AI Notes

Tag: API

  • Workflow of MLOps

    The workflow of MLOps is an iterative and cyclical process that encompasses the entire lifecycle of a machine learning model, from initial ideation to ongoing monitoring and maintenance in production. While specific implementations can vary, here’s a common and comprehensive workflow:

    Phase 1: Business Understanding & Problem Definition

    1. Business Goal Identification: Clearly define the business problem that machine learning can solve and the desired outcomes.
    2. ML Use Case Formulation: Translate the business problem into a specific machine learning task (e.g., classification, regression, recommendation).
    3. Success Metrics Definition: Establish clear and measurable metrics to evaluate the success of the ML model in achieving the business goals.
    4. Feasibility Assessment: Evaluate the technical feasibility, data availability, and potential impact of the ML solution.

    Phase 2: Data Engineering & Preparation

    1. Data Acquisition & Exploration: Gather relevant data from various sources and perform exploratory data analysis (EDA) to understand its characteristics, quality, and potential biases.
    2. Data Cleaning & Preprocessing: Handle missing values, outliers, inconsistencies, and perform transformations like scaling, encoding, and feature engineering to prepare the data for model training.
    3. Data Validation & Versioning: Implement mechanisms to validate data quality and track changes to the datasets used throughout the lifecycle.
    4. Feature Store (Optional but Recommended): Utilize a feature store to centralize the management, storage, and serving of features for training and inference.

    Phase 3: Model Development & Training

    1. Model Selection & Prototyping: Experiment with different ML algorithms and model architectures to find the most suitable approach for the defined task.
    2. Model Training: Train the selected model(s) on the prepared data, iterating on hyperparameters and training configurations.
    3. Experiment Tracking: Use tools (e.g., MLflow, Comet) to track parameters, metrics, artifacts, and code versions for each experiment to ensure reproducibility and comparison.
    4. Model Evaluation: Evaluate the trained models using appropriate metrics on validation and test datasets to assess their performance and generalization ability.
    5. Model Validation: Rigorously validate the model’s performance, fairness, and robustness before considering it for deployment.

    Phase 4: Model Deployment & Serving

    1. Deployment Strategy Selection: Choose a suitable deployment method based on factors like latency requirements, scalability needs, and infrastructure (e.g., , batch processing, edge deployment).
    2. Model Packaging & Containerization: Package the trained model and its dependencies (e.g., using Docker) for consistent deployment across different environments.
    3. Infrastructure Provisioning: Set up the necessary infrastructure for model serving (e.g., cloud instances, Kubernetes clusters).
    4. Model Deployment: Deploy the packaged model to the chosen serving infrastructure.
    5. API Integration (if applicable): Integrate the deployed model with downstream applications through APIs.
    6. Shadow Deployment/Canary Releases (Optional): Gradually roll out the new model by comparing its performance against the existing model in a production-like environment.

    Phase 5: Model Monitoring & Maintenance

    1. Performance Monitoring: Continuously track key performance metrics of the deployed model in production to detect degradation.
    2. Data Drift Monitoring: Monitor the distribution of incoming data to identify significant deviations from the training data, which can impact model performance.
    3. Concept Drift Monitoring: Detect changes in the relationship between input features and the target variable over time.
    4. Model Health Monitoring: Track the operational health of the serving infrastructure (e.g., latency, error rates, resource utilization).
    5. Alerting & Notifications: Set up alerts to notify the relevant teams when performance degradation, data drift, or other issues are detected.
    6. Logging & Auditing: Maintain comprehensive logs of model predictions, input data, and system events for debugging and compliance purposes.
    7. Model Retraining & Redeployment: Based on monitoring insights, trigger automated or manual retraining pipelines with new data or updated configurations. Redeploy the retrained model following the deployment process.
    8. Model Governance & Compliance: Implement policies and procedures to ensure responsible practices, address ethical concerns, and comply with relevant regulations.
    9. Feedback Loops: Establish mechanisms to collect feedback from users and stakeholders to inform model improvements and future iterations.

