We’re organizing a two-day practical workshop to help professionals understand the real mechanics behind modern AI training pipelines.
Across two intensive days, participants will move from concepts to practice. From understanding how data flows into AI systems to learning how annotation and quality validation shape model performance in the real world.
Day 1: Data Annotation
Participants will explore how AI models learn and why high-quality annotation is essential. The session covers image, audio, text, and video data, along with practical annotation techniques used in computer vision and audio systems.
You will learn and practice:
* Bounding boxes, polygons, keypoints, and semantic segmentation
* Audio transcription, speaker segmentation, and dialect tagging
* Annotation best practices and quality assurance methods
* Real challenges such as bias, ambiguity, and scaling annotation teams
Day 2: Data Quality
The second day focuses on one of the most critical areas in AI pipelines: ensuring data reliability and integrity.
Participants will explore:
* Why data quality failures break analytics and AI systems
* Key data quality dimensions like accuracy, completeness, consistency, and timeliness
* Identifying common problems such as duplicates, missing values, outliers, and data drift
* Building automated data quality checks using Python
* Integrating data validation into real ML and ETL pipelines using Airflow
By the end of the workshop, participants will gain practical exposure to building automated data-quality engines and monitoring pipelines for real-world AI systems.
📆 Date: 26–27 March 2026
📍 IIT Alumni Centre (IITACB), Bommasandra, Bengaluru
💡Early Bird Offer: ₹2000 until 15 March
💡Regular Price: ₹2499 after 15 March
🔗Register here: https://rzp.io/rzp/UT1Wl5WB
At ACSEL, we believe strong AI systems are built on strong foundations. Understanding data annotation and data quality is one of the most valuable skills for anyone building real-world AI.