mlserve-sdk 0.2.3

Python SDK for access to MLServe.com services

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🧠 MLServe.com Python SDK

Official Python SDK for interacting with the MLServe.com API — a cloud platform for serving, monitoring, and collaborating on machine learning models.

This SDK provides a simple and secure interface to manage your models, users, datasets, and experiments — directly from Python or integrated applications.

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🚀 Installation

Install via pip:

pip install mlserve-sdk

Or from source:

git clone https://github.com/nikosga/mlserve-sdk
cd mlserve-sdk
pip install -e .

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⚙️ Setup & Authentication

The MLServe.com SDK requires an API token for authenticated requests.

You can:

• Obtain a token after logging in with your email and password, or
• Use the Google OAuth login flow (for SDK integrations).

Example: Login and set token

from mlserve import MLServeClient

client = MLServeClient()

# Login using your credentials
response = client.login(email="[e-mail]", password="YourPassword123")

# Store your token automatically
print(response)
# → {"access_token": "...", "token_type": "bearer"}

You can also set your token manually:

client.set_token("your-jwt-token")

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🧑‍💻 User Management

🔹 Register a new account

client.register(
    user_name="Alice Example",
    email="[e-mail]",
    password="SecurePass123!"
)

After registration, MLServe.com will send you a verification email. Once verified, you can log in using your credentials.

🔹 Request a password reset

client.request_password_reset(
    email="[e-mail]",
    new_password="MyNewPassword123!"
)

You’ll receive an email with a link to confirm your password change.

🔹 Login

response = client.login(
    email="[e-mail]",
    password="MyNewPassword123!"
)
print(response["access_token"])

🔹 Logout

client.logout()

🔹 Check token validity

profile = client.check_token()
print(profile["user_email"])

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👥 Team Management

🔹 Invite a new team member

client.invite_user("[e-mail]")

The invitee will receive a verification link to join your organization.

🔹 List all team members

team = client.list_team()
for member in team:
    print(member["user_name"], "-", member["role"])

🔹 Update a team member’s role

client.update_user_role(user_id=42, role="admin")

🔹 Remove a team member

client.remove_team_member(user_id=42)

This will disable their access (soft delete).

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🧠 Model Serving & Deployment

🔹 Deploy a model

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()
# Train your model here

response = client.deploy_model(
    model=model,
    name="my_model",
    version="v1",
    features=["feature1", "feature2"],
    background_df=df.sample(100)
)
print(response)

🔹 Make predictions

data = {"inputs": [{"feature1": 1.2, "feature2": 3.4}]}

predictions = client.predict(
    name="my_model",
    version="v1",
    data=data
)

print(predictions)

🔹 Weighted predictions across versions (A/B testing)

weighted_preds = client.predict_weighted(
    name="my_model",
    data=data
)

🔹 Configure A/B test weights

client.configure_abtest("my_model", weights={"v1": 0.7, "v2": 0.3})

🔹 List deployed models

models = client.list_models()
print(models)

🔹 Get latest model version

latest = client.get_latest_version("my_model")
print(latest)

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📊 Model Monitoring & Performance Tracking

MLServe.com makes it easy to monitor deployed models in production, track performance over time, and detect data quality issues — all through the SDK.

🔹 Retrieve recent online metrics

Get recent model metrics (e.g., accuracy, rewards) aggregated over a time window.

metrics = client.get_online_metrics(
    name="my_model",
    version="v2",
    window_hours=168,  # past 7 days
    as_dataframe=True
)
print(metrics)

Returns a single-row pandas DataFrame (if as_dataframe=True) or a dictionary with unpacked metrics.

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🔹 Track model evolution across versions

Compare metrics and deltas between model versions to see performance improvements or regressions over time.

evolution = client.get_model_evolution(
    name="my_model",
    as_dataframe=True
)
evolution.head()

Returns a DataFrame with:

• Each row representing a model version
• Columns for metrics, deltas, and deployed_at timestamps

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🔹 Get hourly metrics for a specific version

Fetch fine-grained endpoint performance data like requests, predictions, latency percentiles and throughput for a given model version.

hourly = client.get_metrics(
    name="my_model",
    version="v2",
    hours=48,
    as_dataframe=True
)
hourly.tail()

Useful for trend visualization and alerting pipelines.

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🔹 Check data quality (drift, missingness, outliers)

Monitor input data to ensure model stability and detect upstream data issues.

dq = client.get_data_quality(
    name="my_model",
    version="v2",
    hours=24,
    as_dataframe=True
)

Returns a dictionary of DataFrames for:

• missingness: feature-wise missing value ratios
• drift: distribution shifts vs. training data
• outliers: detected anomalies in input features

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🔐 Google OAuth Authentication (Optional)

auth_url = client.get_google_auth_url()
print("Visit this URL to authenticate:", auth_url)

After the user grants access, MLServe.com will handle the token exchange.

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⚡ SDK Reference

Method	Description
register(user_name, email, password)	Register a new account
login(email, password)	Login and obtain an access token
logout()	Logout the current session
check_token()	Verify token and return current user info
invite_user(email)	Invite a new user to your team
list_team()	List all users in the organization
update_user_role(user_id, role)	Change user role (admin/user)
remove_team_member(user_id)	Disable a user account
request_password_reset(email, new_password)	Send password reset email
deploy_model(...)	Deploy a trained ML model
predict(name, version, data)	Make predictions with a deployed model
predict_weighted(name, data)	Weighted predictions across versions
configure_abtest(name, weights)	Configure A/B test weights
list_models()	List all deployed models
get_latest_version(model_name)	Get the latest deployed version
google_login()	Login with Google OAuth
get_online_metrics(name, version)	Retrieve recent performance metrics
get_model_evolution(name)	Retrieve performance evolution
get_metrics(name, version, hours)	Fetch hourly metrics for a given model
get_data_quality(name, version).	Retrieve data quality metrics

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🧱 Example Workflow

from mlserve import MLServeClient

client = MLServeClient()

# Step 1: Register a new account
client.register("Bob", "[e-mail]", "Secure123!")

# Step 2: Verify via email
# (User clicks link in email)

# Step 3: Login
login_data = client.login("[e-mail]", "Secure123!")

# Step 4: Invite teammates
client.invite_user("[e-mail]")

# Step 5: Deploy a model
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
client.deploy_model(model=model, name="my_model", version="v1", features=["f1", "f2"], background_df=df.sample(100))

# Step 6: Make predictions
data = {"inputs": [{"f1": 1, "f2": 2}]}
preds = client.predict("my_model", "v1", data)
print(preds)

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Data & Privacy Disclaimer

• The MLServe.com SDK sends data to the MLServe.com API for predictions, registration, feedback, and other services.
• Data transmitted may include input features, user identifiers (emails, IDs), and feedback information.
• MLServe.com may store, process, and log any data sent via the SDK for analytics, model improvement, or operational purposes.
• Users are responsible for ensuring compliance with applicable privacy laws and regulations (e.g., GDPR, HIPAA).
• By using this SDK, you acknowledge that MLServe.com does not guarantee the privacy or confidentiality of transmitted data.
• All actions using the SDK are performed at your own risk, and MLServe.com is not liable for any misuse, data loss, or unintended exposure.
• It is recommended to anonymize sensitive data before sending it through the SDK.

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💬 Support

• 📧 Email: [e-mail]

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🧾 License

This SDK is licensed under the Apache Software License. © 2025 MLServe.com — All rights reserved.

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