    Phase 6: Continuous Improvement & Evolution

    1. Model Refinement: Continuously analyze model performance and identify areas for improvement through feature engineering, hyperparameter tuning, or exploring new model architectures.
    2. Pipeline Optimization: Optimize the efficiency and reliability of the entire MLOps pipeline.
    3. Technology Evaluation: Stay updated with the latest MLOps tools and technologies and evaluate their potential benefits.
    4. Knowledge Sharing & Collaboration: Foster a culture of learning and collaboration across data science, engineering, and operations teams.

    Key Principles Underlying the MLOps Workflow:

    • : Automating as many steps as possible to improve speed, consistency, and reliability.
    • Reproducibility: Ensuring that all steps can be repeated consistently.
    • Scalability: Designing systems that can handle increasing data volumes and model complexity.
    • Reliability: Building robust and fault-tolerant ML systems.
    • Monitoring: Continuously tracking model performance and system health in production.
    • Collaboration: Fostering effective communication and teamwork across different roles.
    • Version Control: Tracking changes to code, data, and models.

    This workflow is not strictly linear but rather an iterative cycle. Insights gained from monitoring and evaluation in production often feed back into earlier stages, driving continuous improvement and evolution of the ML system. The specific steps and tools used will vary depending on the organization’s needs, infrastructure, and the complexity of the ML problem.

  • Using .h5 model directly for Retrieval-Augmented Generation

    Using a .h5 model directly for Retrieval-Augmented Generation () is not the typical or most efficient approach. Here’s why and how you would generally integrate a .h5 model into a RAG pipeline:

    Why Direct Use is Uncommon:

    • .h5 typically stores the weights and architecture of a trained neural network. These models are designed for tasks like classification, regression, or generating new content based on their learned patterns. They don’t inherently have the functionality for:
      • Information Retrieval: Searching and retrieving relevant documents or chunks of text from a knowledge base.
      • Embedding Generation (for retrieval): Converting text into numerical vectors that capture semantic meaning, which is crucial for similarity search in RAG.

    How a .h5 Model Fits into a RAG Pipeline (Indirectly):

    A .h5 model can play a role in the “Generation” part of the RAG pipeline, but the “Retrieval” part usually involves separate models and infrastructure. Here’s a breakdown:

    1. Retrieval:
      • This stage focuses on fetching relevant context from your knowledge base (e.g., documents, articles, web pages) based on the user’s query.
      • Embedding Models: Typically, you’ll use a separate pre-trained embedding model (like those from Sentence Transformers, Hugging Face Transformers, or OpenAI) to convert both the user query and the documents in your knowledge base into dense vector embeddings.
      • Vector : These embeddings are stored in a vector database (like Chroma, Pinecone, FAISS, Weaviate) that allows for efficient similarity search to find the most relevant context.
      • The .h5 model is generally not involved in this retrieval stage.
    2. Augmentation:
      • The retrieved context is then combined with the original user query. This is often done by formatting a prompt that includes both the query and the relevant information.
    3. Generation:
      • This is where a Large Language Model () comes in to generate the final answer based on the augmented prompt (query + context).
      • The .h5 model could potentially be this LLM, but it would need to be a generative model. If your .h5 model is a sequence-to-sequence model or a decoder-only transformer (like those used for text generation), you could load it and use it in this stage.
      • However, for RAG, it’s more common to use powerful, general-purpose LLMs accessible through APIs (like OpenAI’s GPT models, Google’s Gemini, or open-source models accessed via Hugging Face Transformers). These models often provide better generation capabilities for complex reasoning and question answering.

