API-based deployment in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is API-based deployment in machine learning?

API-based deployment means making a machine learning model available through an Application Programming Interface (API) so other programs or users can send data and get predictions easily.

beginner

Why use an API for deploying machine learning models?

Using an API allows many users or applications to access the model remotely, making it easy to integrate predictions into websites, apps, or other software without sharing the model code.

intermediate

Name a common protocol used for API-based deployment.

HTTP (HyperText Transfer Protocol) is commonly used, often with REST (Representational State Transfer) style APIs to send requests and receive responses from the model server.

beginner

What is a typical input and output in an API-based ML model deployment?

Input is usually data in JSON format sent in a request, like text or numbers. Output is the model's prediction or result, also in JSON, sent back in the response.

intermediate

How do you ensure your API-based ML deployment is scalable?

You can use cloud services, load balancers, and container orchestration tools to handle many requests at once and keep the service fast and reliable.

What does API stand for in API-based deployment?

A Artificial Processing Input

B Automated Prediction Integration

C Application Programming Interface

D Advanced Programming Interaction

API stands for Application Programming Interface, which allows different software to communicate.

Which data format is commonly used to send input data to an ML model via API?

A XML

B JSON

C CSV

D YAML

JSON is widely used because it is easy to read and write for both humans and machines.

What is a key benefit of deploying ML models via API?

A The model runs faster on local machines

B It requires no internet connection

C Only one user can access the model

D Multiple applications can use the model remotely

APIs allow many applications to access the model remotely, enabling easy integration.

Which protocol is most often used for API communication in ML deployment?

A HTTP

B SMTP

C SSH

D FTP

HTTP is the standard protocol for web communication and APIs.

How can you handle many API requests to your ML model at the same time?

A Use cloud services and load balancing

B Ignore extra requests

C Use a single powerful computer only

D Limit the model to one request per hour

Cloud services and load balancers help distribute requests to keep the service responsive.

Explain how API-based deployment makes machine learning models accessible to other applications.

Think about how apps talk to each other over the internet.

Describe the steps to deploy a machine learning model using an API.

Consider what happens from model ready to user getting predictions.

Practice

1. What is the main purpose of API-based deployment in AI?

easy

A. To train AI models faster on local machines

B. To share AI models as easy-to-use services over the internet

C. To store large datasets securely

D. To visualize AI model results on graphs

Solution

1. Step 1: Understand API-based deployment

   API-based deployment allows AI models to be accessed remotely as services.

2. Step 2: Identify the main purpose

   This means apps can get predictions without running the model themselves, making sharing easy.

3. Final Answer:

   To share AI models as easy-to-use services over the internet -> Option B

4. Quick Check:

   API deployment = share models online [OK]

Hint: API deployment means sharing models as services online [OK]

Common Mistakes:

• Confusing deployment with training
• Thinking API stores data
• Assuming API is for visualization only

2. Which Python library is commonly used to create a simple API server for deploying AI models?

easy

A. NumPy

B. Matplotlib

C. Flask

D. Pandas

Solution

1. Step 1: Recall common Python libraries

   NumPy is for math, Pandas for data, Matplotlib for plots, Flask for web servers.

2. Step 2: Identify the API server library

   Flask is a lightweight web framework used to create APIs easily.

3. Final Answer:

   Flask -> Option C

4. Quick Check:

   Flask = simple API server [OK]

Hint: Flask is the go-to for simple Python APIs [OK]

Common Mistakes:

• Choosing data libraries instead of web frameworks
• Confusing Flask with data processing tools
• Thinking Matplotlib creates APIs

3. Given this Flask API code snippet, what will be the output when sending a POST request with JSON {"input": 5}?

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    x = data['input']
    result = x * 2
    return jsonify({'output': result})

if __name__ == '__main__':
    app.run()

medium

A. {"output": 10}

B. {"output": 5}

C. {"output": 25}

D. Error: Missing input key

Solution

1. Step 1: Understand the input and processing

   The API receives JSON with key 'input' and value 5, then multiplies it by 2.

2. Step 2: Calculate the output

   5 * 2 = 10, so the output JSON will have 'output': 10.

3. Final Answer:

   {"output": 10} -> Option A

4. Quick Check:

   Input 5 doubled = 10 [OK]

Hint: Multiply input by 2 as per code logic [OK]

Common Mistakes:

• Confusing input and output values
• Assuming output equals input
• Missing JSON key causes error

4. Identify the error in this Flask API code for deploying a model:

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json()
    x = data['input']
    result = x + 1
    return jsonify({'output': result})

if __name__ == '__main__':
    app.run()

medium

A. Incorrect HTTP method, should be GET

B. Missing route decorator

C. Returning plain string instead of JSON

D. Using request.json() instead of request.get_json()

Solution

1. Step 1: Check how JSON is accessed in Flask

   Flask's request object uses get_json() method, not json() function.

2. Step 2: Identify the error

   Using request.json() will cause an AttributeError; correct is request.get_json().

3. Final Answer:

   Using request.json() instead of request.get_json() -> Option D

4. Quick Check:

   Use get_json() to parse JSON [OK]

Hint: Use request.get_json(), not request.json() [OK]

Common Mistakes:

• Confusing request.json with get_json()
• Changing HTTP method unnecessarily
• Forgetting route decorator

5. You want to deploy a machine learning model via an API that predicts house prices. The model expects a JSON with features like 'size' and 'bedrooms'. Which approach best ensures your API handles missing features gracefully?

hard

A. Fill missing features with default values before prediction

B. Return an error if any feature is missing without processing

C. Ignore missing features and predict with available data only

D. Restart the server to reset missing data

Solution

1. Step 1: Understand missing feature handling

   Missing features can cause prediction errors if not handled properly.

2. Step 2: Choose a robust approach

   Filling missing features with default or average values allows prediction to continue safely.

3. Final Answer:

   Fill missing features with default values before prediction -> Option A

4. Quick Check:

   Use defaults for missing inputs [OK]

Hint: Use default values to handle missing features [OK]

Common Mistakes:

• Stopping API on missing data
• Ignoring missing features causing errors
• Restarting server unrelated to missing data