DevOps for Data Scientists: Containers for Data Science
"Unlock the Power of Containers in Data Science Workflows with DevOps"
Updated on Dec, 2023
Language - English
Duration -41 mins
DevOps for Data Scientists 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.
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.
What will you learn in this course:
- Beginner-level introduction to Docker
- Basic Docker Commands with Hands-On Exercises
- Understand what Docker Compose is
- Understand what Docker Swarm is
What are the prerequisites for this course?
- Basic System Administrator Skills
- Good to have (Not Mandatory) access to a Linux System to setup Docker to follow along
Check out the detailed breakdown of what’s inside the course
Introduction to Devops and Its Application in Data Science
- Introduction to Devops and Its Application in Data Science 07:27 07:27
Continuous Integration and Continuous Deployment (CI/CD) and Version controlling
The application of DevOps principles in data science
Examining the Different Types of Containers with an examples
Monitoring and managing containers in a production environment with an example
Optimize resource usage and efficient deployment and scaling of applications
My Name is Akhil Vydyula, I am a Data Scientist
I was previously worked on BFSI data analysis and modelling skills to oversee the full-life cycle of development and execution. He possess strong.
ability to data wrangling, feature engineering, algorithm development, model training and implementation.
SKILLS AND COMPETENCIES
Expert knowledge and experience with C/C++/python Programming and SQL.
Should be able to learn and Implement new technologies quickly and effectively.
Excellent Mathematical Skills, Problem Solving & Logical Skills.
Actively Participating in hackathons in various platforms and writing blogs in medium.
Machine Learning, Natural Language Processing(NLP),Computer Vision,Regression, Multi Label
Classification.Transfer Learning, Transformers, Ensembles, Stacking Classifiers.AutoML, SQL, Python, Keras, Pandas, NumPy, Seaborn,Matplotlib,Clustering,Recommendation Systems,Time Series Analysis.
User your certification to make a career change or to advance in your current career. Salaries are among the highest in the world.
Our students work
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