    Example of a RAG Pipeline using a .h5 Generative Model (Conceptual):

    Let’s imagine you have a .h5 model that is a trained sequence-to-sequence model for text generation:

    from flask import Flask, request, jsonify
import tensorflow as tf
import numpy as np
from sentence_transformers import SentenceTransformer
import faiss

app = Flask(__name__)

# 1. Load the generative .h5 model
try:
    generative_model = tf.keras.models.load_model('your_generative_model.h5')
    print("Generative model loaded successfully!")
except Exception as e:
    print(f"Error loading generative model: {e}")
    generative_model = None

# 2. Load the embedding model for retrieval
embedding_model = SentenceTransformer('all-mpnet-base-v2')

# 3. Load the knowledge base embeddings and index (assuming you have these pre-computed)
knowledge_base_embeddings = np.load('knowledge_base_embeddings.npy')
knowledge_base_texts = np.load('knowledge_base_texts.npy')
index = faiss.IndexFlatIP(knowledge_base_embeddings.shape[1])
index.add(knowledge_base_embeddings)

@app.route('/rag', methods=['POST'])
def rag():
    if generative_model is None:
        return jsonify({'error': 'Generative model not loaded'}), 500

    try:
        data = request.get_json()
        if not data or 'query' not in data:
            return jsonify({'error': 'Missing "query" in request'}), 400

        query = data['query']

        # 4. Retrieval: Embed the query and search the knowledge base
        query_embedding = embedding_model.encode([query])[0]
        D, I = index.search(np.array([query_embedding]), k=3) # Retrieve top 3 relevant chunks
        relevant_contexts = [knowledge_base_texts[i] for i in I[0]]

        # 5. Augmentation: Combine query and context (simple concatenation for example)
        prompt = f"Context: {', '.join(relevant_contexts)}\n\nQuestion: {query}\n\nAnswer:"

        # 6. Generation: Use the .h5 generative model to generate the answer
        # **You'll need to adapt this part based on your generative model's input/output format**
        input_sequence = tokenizer.encode(prompt, return_tensors='tf') # Example for a transformer-based model
        output_sequence = generative_model.generate(input_sequence, max_length=200) # Example generation
        answer = tokenizer.decode(output_sequence[0], skip_special_tokens=True)

        return jsonify({'answer': answer, 'context': relevant_contexts})

    except Exception as e:
        return jsonify({'error': str(e)}), 400

if __name__ == '__main__':
    # Assume you have a tokenizer if your generative model requires it
    # from transformers import AutoTokenizer
    # tokenizer = AutoTokenizer.from_pretrained("your_generative_model_name")
    app.run(debug=True, port=5000)

    Key Points:

    • Separate Models: You’ll likely need a separate model for embeddings (for retrieval) and your .h5 model would be used for generation (if it’s a suitable generative model).
    • Knowledge Base Preparation: You need to have your knowledge base processed, embedded, and stored in a vector database beforehand.
    • Generative Model Input/Output: The code for using the .h5 model for generation will heavily depend on its architecture and how it expects input and produces output. You might need tokenizers and specific generation functions.
    • Complexity: Building a full RAG system involves several components and careful orchestration.

    In summary, while you can technically use a .h5 model for the generation part of RAG if it’s a generative model, the retrieval part typically relies on separate embedding models and vector databases. You would build an that orchestrates these components.

  • Tensor

    PyTorch’s fundamental data structure is the Tensor. It’s the central object for numerical computation in PyTorch, analogous to NumPy’s ndarray but with added capabilities for GPU acceleration and automatic differentiation (crucial for deep learning).

    Here’s a breakdown of PyTorch’s data structure landscape, with the Tensor at the core:

    1. Tensors (torch.Tensor)

    • Core Building Block: The torch.Tensor is a multi-dimensional array containing elements of a single data type. It can represent scalars (0D), vectors (1D), matrices (2D), and higher-dimensional data.
    • NumPy-like Interface: PyTorch Tensors have a very similar to NumPy arrays, making it easy for those familiar with NumPy to transition. You can perform element-wise operations, slicing, indexing, reshaping, and mathematical functions.
    • GPU Acceleration: Tensors can be moved to and operated on GPUs, significantly accelerating computations for deep learning tasks.
    • Automatic Differentiation (autograd): PyTorch’s automatic differentiation engine tracks operations performed on Tensors. By setting requires_grad=True, you enable gradient computation for these Tensors, which is essential for backpropagation during neural network training.
    • Data Types (torch.dtype): Tensors can hold various numerical data types, including:
      • Floating-point: torch.float32 (default), torch.float64, torch.float16, torch.bfloat16
      • Integer: torch.int64 (default), torch.int32, torch.int16, torch.int8, torch.uint8
      • Boolean: torch.bool
    • Device (torch.device): Tensors reside on a specific device, either the CPU ('cpu') or a GPU ('cuda').

