No More Manual Overheads: Embracing DevOps in Machine Learning#

What is DevOps?#

DevOps is a cultural and technical movement aimed at improving the collaboration between software development (Dev) and IT operations (Ops). It seeks to reduce software development cycles, increase deployment frequency, and encourage close alignment between these traditionally siloed teams.

Key principles include:

• Collaboration and communication
• Continuous integration and continuous deployment (CI/CD)
• Version control and traceability
• Automation of repetitive processes

What is Machine Learning?#

Machine Learning (ML) is the subset of artificial intelligence that enables software applications to become more accurate at predicting outcomes without explicit programming. Typical ML workflows involve:

1. Gathering data
2. Preprocessing and cleaning data
3. Feature engineering
4. Model training
5. Model evaluation
6. Model deployment
7. Model monitoring and updates

ML projects are inherently iterative and data-driven, meaning each step in the pipeline might need to be revisited multiple times as one tunes hyperparameters, gathers more data, or updates the training code.

The Intersection of DevOps and ML#

DevOps and ML intersect when organizations require robust, automated, and reproducible pipelines for model development and deployment. Traditional DevOps addresses continuous integration and delivery of code, but ML code is heavily data-dependent, and it requires additional considerations like dataset versioning and model artifact storage. Integrating DevOps best practices with machine learning leads to what is commonly referred to as MLOps, focusing on:

• Automating the entire ML pipeline
• Tracking data, code, and model versions
• Ensuring reliability and reproducibility

Manual Overheads in ML#

Without DevOps, ML pipelines often rely on ad hoc scripts and manual steps. For example:

• Data scientists might download data locally and clean it on their machine.
• Model deployment might involve copying files onto servers manually.
• Monitoring performance might rely on occasional spreadsheets or logs.

This approach can lead to:

• Loss of reproducibility: Difficulty in retracing how a model was trained or which data was used.
• Slow iterations: Any changes to data or model code can break the pipeline.
• Poor collaboration: Multiple data scientists stepping on each other’s toes when sharing code or data.

Benefits of DevOps for ML#

1. Version Control and Reproducibility
   Automated versioning of data, models, and code ensures you can always reproduce results.

2. Faster Iterations
   Automated pipelines drastically reduce the time spent on repetitive tasks, allowing for quicker feedback loops.

3. Scalability
   Infrastructure as code and containerization ensure you can easily scale training and deployment across multiple environments.

4. Improved Collaboration
   Shared repositories, integrated workflows, and standardized processes relieve friction between data science, development, and operations teams.

5. Consistent, High-Quality Releases
   Automated testing and continuous deployment reduce the risk of bugs and performance degradations making it into production.

Step-by-Step Overview#

At a high level, a basic ML DevOps pipeline might look like this:

1. Data Ingestion & Preprocessing

   • Ingest data from a source (like a database or CSV files).
   • Clean and preprocess data.
   • Store processed data for training.

2. Model Training & Validation

   • Pull the latest code and data from version control.
   • Train the model using a configured environment.
   • Validate the model with automated tests and metrics checks.

3. Model Packaging

   • Once validated, package the model artifact (e.g., a pickle file, ONNX, or TensorFlow SavedModel).

4. Deploy & Serve

   • Deploy the model to a staging environment (like a development server).
   • Run integration or acceptance tests.
   • Deploy to production environment upon successful tests.

5. Monitoring & Logging

   • Monitor performance metrics (accuracy, precision, recall, etc.).
   • Track system logs and data drift.

A Simple Example#

Below is a simplified directory structure showing how you might organize an ML project under DevOps:

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ml-project/
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|-- data/
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|   |-- raw/
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|   |-- processed/
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|-- models/
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|   |-- ...
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|-- src/
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|   |-- preprocessing/
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|   |-- training/
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|   |-- inference/
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|-- scripts/
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|   |-- run_training.sh
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|   |-- run_inference.sh
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|-- tests/
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|   |-- unit/
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|   |-- integration/
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|-- requirements.txt
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|-- Dockerfile
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|-- Makefile (optional)
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|-- .gitlab-ci.yml (or similar for GitHub Actions)

Keeping your project structure clear and documented eases onboarding for new team members and sets the foundation for automation.

Why Infrastructure as Code?#

Infrastructure as Code (IaC) refers to managing your infrastructure—servers, storage, networks—using configuration files that can be version-controlled.

This benefits ML pipelines in multiple ways:

• Reproducibility: You can replicate the same environment used for training for production or for new developers.
• Scalability: Automated scripts can spin up multiple GPU- or CPU-based nodes as required.
• Disaster Recovery: You have a blueprint of your entire environment, making it easy to rebuild if something goes wrong.

Common IaC Tools#

• Terraform: A popular open-source tool that allows you to manage infrastructure on multiple cloud providers through a single language (HCL).
• Ansible: Uses a playbook-based approach to configure systems and deploy software.
• AWS CloudFormation: Native AWS service for managing AWS resources as code.

