DevOps for Data Scientists: Master Containers in Data Science

DevOps for Data Scientists Containers for Data Science

"Unlock the Power of Containers in Data Science Workflows with DevOps"

Created by : Rated :4.3 56 views

Last Updated: 2024-12-13 09:12:08 English Course Type: Self Learning

Beginner level introduction to Docker
Basic Docker Commands with Hands-On Exercises
Understand what Docker Compose is
Understand what Docker Swarm is

Unlock the full potential of data science by mastering DevOps principles tailored specifically for data scientists. This course provides a comprehensive introduction to DevOps practices, focusing on how containers can revolutionize data science workflows.

Through engaging video lectures, you'll explore essential topics such as:

Understanding DevOps Basics: Discover how DevOps integrates with data science to enhance collaboration and efficiency.

Continuous Integration and Continuous Deployment (CI/CD): Learn the significance of CI/CD in automating your data science projects and maintaining version control.

DevOps Principles in Data Science: Gain insights into how these principles can streamline data pipelines and improve project outcomes.

Container Types: Examine various container technologies and their applications within data science.

Container Management: Understand best practices for monitoring and managing containers in a production environment.

Resource Optimization: Explore strategies for optimizing resource usage and deploying scalable applications efficiently.

Whether you're a data scientist looking to enhance your skills or a professional seeking to integrate DevOps into your projects, this course will equip you with the knowledge and tools needed to succeed in today’s data-driven landscape. Join us to transform your approach to data science through effective DevOps practices

Basic System Administrator Skills
Good to have (Not Mandatory) access to a Linux System to setup Docker to follow along

System Administrators
Cloud Infrastructure Engineers
Developers

Section 1 : Introduction to Devops and Its Application in Data Science 1 Lectures 00:07:27
Lecture 1 :
Understanding the Basics of DevOps and Its Application in Data Science Preview
Section 2 : Continuous Integration and Continuous Deployment (CI/CD) and Version controlling 1 Lectures 00:07:23
Lecture 1 :
Continuous Integration and Continuous Deployment (CI/CD)
Section 3 : The application of DevOps principles in data science 1 Lectures 00:08:41
Lecture 1 :
The application of DevOps principles in data science
Section 4 : Examining the Different Types of Containers with an examples 1 Lectures 00:07:45
Lecture 1 :
Examining the Different Types of Containers
Section 5 : Monitoring and managing containers in a production environment with an example 1 Lectures 00:05:52
Lecture 1 :
Monitoring and managing containers in a production environment
Section 6 : Optimize resource usage and efficient deployment and scaling of applications 1 Lectures 00:04:33
Lecture 1 :
Optimize resource usage and efficient deployment and scaling of applications

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AKHIL VYDYULA,

290 Course Views

10 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!