    2. NumPy Arrays (numpy.ndarray)

    • Interoperability: PyTorch has excellent interoperability with NumPy. You can easily convert between PyTorch Tensors and NumPy arrays using .numpy() and torch.from_numpy(). This allows you to leverage the vast ecosystem of NumPy and SciPy for data processing and scientific computing within your PyTorch workflows.

    3. Data Loading Utilities (torch.utils.data)

    PyTorch provides powerful utilities for efficient data loading and preprocessing, especially for training machine learning models:

    • torch.utils.data.Dataset: An abstract class representing a dataset. You need to create a custom subclass that:
      • Implements __len__(self) to return the size of the dataset.
      • Implements __getitem__(self, idx) to return the sample at the given index.
      • This allows you to work with various data sources (files, databases, etc.) in a consistent way.
    • torch.utils.data.DataLoader: An iterator that provides batches of data from a Dataset. It handles shuffling, batching, and parallel loading of data, making the training process more efficient. You can specify the batch size, whether to shuffle the data, and the number of worker processes for parallel loading.

    4. Specialized Data Structures (for specific tasks)

    While torch.Tensor is the foundation, PyTorch also offers or integrates with structures optimized for particular use cases:

    • Packed Sequences (torch.nn.utils.rnn.PackedSequence): Used for efficiently processing variable-length sequences in Recurrent Neural Networks (RNNs). It represents a batch of sequences that have been padded and then “packed” to avoid unnecessary computations on the padding elements.
    • Data Structures in Libraries: Libraries built on top of PyTorch (like torchvision for computer vision or torchaudio for audio) often have their own specialized data structures or conventions for handling specific data types (e.g., images, audio signals).

    In Summary:

    The core data structure in PyTorch is the torch.Tensor, which provides a flexible and powerful way to represent numerical data with support for GPU acceleration and automatic differentiation. PyTorch seamlessly integrates with NumPy arrays for broader data manipulation. For efficient data loading during training, the torch.utils.data module with Dataset and DataLoader is essential. Finally, specialized data structures exist for handling specific types of sequential data. Understanding these data structures is fundamental to building and training neural networks in PyTorch.

  • Describing Prediction Input and Output

    In the context of machine learning, particularly when discussing model deployment and serving, prediction input refers to the data you provide to a trained model to get a prediction, and prediction output is the result the model returns based on that input.

    Let’s break down these concepts in more detail:

    Prediction Input:

    • The “What”: This is the data you feed into your deployed machine learning model to get an answer or a forecast. The format and content of this input must align with what the model was trained on and expects.
    • Format: The input can take various forms depending on the type of model and how it’s deployed:
      • Structured Data (Tabular): Often provided as a row of data with values for each feature the model was trained on. This could be in formats like JSON, CSV, or a dictionary of feature names and values.
      • Image Data: Typically provided as an array representing pixel values, often encoded in formats like JPEG or PNG.
      • Text Data: Can be a string or a sequence of tokens, depending on how the model was trained (e.g., using word embeddings or token IDs).
      • Data: A sequence of data points ordered by time.
      • Audio Data: An array representing the sound wave.
      • Video Data: A sequence of image frames.
    • Content: The input data must contain the relevant features that the model learned to use during training. If your model was trained on features like “age,” “income,” and “location,” your prediction input must also include these features.
    • Preprocessing: Just like the training data, the prediction input often needs to undergo the same preprocessing steps before being fed to the model. This might include scaling, encoding categorical variables, handling missing values, or other transformations.