Example: Terraform for ML#

Below is a small snippet in Terraform that can be used to create a simple AWS EC2 instance, often used for ML experiments:

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provider "aws" {
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  region = "us-east-1"
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}
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resource "aws_instance" "ml_training_node" {
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  ami           = "ami-0c94855ba95c71c99" # Amazon Linux 2
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  instance_type = "m5.large"
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  tags = {
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    Name = "ML-Training-Node"
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  }
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}

You could extend this to include GPU instances, load balancers, or specialized storage for data. Everything is tracked in Git, so you can revert to a previous configuration if needed.

Git for Code and Scripts#

A crucial first step in ML DevOps is to place every piece of code—from data preprocessing scripts to training notebooks—under version control. Git is the de facto standard, providing:

• Branching for feature development
• Pull Requests or Merge Requests for code reviews
• History of changes for easy rollback

Data Versioning#

Data is not always held in Git due to size constraints. Instead, you might use data versioning tools like:

• DVC (Data Version Control): Works similarly to Git, tracks data changes, and integrates well with cloud storage.
• MLflow: Tracks metrics, parameters, and artifacts (including data and models).
• Git LFS: Large File Storage extension for Git, although better suited for simpler cases.

Model Versioning#

Storing and tracking model artifacts is another critical aspect. A model’s performance depends on code, data, hyperparameters, and the environment. Tools like MLflow and Weights & Biases keep all of these aspects recorded, allowing you to compare different experiments and quickly restore a previous model if necessary.

Why Testing is Unique in ML?#

Traditional software tests check functionalities: Does the function return the correct output given some input? In ML, outputs are probabilistic and performance-based. This makes testing more nuanced.

Types of ML Tests#

1. Unit Tests

   • Check individual functions or classes in your code.
   • For example, test if your data preprocessing function correctly scales numeric values.

2. Integration Tests

   • Ensure various parts of the pipeline work together.
   • For example, check if the model training script correctly loads data from a data warehouse.

3. Data Validation Tests

   • Validate schema, missing values, or anomalies in your dataset.
   • Can be automated using Great Expectations or TFX Data Validation.

4. Performance Tests

   • Test if your model meets performance thresholds (accuracy, F1-score, etc.).
   • If your model’s performance dips below a certain threshold in a new dataset, the test fails.

5. Regression Tests

   • Compare the current model’s performance with a baseline or the last production model.
   • Helps ensure no unintentional drift in accuracy or other metrics.

Example: A Simple Unit Test in PyTest#

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import pytest
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import numpy as np
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from src.preprocessing import scale_features
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def test_scale_features():
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    data = np.array([[1, 2], [3, 4]], dtype=float)
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    scaled = scale_features(data)
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    # Check shape remains the same
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    assert scaled.shape == data.shape
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    # Check the mean is close to zero
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    assert np.isclose(np.mean(scaled), 0, atol=0.1)
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    # Check the std is close to 1
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    assert np.isclose(np.std(scaled), 1, atol=0.1)

These tests can be integrated into a CI system, ensuring they run every time someone pushes new code.

The Need for Containers in ML#

ML requires consistent environments to avoid the dreaded “works on my machine�?syndrome. Various library incompatibilities can break your pipeline. Docker solves this problem by creating portable, self-contained environments.

Docker Basics#

Docker images are templates that define:

• Base OS (e.g., Ubuntu)
• Language runtime (e.g., Python)
• Libraries and dependencies (e.g., scikit-learn, PyTorch)
• Environment variables

Example Dockerfile#

Below is a basic Dockerfile for an ML project:

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FROM python:3.9-slim
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# Set a working directory
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WORKDIR /app
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# Copy requirements and install
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy project files
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COPY src/ ./src/
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COPY scripts/ ./scripts/
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# Run a test command (optional)
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RUN pytest --maxfail=1 --disable-warnings
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# Entrypoint for container
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CMD ["python", "src/training/train.py"]

When you build and run this image, your code will run in a consistent environment every time.

CI Overview#

Continuous Integration (CI) automates the process of merging code changes, running tests, and ensuring the codebase is always in a functional state. For ML, this might include:

• Environment setup
• Installing dependencies
• Running data validation tests
• Running model training tests
• Packaging artifacts

Example CI Config (GitHub Actions)#

Below is a simplified .github/workflows/ci.yml file that demonstrates CI for an ML project:

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name: ML CI
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on:
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  push:
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    branches:
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      - main
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  pull_request:
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jobs:
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  build-and-test:
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    runs-on: ubuntu-latest
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    steps:
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      - name: Checkout Code
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        uses: actions/checkout@v2
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      - name: Set up Python
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        uses: actions/setup-python@v2
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        with:
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          python-version: '3.9'
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      - name: Install Dependencies
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        run: |
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          pip install --upgrade pip
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          pip install -r requirements.txt
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      - name: Run Unit Tests
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        run: |
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          pytest --maxfail=1 --disable-warnings

In a real-world scenario, you might also include data version fetching from a separate storage, additional integration tests, and even building a Docker image as part of the CI pipeline.

CD Goals#

Continuous Delivery and Deployment aim to ensure that every change is automatically built, tested, and deployed to a production environment (or staging first, then production) if it passes all checks. This significantly reduces manual overhead and deployments become frequent and reliable.

