AED
UAD Dirham
Code: AED
Mentor Based
Data Engineering for AI
Power AI with Robust Data Engineering: Build Scalable, Efficient Data Pipelines
Early access to the e-LMS platform is included
About This Course
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
Who Should Enrol?
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
Discounted: ₹10999 | $164
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- One-on-one project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
★★★★★
Prediction of Protein Structure Using AlphaFold: An Artificial Intelligence (AI) Program
Diego Ordoñez • ★★★★★
AI for Environmental Monitoring and Sustainablility
Mladen Kulev • ★★★★★
Prediction of Protein Structure Using AlphaFold: An Artificial Intelligence (AI) Program
Liz Maria Luke • ★★★★★
AI for Psychological and Behavioral Analysis
Dr srilatha Ande srilatha.ammu12@gmail.com • View All Feedbacks →