    Prediction Output:

    • The “Result”: This is what the trained machine learning model produces after processing the prediction input. The format and meaning of the output depend on the type of machine learning task the model was trained for.
    • Format: The output can also take various forms:
      • Classification: Typically a probability score for each class or a single predicted class label. For example, for a spam detection model, the output might be {'probability_spam': 0.95, 'predicted_class': 'spam'}.
      • Regression: A numerical value representing the predicted outcome. For example, a house price prediction model might output {'predicted_price': 550000}.
      • Object Detection: A list of bounding boxes with associated class labels and confidence scores indicating the detected objects in an image.
      • Natural Language Processing (NLP):
        • Text Generation: A string of generated text.
        • Sentiment Analysis: A score or label indicating the sentiment (e.g., positive, negative, neutral).
        • Translation: The translated text.
      • Recommendation Systems: A list of recommended items.
    • Interpretation: The raw output of a model might need further interpretation or post-processing to be useful. For example, converting probability scores into a final class prediction based on a threshold.

    Relationship between Input and Output:

    The trained machine learning model acts as a function that maps the prediction input to the prediction output based on the patterns it learned from the training data. The quality and accuracy of the prediction output heavily depend on:

    • The quality and relevance of the training data.
    • The appropriateness of the chosen model architecture.
    • The effectiveness of the training process.
    • The similarity of the prediction input to the data the model was trained on.
    • The correct preprocessing of the input data.

    In an MLOps context, managing prediction input and output involves:

    • Defining clear schemas: Specifying the expected format and data types for both input and output.
    • Validation: Ensuring that the input data conforms to the defined schema.
    • Serialization and Deserialization: Converting data between different formats (e.g., JSON for requests, NumPy arrays for model processing).
    • Monitoring: Tracking the characteristics of the input data and the distribution of the output predictions to detect potential issues like data drift or model degradation.
    • Logging: Recording prediction requests and responses for auditing and analysis.

    Understanding prediction input and output is fundamental for building, deploying, and using machine learning models effectively in real-world applications.

  • Deploying a PyTorch model on Vertex AI

    Deploying a PyTorch model on involves several steps. Here’s a breakdown:

    1. Prerequisites:

    • Trained Model: You have a trained PyTorch model (house_price_model.pth).
    • Preprocessor: You’ve saved the preprocessor (e.g., as a pickle file) used to transform your data.
    • Google Cloud Project: You have a Google Cloud Project.
    • Vertex Enabled: The Vertex AI API is enabled in your project.
    • Google Cloud Storage (GCS) Bucket: You have a GCS bucket to store your model artifacts and serving code.
    • Serving Container: A Docker container that serves your model.

    2. Steps

    Here’s a conceptual outline with code snippets using the Vertex AI SDK:

    2.1 Upload Model Artifacts

    First, upload your trained model (house_price_model.pth) and preprocessor to your GCS bucket.

    from google.cloud import storage
import os
import pickle

# Configuration
PROJECT_ID = "your-project-id"  # Replace with your GCP project ID
BUCKET_NAME = "your-bucket-name"  # Replace with your GCS bucket name
REGION = "us-central1"  # Or your desired region
MODEL_DIR = "house_price_model"  # Directory in GCS to store model artifacts

# Create a GCS client
storage_client = storage.Client(project=PROJECT_ID)
bucket = storage_client.bucket(BUCKET_NAME)

# Upload the model
model_blob = bucket.blob(os.path.join(MODEL_DIR, "house_price_model.pth"))
model_blob.upload_from_filename("house_price_model.pth")  # Local path to your model

# Upload the preprocessor
preprocessor_blob = bucket.blob(os.path.join(MODEL_DIR, "preprocessor.pkl"))
with open("preprocessor.pkl", "rb") as f:  # Local path to your preprocessor
    preprocessor_blob.upload_from_file(f)

print(f"Model and preprocessor uploaded to gs://{BUCKET_NAME}/{MODEL_DIR}/")

    2.2 Create a Serving Container

    Since you’re using PyTorch, you’ll need a custom serving container. This container will:

    • Have the necessary PyTorch dependencies.
    • Load your model and preprocessor.
    • Define a prediction function that:
      • Receives the input data.
      • Preprocesses the data using the loaded preprocessor.
      • Passes the preprocessed data to your PyTorch model.
      • Returns the prediction.

