Skyrocketing AI Projects: Leveraging Cloud Platforms for Model Delivery#

1.1 What Is AI in the Cloud?#

Adopting AI in the cloud means using remote servers—hosted by providers such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP)—to train, run, and monitor machine learning models. This approach can drastically reduce up-front costs while providing near-limitless computational power.

AI in the cloud is more than just offloading computations; it’s also about leveraging managed services, scalable storage, and easily integrable data pipelines. These services remove many of the traditional barriers of on-premises AI, including physical hardware, large capital expenditures, maintenance, and specialized data-center requirements. With a cloud-based approach, even small companies can access enterprise-grade hardware and services that were once reserved for only the largest organizations.

1.2 Key Cloud Service Models for AI#

When you use AI in the cloud, you generally rely on one or more of three key service models:

Choosing the right model depends on factors such as control vs. ease of use, the complexity of your AI workflows, and the skill set of your team.

2.1 Data Collection and Preprocessing#

Data is the lifeblood of any AI project. Even the most sophisticated model architecture won’t produce valuable outputs if the underlying dataset is incorrect, incomplete, or not relevant. Key considerations early in the project lifecycle include:

• Data Sources: Databases, IoT sensors, third-party APIs, or user-generated data.
• Data Quality: Handling missing values or inconsistent data distributions.
• Data Cleaning: Removing duplicates, outliers, or biased data points.
• Feature Engineering: Transforming raw data into meaningful inputs for your model.

Modern cloud-based AI projects often rely on tools like AWS Glue, Azure Data Factory, or Google Cloud Dataflow to automate data ingestion, cleaning, and transformation at scale.

2.2 Model Architecture and Training#

Once data is prepped, the next step is designing, training, and validating a model that can learn from the dataset. Depending on your problem (e.g., image classification, natural language processing, time-series forecasting), you may use varied architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), or transformers.

Training these models can be computationally expensive, especially for deep learning. Cloud providers address this challenge by offering compute-optimized instances with GPUs and TPUs (Tensor Processing Units, in the case of Google). This on-demand approach ensures you only pay for compute while actively training, which can significantly reduce costs compared to purchasing on-site hardware.

2.3 Evaluation#

Before deploying a model, rigorous evaluation is essential. Common metrics include accuracy, precision, recall, F1-score, and ROC-AUC for classification, while mean squared error or mean absolute error might be used for regression. In the cloud, teams can run multiple training and validation experiments in parallel, using managed services to keep track of different runs and hyperparameter settings.

3.1 AWS#

AWS was a pioneer in cloud computing, offering robust AI services and extensive documentation. They have specialized solutions such as Amazon SageMaker for end-to-end machine learning workflow management. Key features of AWS for AI:

• Amazon SageMaker: Train and deploy ML models at scale.
• AWS Lambda: Serverless computing for lightweight AI functions.
• Elastic Inference: Attach GPU acceleration to certain EC2 instances when needed, reducing overhead costs.

3.2 Microsoft Azure#

Azure caters to businesses that often use Microsoft’s ecosystem of software and services. Azure Machine Learning (Azure ML) provides MLOps (machine learning operations) capabilities and straightforward integrations with GitHub for CI/CD. Notable features of Azure for AI:

• Azure Machine Learning: Enables you to build, train, deploy, and manage models in a collaborative setting.
• Azure Cognitive Services: Pre-built AI models for computer vision, speech, natural language, and decision-making tasks.
• Azure Kubernetes Service (AKS): Container orchestration and scaling for more complex AI solutions.

3.3 Google Cloud Platform (GCP)#

Google’s AI products stem from extensive research accomplishments and innovations like TensorFlow. GCP stands out in:

• AI Platform: Provides hosted notebooks, training, and model deployment.
• Vertex AI: A unified platform for ML workflow, including hyperparameter tuning and model monitoring.
• TPUs: High-performance custom hardware specialized for training and inference tasks in TensorFlow.

