fi = pd.Series(imp.importances_mean, index=best.named_steps["prep"].get_feature_names_out()).nlargest(15).to_dict()
mlflow.log_dict({"top_features": fi}, "feature_importance.json")
sig = infer_signature(Xtr.sample(min(200, len(Xtr))), best.predict_proba(Xtr.sample(min(200, len(Xtr))))[:,1])
mlflow.sklearn.log_model(best, "model", signature=sig, registered_model_name=model_name)

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