    Here’s a Dockerfile example:

    # Use a PyTorch base image
FROM pytorch/pytorch:1.10.0-cuda11.3-cudnn8-runtime

# Install other dependencies
RUN pip install scikit-learn

# Copy model artifacts and serving script
COPY model /model
WORKDIR /model

# Expose the serving port
EXPOSE 8080

# Command to start the serving server (e.g., using gunicorn)
CMD ["gunicorn", "--bind", "0.0.0.0:8080", "app:app", "--workers", "1", "--threads", "1"]

    Here’s an example app.py (Flask application) that serves your model:

    from flask import Flask, request, jsonify
import torch
import joblib  # For loading the preprocessor
import numpy as np
import json
import logging
from google.cloud import storage

app = Flask(__name__)
#GCS Configuration
PROJECT_ID = "your-project-id"  # Replace with your GCP project ID
BUCKET_NAME = "your-bucket-name"  # Replace with your GCS bucket name
MODEL_DIR = "house_price_model"

def download_from_gcs(bucket_name, source_blob_name, destination_file_name):
    """Downloads a blob from the bucket."""
    storage_client = storage.Client(project=PROJECT_ID)
    bucket = storage_client.bucket(bucket_name)
    blob = bucket.blob(source_blob_name)

    blob.download_to_filename(destination_file_name)

    print(f"Blob {source_blob_name} downloaded to {destination_file_name}.")


# Download model and preprocessor from GCS
download_from_gcs(BUCKET_NAME, f"{MODEL_DIR}/house_price_model.pth", "house_price_model.pth")
download_from_gcs(BUCKET_NAME, f"{MODEL_DIR}/preprocessor.pkl", "preprocessor.pkl")
# Load model and preprocessor
try:
    model = torch.load("house_price_model.pth")
    model.eval()  # Set the model to inference mode
    preprocessor = joblib.load("preprocessor.pkl")
    logging.info("Model and preprocessor loaded successfully")
except Exception as e:
    logging.error(f"Error loading model or preprocessor: {e}")
    raise

def preprocess_input(data):
    """Preprocesses the input data using the loaded preprocessor.

    Args:
        data: A JSON object containing the input data.

    Returns:
        A NumPy array of the preprocessed data.
    """
    try:
        # Convert the JSON data to a pandas DataFrame
        input_df = pd.DataFrame([data])

        # Preprocess the input DataFrame
        processed_data = preprocessor.transform(input_df)

        # Convert to numpy array
        return processed_data
    except Exception as e:
        logging.error(f"Error during preprocessing: {e}")
        raise

@app.route("/predict", methods=["POST"])
def predict():
    """Endpoint for making predictions."""
    if request.method == "POST":
        try:
            data = request.get_json(force=True)  # Get the JSON data from the request

            # Log the request data
            logging.info(f"Received data: {data}")
            # Preprocess the input data
            input_data = preprocess_input(data)

            # Convert the NumPy array to a PyTorch tensor
            input_tensor = torch.tensor(input_data, dtype=torch.float32)

            # Make the prediction
            with torch.no_grad():
                prediction = model(input_tensor)

            # Convert the prediction to a Python list
            output = prediction.numpy().tolist()
            logging.info(f"Prediction: {output}")
            return jsonify(output)
        except Exception as e:
            error_message = f"Error: {e}"
            logging.error(error_message)
            return jsonify({"error": error_message}), 500
    else:
        return "This endpoint only accepts POST requests", 405

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8080, debug=True)

    Build and push the container to Google Container Registry (GCR) or Artifact Registry:

    docker build -t gcr.io/your-project-id/house-price-prediction:v1 .  # Build the image
docker push gcr.io/your-project-id/house-price-prediction:v1  # Push the image

    2.3 Create a Vertex AI Model Resource

    from google.cloud import aiplatform

aiplatform.init(project=PROJECT_ID, location=REGION)

# GCR image URI
serving_container_image_uri = "gcr.io/your-project-id/house-price-prediction:v1"  # Replace

model = aiplatform.Model.upload(
    display_name="house-price-prediction-model",
    artifact_uri=f"gs://{BUCKET_NAME}/{MODEL_DIR}",  # GCS path to model artifacts
    serving_container_image_uri=serving_container_image_uri,
)

print(f"Model resource name: {model.resource_name}")