4.1 Introduction to Docker and Containers#

To turn local AI experiments into production-ready services, you must ensure the environment is consistent, from development to deployment. Docker simplifies this by packaging code, dependencies, and runtime configurations in containers. These containers can then run on any host with Docker installed—whether it’s your local machine or a cluster of cloud VMs.

Benefits of using containers for AI projects:

• Consistency between development and production.
• Rapid scaling and load balancing.
• Streamlined updates and rollbacks.

4.2 Setting Up a Dockerfile#

A Dockerfile is a script-like file containing instructions for assembling a Docker image. Here’s a basic example for a simple AI project using Python:

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# Use an official Python runtime as a parent image
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FROM python:3.9-slim
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# Set the working directory in the container
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WORKDIR /app
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# Copy your local code to the container
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COPY requirements.txt requirements.txt
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COPY . /app
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# Install dependencies
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RUN pip install --no-cache-dir -r requirements.txt
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# Expose port for the API server if you plan to run a web service
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EXPOSE 8080
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# Start the application
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CMD ["python", "app.py"]

In this example:

1. We start with a lightweight Python image.
2. The WORKDIR directive creates and switches to /app.
3. We copy the local files, then install dependencies from requirements.txt.
4. We open a port for web communication and execute the Python script.

4.3 Container Orchestration with Kubernetes#

When you have multiple containers and need high availability, Kubernetes helps manage container clusters. Kubernetes (often abbreviated as K8s) automates deployment, scaling, and management by grouping containers into logical units. It’s a powerful integration point for advanced workflows, including canary deployments, automatic rollbacks, and complicated multi-service AI systems.

5.1 Sample Python Project Structure#

Before deploying to the cloud, it’s wise to build and test your model locally. A common Pythonic project structure might look like this:

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my_ai_project/
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├── data/
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│   └── training_data.csv
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├── models/
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│   ├── model.py
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│   └── test_model.py
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├── notebooks/
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│   └── experimentation.ipynb
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├── requirements.txt
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├── app.py
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└── Dockerfile

• data/: Store training or validation datasets.
• models/: Python modules handling the model architecture, training, and testing.
• notebooks/: Iterative experimentation or exploratory data analysis.
• app.py: Main file to run the model or start a web service.
• Dockerfile: Container build instructions.

5.2 Building a Simple Machine Learning Model in TensorFlow#

Below is a simplified example of training a neural network on a dataset using TensorFlow:

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import tensorflow as tf
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from tensorflow.keras import layers, models
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import numpy as np
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# Sample data: random input features and random labels
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X_train = np.random.rand(1000, 20)  # 1000 samples, 20 features
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y_train = np.random.randint(2, size=(1000, 1))  # Binary classification
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# Define a simple neural network
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model = models.Sequential([
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    layers.Dense(64, activation='relu', input_shape=(20,)),
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    layers.Dense(32, activation='relu'),
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    layers.Dense(1, activation='sigmoid')
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])
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model.compile(optimizer='adam',
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              loss='binary_crossentropy',
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              metrics=['accuracy'])
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# Train the model
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model.fit(X_train, y_train, epochs=10, batch_size=32)
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# Create an inference function
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def predict(input_data):
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    return model.predict(input_data)

This trivial example demonstrates how easy it can be to prototype a neural network. In real scenarios, you would import and preprocess your dataset from storage, apply normalization or feature engineering, and iterate through hyperparameter tuning.

6.1 Using AWS EC2 for AI Deployments#

Amazon EC2 (Elastic Compute Cloud) offers scalable virtual machines where you can install your AI software. You can:

1. Launch an EC2 instance with GPU acceleration (e.g., g4dn.xlarge).
2. SSH into the instance, then install Docker or any necessary libraries.
3. Pull your container image from Amazon ECR (Elastic Container Registry) and run it.
4. Optionally attach an Elastic IP to ensure the instance retains a static public IP address.