Deployment Strategies#

1. Blue-Green Deployment
   • Maintain two identical production environments. One is “blue�?(current production), and the other is “green�?(new version). Switch traffic to the green environment after successful validation.
2. Canary Deployment
   • Gradually route a small percentage of traffic to the new version while most traffic continues to go to the old version. This approach allows monitoring of real user metrics on the new model.
3. Rolling Upgrades
   • Gradually replace instances in a production environment with new ones until all are upgraded.

Example Deployment Pipeline#

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deploy:
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  stage: deploy
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  image: google/cloud-sdk:latest
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  script:
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    - gcloud auth activate-service-account --key-file $GOOGLE_APPLICATION_CREDENTIALS
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    - gcloud config set project my-ml-project
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    - gcloud app deploy app.yaml --quiet
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  only:
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    - main

The above snippet (for GitLab CI/CD) deploys your ML application (e.g., a REST API serving your model) to a Google Cloud App Engine upon successful builds and tests.

Why Monitoring Matters#

In ML, the best model today can become worthless tomorrow if the data distribution shifts (data drift). Monitoring helps you spot issues early and maintain model performance.

Monitoring Metrics#

1. System Metrics
   • CPU, GPU, memory usage, response times, and throughput.
2. Model Performance Metrics
   • Accuracy, F1-score, precision, recall, etc.
3. Data Drift
   • Statistical tests comparing the current input distribution with the training distribution (e.g., KL divergence).
4. Concept Drift
   • Gradual changes in the relationships between features and targets, requiring model retraining.

Observability Tools#

• Prometheus + Grafana: Collect and visualize system metrics.
• Elasticsearch + Kibana: Log aggregation and analytics.
• Sentry or Datadog: Application performance monitoring.
• MLflow: Tracks ML project metrics, artifacts, and parameters.

Here’s a simplistic Python example that logs prediction requests:

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from flask import Flask, request, jsonify
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import logging
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import time
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app = Flask(__name__)
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logging.basicConfig(level=logging.INFO)
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@app.route('/predict', methods=['POST'])
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def predict():
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    start_time = time.time()
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    data = request.json['data']
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    # Suppose we call your model here
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    prediction = model.predict(data)
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    duration = time.time() - start_time
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    app.logger.info(f"Processed prediction in {duration:.4f} seconds")
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    return jsonify({"prediction": prediction})

The logs can then be shipped to a logging system for further analysis.

Horizontal and Vertical Scaling#

1. Horizontal Scaling: Adding more machines to handle increased workload, useful for distributed training on large datasets or microservices architecture for inference.
2. Vertical Scaling: Increasing the computational resources (CPU, GPU, RAM) on a single machine, often beneficial for model training tasks that require large GPU memory.

Orchestration Tools#

• Kubernetes: Allows you to manage containers in clusters, configure scaling policies, and handle rolling updates.
• Apache Airflow or Kubeflow Pipelines: Orchestrate complex data pipelines and ML workflows with a DAG (Directed Acyclic Graph) approach.

Example: Airflow DAG for ML#

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from airflow import DAG
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from airflow.operators.bash_operator import BashOperator
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from airflow.utils.dates import days_ago
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default_args = {
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    'owner': 'airflow',
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    'start_date': days_ago(1),
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}
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with DAG('ml_pipeline',
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         default_args=default_args,
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         schedule_interval='@daily',
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         catchup=False) as dag:
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    fetch_data = BashOperator(
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        task_id='fetch_data',
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        bash_command='python /app/src/data/fetch_data.py'
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    )
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    preprocess = BashOperator(
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        task_id='preprocess',
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        bash_command='python /app/src/preprocessing/clean_data.py'
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    )
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    train_model = BashOperator(
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        task_id='train_model',
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        bash_command='python /app/src/training/train.py'
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    )
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    fetch_data >> preprocess >> train_model

The DAG defines three tasks: fetching data, preprocessing, and training, executed in order. Airflow handles scheduling, logging, retries, and more.

Feature Stores#

A feature store centralizes and manages the features used across different models. It ensures consistency in how features are computed and served, reducing duplication of effort. Some popular feature store solutions are:

• Feast (open source)
• Hopsworks
• Tecton

Managing Multiple Environments#

While having a single production environment may work for smaller projects, enterprise-level projects often require:

• Development: For experimentation by individual data scientists.
• Staging: For integration tests and acceptance tests before production.
• Production: For serving live predictions.

Environment-specific configuration management is crucial to avoid mistakes such as pointing production code to a development database.

Secure ML Systems#

As your pipeline grows, security considerations become major:

• Restricted access to sensitive data
• Encryption of data in transit and at rest
• Role-based access control (RBAC) for your CI/CD platform
• Auditing all model predictions for compliance

ML Governance#

Enterprises often require regulatory compliance and auditability. ML governance covers:

• Approval workflows for high-stakes models
• Documenting how and why a model makes certain decisions
• Ensuring fairness and avoiding biases in training data