    2.4 Create a Vertex AI Endpoint and Deploy the Model

    endpoint = aiplatform.Endpoint.create(
    display_name="house-price-prediction-endpoint",
    location=REGION,
)

model_deployed = endpoint.deploy(
    model=model,
    traffic_split={"0": 100},
    deployed_model_display_name="house-price-prediction-deployed-model",
    machine_type="n1-standard-4",  # Or your desired machine type
)

print(f"Endpoint resource name: {endpoint.resource_name}")
print(f"Deployed model: {model_deployed.id}")

    3. Make Predictions

    Now you can send requests to your endpoint:

    import json

# Sample data for a single house prediction
sample_data = {
    "Size_LivingArea_SqFt": 2000,
    "Size_Lot_SqFt": 8000,
    "Size_TotalArea_SqFt": 2800,
    "Rooms_Total": 7,
    "Bedrooms": 3,
    "Bathrooms_Full": 2,
    "Bathrooms_Half": 1,
    "Basement_Area_SqFt": 800,
    "Basement_Finished": 1,
    "Garage_Cars": 2,
    "Fireplaces": 1,
    "Porch_Area_SqFt": 100,
    "Year_Built": 2000,
    "Year_Remodeled": 2010,
    "Condition_Overall": 7,
    "Quality_Overall": 7,
    "Building_Type": "House",
    "House_Style": "Ranch",
    "Foundation_Type": "Slab",
    "Roof_Material": "Composition Shingle",
    "Exterior_Material": "Brick",
    "Heating_Type": "Forced Air",
    "Cooling_Type": "Central AC",
    "Kitchen_Quality": "Good",
    "Bathroom_Quality": "Good",
    "Fireplace_Quality": "Average",
    "Basement_Quality": "Average",
    "Stories": 1,
    "Floor_Material": "Hardwood",
    "Neighborhood": "Bentonville Central",
    "Proximity_Schools_Miles": 0.5,
    "Proximity_Parks_Miles": 1.2,
    "Proximity_PublicTransport_Miles": 0.8,
    "Proximity_Shopping_Miles": 1.5,
    "Proximity_Hospitals_Miles": 2.0,
    "Safety_CrimeRate_Index": 65,
    "Environmental_NoiseLevel_dB": 45,
    "Environmental_AirQuality_Index": 35,
    "Flood_Zone": "No",
    "View": "None",
    "Time_of_Sale": "2024-08",
    "Interest_Rate": 6.2,
    "Inflation_Rate": 3.5,
    "Unemployment_Rate": 4.2,
    "Housing_Inventory": 0.05,
    "Economic_Growth_Rate": 2.5,
}


# Get the endpoint
endpoint = aiplatform.Endpoint(endpoint_name=endpoint.resource_name)

# Make the prediction
response = endpoint.predict(instances=[sample_data])
predictions = response.predictions

print(f"Prediction: {predictions}")

  • Call Vertex AI endpoint

    To call your Vertex endpoint using HTTP, you’ll need to construct a POST request with the correct authorization and data format. Here’s a breakdown and an example using curl:

    1. Prerequisites

    • Endpoint ID: You’ll need the ID of your endpoint. You can find this in the Google Cloud Console or by using the Vertex AI SDK (as shown in the previous response).
    • Google Cloud Credentials: You’ll need credentials to authorize the request. The easiest way to do this from your local machine is to have the Google Cloud SDK (gcloud) installed and configured.
    • Project ID and Region: You will need your Google Cloud Project ID and the region where you deployed the endpoint.

    2. Authorization

    Vertex AI requests require an authorization header with a valid access token. If you have the Google Cloud SDK installed, you can obtain an access token using the following command:

    gcloud auth print-access-token

    3. Construct the HTTP Request

    You’ll make a POST request to the Vertex AI API endpoint. The URL will look like this:

    https://{region}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{region}/endpoints/{endpoint_id}:predict
    • {project_id}: Your Google Cloud Project ID.
    • {region}: The region where your endpoint is deployed (e.g., “us-central1”).
    • {endpoint_id}: The ID of your Vertex AI endpoint.