Here is a simple Bash script to run the container once the instance is up:

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# Update package lists
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sudo apt-get update
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# Install Docker
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sudo apt-get install -y docker.io
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# Start Docker
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sudo systemctl start docker
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# Pull your Docker image from ECR
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docker pull <aws_account_id>.dkr.ecr.<region>.amazonaws.com/my_ai_project:latest
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# Run the container, exposing port 8080
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docker run -d -p 8080:8080 <aws_account_id>.dkr.ecr.<region>.amazonaws.com/my_ai_project:latest

6.2 AWS Elastic Beanstalk and AI Services#

Elastic Beanstalk simplifies application deployment, automatically handling capacity provisioning, load balancing, and health monitoring. Although commonly used for web applications, it can also host AI services. You upload your application (or container), specify environment variables, and let Elastic Beanstalk manage the underlying servers.

For advanced AI workloads with large models or streaming data, consider Amazon SageMaker. It helps train, tune, and deploy models in production with minimal overhead. SageMaker can also integrate with private Docker containers, giving you flexibility with custom frameworks.

6.3 AWS Lambda for Serverless AI Functions#

AWS Lambda offers a rapid way to deploy AI inference code in a serverless fashion. Serverless implies you don’t maintain or pay for dedicated servers—Lambda scales automatically based on requests. However, the environment is memory- and time-constrained, as Lambdas typically operate in short bursts.

You can package a lightweight AI model (or use a model stored in S3) that loads within the Lambda function. Example use cases include on-demand predictions for simple classification tasks, e.g., text sentiment analysis. Keep in mind that for large deep learning frameworks, cold-start times may become significant, so serverless might only be suitable for specific, optimized workflows or smaller models.

7.1 Azure ML: A Quick Overview#

Azure ML aims to streamline the ML lifecycle. You can start from data collection, run experiments, tune hyperparameters, track metrics, and finally deploy models:

1. Import your dataset to Azure Blob Storage or Data Lake Storage.
2. Choose a compute target, such as an Azure ML Compute Cluster, to train your model.
3. Track experiments and logs using Azure ML’s MLflow integration.
4. Package and deploy the model to an endpoint or container.

7.2 Deploying a Model with Azure Container Instances#

Azure Container Instances (ACI) is a straightforward way to run a Docker image in Azure. For a quick deployment pipeline:

1. Build and push your Docker image to Azure Container Registry (ACR).
2. Create a container group with ACI.
3. Expose or create a secure endpoint for the container.

Example Azure CLI snippet:

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# Login to Azure
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az login
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# Create a resource group
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az group create --name myResourceGroup --location eastus
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# Create a container registry
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az acr create --resource-group myResourceGroup --name myRegistryName --sku Basic
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# Build and push your Docker image
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az acr build --registry myRegistryName --image my_ai_project:latest .
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# Run the container instance, mapping port 80
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az container create \
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  --resource-group myResourceGroup \
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  --name myAIContainer \
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  --image myRegistryName.azurecr.io/my_ai_project:latest \
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  --cpu 2 --memory 4 \
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  --ports 80 \
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  --registry-login-server myRegistryName.azurecr.io \
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  --registry-username <your_acr_username> \
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  --registry-password <your_acr_password>

ACI handles small to medium-scale workloads efficiently. For larger-scale AI deployments, Azure Kubernetes Service (AKS) is more suitable.

7.3 Azure Kubernetes Service for Scaling AI#

AKS provides a fully managed Kubernetes environment in Azure. Developers can seamlessly scale their containerized AI solution while integrating with Azure’s suite of monitoring and security tools. You can:

• Use the Azure CLI or Azure Portal to create an AKS cluster.
• Pull your images from ACR into the cluster.
• Automate scaling based on CPU/Memory utilization or custom metrics.

Monitoring can be enhanced with Azure Monitor or custom tooling to track queries per second, latency, and resource usage metrics.

