Data Engineering for AI

Power AI with Robust Data Engineering: Build Scalable, Efficient Data Pipelines

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Enroll now for early access of e-LMS

MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
4 Weeks

About

The program delves into the core principles of data engineering for AI, including database management, ETL (extract, transform, load) processes, data warehousing, and cloud computing tools. Participants will learn to build robust and scalable data infrastructures to support machine learning models and AI applications.

Aim

This program is designed to equip participants with advanced skills in building scalable data pipelines, managing large datasets, and preparing data for AI models. It focuses on the practical application of data engineering techniques for AI-driven solutions, ensuring efficient data flow, storage, and processing in both cloud and on-premise environments.

Program Objectives

Understand the core principles of data engineering for AI.
Build scalable data pipelines and automate ETL workflows.
Manage, store, and process large datasets efficiently.
Implement real-time data streaming for AI models.
Gain hands-on experience with cloud-based data engineering tools.

Program Structure

Introduction to Data Engineering for AI
- The role of data engineering in AI and machine learning workflows
- Data engineering vs data science vs machine learning
Data Pipelines and Workflow Automation
- Building scalable ETL pipelines for AI
- Automating data workflows using Apache Airflow or Prefect
Data Storage and Management
- Managing structured and unstructured data
- Choosing the right databases for AI (SQL, NoSQL, Hadoop, Spark)
Data Transformation and Feature Engineering
- Cleaning, transforming, and preparing data for AI models
- Feature selection and engineering techniques
Cloud Data Engineering for AI
- Leveraging cloud platforms (AWS, GCP, Azure) for scalable data processing
- Using tools like S3, BigQuery, and Redshift for AI datasets
Real-Time Data Processing for AI
- Real-time data streaming with Kafka, Kinesis, and Spark Streaming
- Implementing real-time AI models using streaming data
Hands-on Project: Building AI-Ready Data Pipelines
- End-to-end data pipeline development from ingestion to deployment
- Managing and optimizing data pipelines for machine learning projects

Participant’s Eligibility

Data engineers, machine learning engineers, AI researchers, and data scientists focusing on building data pipelines for AI applications.

Program Outcomes

Master the design and implementation of scalable data pipelines for AI.
Proficiency in using cloud and big data tools to manage AI datasets.
Ability to automate data workflows and manage real-time data streams.
Experience in preparing and transforming data for machine learning models.

Fee Structure

Standard Fee: INR ~~5,998~~ USD 90

Discounted Fee: INR 2,999 USD 45

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

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Key Takeaways

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.