    The request body should be a JSON object with an “instances” key. The value of “instances” is a list of data instances. In your case, each instance represents the features of a house for which you want to predict the price.

    4. Example using curl

    Here’s an example of how to call your endpoint using curl:

    ACCESS_TOKEN=$(gcloud auth print-access-token)
PROJECT_ID="your-project-id"  # Replace with your Project ID
REGION="us-central1"      # Replace with your Region
ENDPOINT_ID="your-endpoint-id"  # Replace with your Endpoint ID

# Sample data (same as in the Python SDK example)
DATA='{
    "instances": [
        {
            "Size_LivingArea_SqFt": 2000,
            "Size_Lot_SqFt": 8000,
            "Size_TotalArea_SqFt": 2800,
            "Rooms_Total": 7,
            "Bedrooms": 3,
            "Bathrooms_Full": 2,
            "Bathrooms_Half": 1,
            "Basement_Area_SqFt": 800,
            "Basement_Finished": 1,
            "Garage_Cars": 2,
            "Fireplaces": 1,
            "Porch_Area_SqFt": 100,
            "Year_Built": 2000,
            "Year_Remodeled": 2010,
            "Condition_Overall": 7,
            "Quality_Overall": 7,
            "Building_Type": "House",
            "House_Style": "Ranch",
            "Foundation_Type": "Slab",
            "Roof_Material": "Composition Shingle",
            "Exterior_Material": "Brick",
            "Heating_Type": "Forced Air",
            "Cooling_Type": "Central AC",
            "Kitchen_Quality": "Good",
            "Bathroom_Quality": "Good",
            "Fireplace_Quality": "Average",
            "Basement_Quality": "Average",
            "Stories": 1,
            "Floor_Material": "Hardwood",
            "Neighborhood": "Bentonville Central",
            "Proximity_Schools_Miles": 0.5,
            "Proximity_Parks_Miles": 1.2,
            "Proximity_PublicTransport_Miles": 0.8,
            "Proximity_Shopping_Miles": 1.5,
            "Proximity_Hospitals_Miles": 2.0,
            "Safety_CrimeRate_Index": 65,
            "Environmental_NoiseLevel_dB": 45,
            "Environmental_AirQuality_Index": 35,
            "Flood_Zone": "No",
            "View": "None",
            "Time_of_Sale": "2024-08",
            "Interest_Rate": 6.2,
            "Inflation_Rate": 3.5,
            "Unemployment_Rate": 4.2,
            "Housing_Inventory": 0.05,
            "Economic_Growth_Rate": 2.5
        }
    ]
}'

# Construct the URL
URL="https://${REGION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${REGION}/endpoints/${ENDPOINT_ID}:predict"

# Make the POST request
curl -X POST \
     -H "Authorization: Bearer ${ACCESS_TOKEN}" \
     -H "Content-Type: application/json" \
     -d "${DATA}" \
     "${URL}"

    Explanation:

    • ACCESS_TOKEN=$(gcloud auth print-access-token): Gets your current access token.
    • PROJECT_ID, REGION, ENDPOINT_ID: Replace these with your actual values.
    • DATA: A JSON string containing the input data. Crucially, it’s wrapped in an “instances” list.
    • URL: The Vertex AI API endpoint URL.
    • The curl command:
      • -X POST: Specifies the POST request method.
      • -H "Authorization: Bearer ${ACCESS_TOKEN}": Adds the authorization header.
      • -H "Content-Type: application/json": Sets the content type to JSON.
      • -d "${DATA}": Sends the JSON data in the request body.
      • ${URL}: The URL to send the request to.

    5. Response

    The response from the Vertex AI endpoint will be a JSON object with a “predictions” key. The value of “predictions” will be a list, where each element corresponds to the prediction for an instance in your input. In this case, you’ll get a list with a single element: the predicted house price.