8.1 Google Cloud AI Platform Basics#

GCP provides the AI Platform (aka Vertex AI) that unifies data preparation, model training, and deployment. It’s well integrated with TensorFlow, though you can use PyTorch, scikit-learn, or other libraries:

• Vertex AI Training: Managed infrastructure for large-scale model training.
• Vertex AI Prediction: Serve predictions in a scalable, low-latency environment.
• Pipeline Orchestration: Automate multi-step processes like data ingestion, training, and evaluation.

8.2 Deploying with Google Kubernetes Engine (GKE)#

GKE is a managed Kubernetes service that lets you run containerized workloads. For AI deployments, you can:

1. Containerize your model server using a Dockerfile.
2. Push the container image to Google Container Registry (GCR) or Artifact Registry.
3. Create a Kubernetes Deployment and Service.
4. Optionally configure Ingress for external, load-balanced traffic.

Example YAML for deploying a simple container on GKE:

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apiVersion: apps/v1
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kind: Deployment
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metadata:
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  name: ai-model-deployment
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spec:
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  replicas: 2
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  selector:
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    matchLabels:
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      app: ai-model
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  template:
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    metadata:
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      labels:
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        app: ai-model
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    spec:
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      containers:
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      - name: ai-container
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        image: us.gcr.io/<PROJECT_ID>/my_ai_project:latest
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        ports:
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        - containerPort: 8080
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---
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apiVersion: v1
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kind: Service
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metadata:
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  name: ai-service
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spec:
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  selector:
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    app: ai-model
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  ports:
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    - protocol: TCP
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      port: 80
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      targetPort: 8080
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  type: LoadBalancer

After applying this, GKE will schedule two replicas (replicas: 2) across the cluster, and a LoadBalancer will be provisioned for external access.

8.3 Cloud Functions for Lightweight AI Services#

Similar to AWS Lambda, Google Cloud Functions support serverless development, excellent for quick or event-driven AI inferences. Key considerations include memory limits, maximum function execution time, and cold starts. While not suitable for massive deep learning models, they can be a cost-effective approach for tasks like real-time text classification or image thumbnail generation.

9.1 Monitoring and Logging#

Regardless of your chosen cloud platform, production-level AI services require robust monitoring. Logging frameworks like CloudWatch (AWS), Azure Monitor, or Google Cloud Logging capture application logs and system metrics, allowing operators to detect issues early.

Key metrics and logs typically include:

• Latency and throughput (e.g., requests per second).
• CPU, GPU, and memory utilization.
• Error rates and exception traces.
• Model performance drift (comparing predictions over time).

Additionally, employing alerting mechanisms ensures you’re notified via email, Slack, or SMS whenever anomalies occur—such as out-of-bound throughput or memory usage spikes.

9.2 CI/CD Pipelines for AI#

Continuous integration and continuous delivery (CI/CD) can significantly streamline AI deployments:

1. Version Control: Keep your data processing scripts, model code, and config files in a repository like GitHub or GitLab.
2. Automated Testing: Trigger unit tests and integration tests for each commit. For AI, you might also run quick training checks on smaller subsets of data to ensure the model remains functional.
3. Container Build: Automated Docker image building.
4. Deployment: Automatic promotion of images to staging or production environments if tests pass.

Tools like Jenkins, GitHub Actions, GitLab CI, or Azure DevOps can tie these steps together, resulting in faster iteration without compromising reliability.

9.3 Security and Governance in the Cloud#

Security concerns increase once models handle sensitive or proprietary data. Following best practices is crucial:

• Encrypt data at rest and in transit using encryption mechanisms provided by the cloud provider (e.g., AWS KMS, Azure Key Vault, Google Cloud KMS).
• Employ role-based access control (RBAC) to limit access to training data and model endpoints.
• Audit logs to track changes in data and model configurations.
• If required by regulations (like HIPAA or GDPR), ensure compliance by storing private data in dedicated services or regions that meet those standards.

Governance also extends to controlling model versions, ensuring reproducibility, and systematically documenting changes to training procedures, hyperparameters, and datasets.