Master Containers for Seamless Data Science Workflows
Requirements
- Basic System Administrator Skills
- Good to have (Not Mandatory): Access to a Linux System to set up Docker and follow along
- No prior Docker experience needed. You will learn everything from scratch.
Description
Revolutionize Your Data Science Career with DevOps and Containerization
In today’s fast-paced world, data scientists are expected to deliver insights faster, collaborate across teams, and deploy scalable solutions efficiently. Traditional workflows often fall short when managing complex environments. That’s where DevOps and containerization come in.
This hands-on course is designed specifically for data scientists who want to build reliable, reproducible, and scalable workflows using Docker and DevOps practices. You will learn to automate deployments, collaborate seamlessly, and take your projects from the laptop to production with confidence.
What You’ll Learn:
Master DevOps for Data Science
Discover why DevOps matters for data science and how it can drastically improve collaboration, version control, and deployment efficiency.
Get Hands-On with Containerization
Understand the power of Docker and Kubernetes to create consistent and portable environments. Build customized containers for your data science applications.
Create Reproducible Data Science Environments
Say goodbye to "works on my machine" issues. Learn to package your models, dependencies, and code into lightweight, shareable containers.
Automate with CI/CD Pipelines
Streamline your workflow by implementing Continuous Integration and Continuous Deployment (CI/CD) for data science projects using modern DevOps tools.
Deploy and Scale with Confidence
Learn to deploy containerized data science applications to cloud platforms like AWS and Azure. Explore real-world scaling strategies for big data workloads.
Monitor and Manage with Ease
Set up monitoring for your deployed models and explore Infrastructure as Code (IaC) to automate infrastructure provisioning.
Explore Best Practices and Real-World Case Studies
Study successful DevOps implementations for data science, understand common pitfalls, and gain insights to optimize your workflows.
Why Enroll in This Course?
Practical, real-world projects you can immediately apply at work
Skills highly sought after by top companies hiring data scientists
Clear step-by-step tutorials suitable for beginners and advanced professionals
Lifetime access and continuous updates to keep you ahead
By the end of this course, you will not just know what DevOps and containers are — you’ll have real, production-ready skills that set you apart in the competitive data science job market.
Level up your data science career today. Enroll now and start building smarter, faster, and more reliable data science workflows.
Who this course is for:
- System Administrators looking to streamline deployments
- Cloud Infrastructure Engineers aiming to master containerization
- Developers interested in building, shipping, and running applications in containers
- Anyone curious about modern DevOps and container technologies
Instructor
- 4.0 Instructor Rating
- 1,142 Reviews
- 72,248 Students
- 45 Courses
Hello, I'm Akhil Vydyula — Lead Data Engineer at Publicis Sapient, and Former Senior Data Scientist at PwC.
With over 5 years of rich industry experience and a strong focus on the BFSI sector, I’ve led and delivered end-to-end data and analytics solutions that power strategic decisions and transform business outcomes.
At Publicis Sapient, I currently lead complex data engineering initiatives, leveraging my deep expertise in cloud-native platforms like AWS to architect robust, scalable data pipelines. My work spans across developing and optimizing ETL workflows using PySpark and Spark SQL, orchestrating data flows via EMR, Step Functions, and EventBridge, and driving real-time and batch data processing into PostgreSQL (RDS/Redshift) environments. I've also implemented AWS Glue and DMS to seamlessly replicate and transform large-scale on-premise data into cloud-native formats.
Previously, at PwC, I specialized in advanced analytics and machine learning within the Advisory Consulting practice. I’ve built and deployed predictive models using statistical analysis, regression, classification, clustering, and text mining—particularly for risk identification and decision modeling. My passion lies in transforming raw data into actionable insights through effective data storytelling and visualization.
In parallel to my corporate career, I bring over 5 years of teaching experience, mentoring hundreds of aspiring data professionals. I’m deeply committed to helping students break into the data industry by translating real-world challenges into practical learning experiences.
Whether it's building data pipelines, uncovering business insights, or shaping the next generation of data talent, I thrive at the intersection of technology, strategy, and impact.
Let’s connect if you're passionate about data, eager to learn, or looking to collaborate on meaningful, data-driven initiatives.