Introduction

DevOps has become a crucial methodology for streamlining and improving the development, deployment, and maintenance of software systems. In the context of data science, adopting DevOps practices helps automate the deployment of machine learning models, ensures continuous integration and delivery, and improves collaboration between data scientists and operations teams. This course focuses on integrating DevOps practices into the data science lifecycle, with the aim of automating workflows, increasing model deployment speed, and ensuring scalability and reliability in production environments.

Through hands-on exercises, participants will learn how to implement DevOps principles in data science workflows and gain practical experience with tools commonly used in DevOps for machine learning projects.

Objectives

By the end of this course, participants will:

• Understand the fundamentals of DevOps and how it applies to data science.
• Learn how to set up continuous integration (CI) and continuous deployment (CD) pipelines for machine learning models.
• Master version control, containerization, and orchestration in data science projects.
• Gain hands-on experience with tools like Git, Docker, Jenkins, Kubernetes, and MLflow for automating data science workflows.
• Understand how to monitor and maintain machine learning models in production.
• Learn best practices for scaling and managing machine learning models using cloud platforms.

Who Should Attend?

This course is ideal for:

• Data scientists and machine learning engineers who want to learn about DevOps practices.
• DevOps engineers looking to apply their expertise to data science workflows.
• Project managers, data engineers, and IT professionals involved in machine learning model deployment and lifecycle management.
• Anyone interested in automating and scaling data science processes.

Day 1: Introduction to DevOps and Data Science Workflow

Morning Session: Understanding DevOps in Data Science

• Overview of DevOps principles: Continuous integration (CI), continuous delivery (CD), and infrastructure as code (IaC).
• The evolution of DevOps in software engineering and its relevance to data science.
• Challenges in traditional data science workflows: Bottlenecks in model development, deployment, and maintenance.
• The intersection of DevOps and data science: Automating data science processes to improve collaboration, speed, and reliability.
• Key tools and technologies in DevOps for data science: Git, Docker, Jenkins, MLflow, Kubernetes.

Afternoon Session: Setting Up Version Control for Data Science

• Introduction to version control in data science: Git and GitHub for managing code, data, and models.
• Best practices for managing machine learning code and experiments using Git.
• How to manage data science models: Versioning datasets, model parameters, and configurations.
• Hands-on: Setting up a Git repository for a machine learning project and committing code.

Day 2: Continuous Integration and Testing in Data Science

Morning Session: Continuous Integration (CI) for Data Science Projects

• The role of CI in data science: Automating testing and validation of models.
• Building automated testing pipelines for machine learning: Unit tests, integration tests, and model validation.
• Tools for CI in data science: Jenkins, CircleCI, GitLab CI.
• Benefits of CI: Reducing bugs, improving collaboration, and ensuring reproducibility.
• Hands-on: Setting up a basic CI pipeline using Jenkins or GitLab CI for model validation.

Afternoon Session: Automating Model Testing and Quality Assurance

• Writing automated tests for data pipelines, machine learning code, and model outputs.
• Using testing frameworks: PyTest, unittest for Python-based projects.
• Ensuring data quality: Testing data preprocessing steps and model inputs.
• Techniques for validating model performance: Cross-validation, A/B testing, and real-time model testing.
• Hands-on: Creating automated tests for a machine learning model and integrating them into the CI pipeline.

Day 3: Containerization and Deployment with Docker

Morning Session: Introduction to Docker for Data Science

• What is Docker and why is it important for data science?
• Creating reproducible machine learning environments with Docker containers.
• Benefits of containerization: Isolation, scalability, and portability.
• Building Docker images for data science projects: Including Python environments, libraries, and dependencies.
• Hands-on: Creating a Docker container for a Python-based machine learning model.

Afternoon Session: Deploying Machine Learning Models with Docker

• Deploying machine learning models in Docker containers for consistency across environments.
• Container orchestration with Kubernetes: Managing and scaling Docker containers in production.
• Best practices for deploying models in containers: Networking, data access, and configuration management.
• Hands-on: Deploying a machine learning model using Docker and exposing it as a REST API.

Day 4: Continuous Deployment and Model Monitoring

Morning Session: Continuous Deployment (CD) in Data Science

• Understanding the principles of continuous deployment: Automating the release of models into production.
• Creating CI/CD pipelines for data science workflows: From model training to deployment.
• Tools for CD in data science: Jenkins, GitLab, Azure DevOps.
• Integrating model deployment with cloud services: AWS, GCP, Azure.
• Hands-on: Building a CI/CD pipeline to automate model deployment from GitHub to cloud infrastructure.

Afternoon Session: Monitoring and Maintaining Models in Production

• The importance of monitoring machine learning models after deployment: Performance, drift, and accuracy.
• Setting up model monitoring: Tools like Prometheus, Grafana, and Datadog.
• Techniques for tracking and logging model performance: Retraining, model drift detection, and anomaly detection.
• Scaling models: Horizontal vs. vertical scaling, autoscaling in Kubernetes.
• Hands-on: Setting up model monitoring with Grafana for a deployed model.

Day 5: Advanced DevOps for Data Science and Scaling Models

Morning Session: Advanced DevOps Practices for Data Science

• Infrastructure as code (IaC) for data science projects: Using Terraform and Ansible for automating infrastructure setup.
• Automating model experimentation with MLflow: Tracking experiments, parameters, and metrics.
• Using Kubernetes for scaling machine learning models and pipelines.
• Cloud platforms for DevOps in data science: Leveraging AWS, GCP, or Azure for end-to-end deployment.
• Hands-on: Setting up an automated infrastructure using Terraform for a data science pipeline.

Afternoon Session: Scaling and Automating Model Pipelines

• Best practices for scaling data science workflows: Handling large datasets, distributed computing, and parallel processing.
• Building end-to-end data pipelines with Apache Airflow and Kubeflow.
• Using cloud-native tools for model scaling and orchestration.
• Hands-on: Automating an end-to-end data pipeline using Apache Airflow/Kubeflow and deploying to a cloud platform.

Materials and Tools:

• Software: Python, Git, Docker, Jenkins, Kubernetes, MLflow, Terraform, Apache Airflow, Grafana.
• Cloud Platforms: AWS, GCP, Azure (participants should have access to cloud accounts for hands-on sessions).
• Datasets: Real-world datasets for practice (e.g., Kaggle datasets, UCI ML repository).

Post-Course Support:

• Access to recorded sessions, course materials, and additional resources for continued learning.
• Follow-up workshops on advanced topics like advanced Kubernetes usage, cloud-native MLops, and scaling distributed machine learning workflows.
• Community forum for sharing experiences, asking questions, and collaborating on DevOps for data science challenges.