DevOps for Data Scientists: Containers for Data Science
What you'll learn
- Beginner level introduction to Docker
- Basic Docker Commands with Hands-On Exercises
- Understand what Docker Compose is
- Understand what Docker Swarm is
Requirements
- Basic System Administrator Skills
- Good to have (Not Mandatory) access to a Linux System to setup Docker to follow along
Description
Course Overview:
In today's data-driven world, data scientists play a crucial role in extracting valuable insights from vast amounts of data. However, working with complex data science projects often requires collaboration with software developers and IT operations teams. DevOps practices and containerization can greatly enhance the efficiency and reproducibility of data science workflows.
In this course, you will learn how to leverage DevOps principles and containerization techniques to streamline your data science projects. Specifically, we will focus on the use of containers, such as Docker, to encapsulate data science environments and enable seamless collaboration and deployment.
Course Highlights:
1. Introduction to DevOps in Data Science:
- Understand the core concepts of DevOps and its relevance in the context of data science.
- Explore the benefits of adopting DevOps practices for data scientists.
2. Introduction to Containerization:
- Gain a solid understanding of containerization and its advantages for data science projects.
- Learn about Docker and container orchestration platforms like Kubernetes.
3. Creating Data Science Environments with Containers:
- Discover how to create reproducible and portable data science environments using Docker.
- Build custom Docker images with the necessary dependencies and libraries for your projects.
4. Collaboration and Version Control:
- Learn how to effectively collaborate with software developers and version control your data science projects.
- Integrate your containerized workflows with version control systems like Git.
5. Continuous Integration and Deployment (CI/CD) for Data Science:
- Implement CI/CD practices for your data science projects using containerization.
- Automate the building, testing, and deployment of your data science applications.
6. Scaling and Deployment Considerations:
- Explore strategies for scaling your containerized data science applications to handle larger datasets and increased workloads.
- Understand deployment options, such as deploying containers to cloud platforms like AWS or Azure.
7. Monitoring and Infrastructure as Code:
- Learn how to monitor and manage your containerized data science applications.
- Explore the concept of infrastructure as code (IaC) and its application in data science workflows.
8. Best Practices and Case Studies:
- Discover industry best practices and real-world case studies of successful DevOps implementations in data science.
- Gain insights into common challenges and effective strategies for overcoming them.
By the end of this course, you will have the skills and knowledge to leverage DevOps principles and containerization techniques to enhance your data science workflows. Whether you work independently or as part of a larger team, this course will empower you to collaborate effectively and deploy your data science applications with confidence. Join us on this journey to revolutionize your data science practices with DevOps and containers.
Who this course is for:
- System Administrators
- Cloud Infrastructure Engineers
- Developers
Instructor
- 3.9 Instructor Rating
- 283 Reviews
- 21,644 Students
- 41 Courses
Hello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.
My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.
I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.
Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.
Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.
I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!