Learn: Why Your ML Pipeline Needs Stream Processing

​🚀 Free Webinar: Why Your ML Pipeline Needs Stream Processing

​Modern ML isn’t just about training models - it’s about building pipelines that work reliably in production. This webinar breaks down when and why stream processing becomes essential for ML teams.

​🔍 What You’ll Learn

• ​⚡ Real-time feature engineering fundamentals

• ​🔄 How to minimize training-serving skew

• ​🧠 When batch processing is enough (and when it’s not)

• ​🏗️ How to think about ML pipelines from a system design perspective

​👥 Who Should Attend

• ​🤖 ML Engineers

• ​🛠️ Data Engineers & MLOps practitioners

• ​📊 Data Scientists moving to production

• ​🏗️ Backend Engineers & Tech Leads

​🎯 What You’ll Gain

• ​Clear decision-making framework: Batch vs Streaming

• ​Practical production insights (no fluff)

• ​Stronger ML system design intuition

• ​Confidence to architect real-time ML pipelines

​If you want to build ML systems that actually survive production, this session is for you.

​

​Host & Instructor: Yusuf Ganiyu

​Yusuf Ganiyu is an AI and Big Data architect specializing in real-time ML pipelines, MLOps, and scalable data systems. An MSc graduate of Cranfield University, he has taught 50,000+ learners and shares practical AI insights through Data Mastery Lab and CodeWithYu.

​

​Format & Access

• ​​Format: Live virtual webinar

• ​​Accessibility: Join from anywhere in the world

• ​​Pricing: Free

• ​​Date & Time: 21st March 2026, 01:00 - 02:30 (GMT+00:00)

​​

​​Important Instructions

​​• Registration is mandatory.
• The Microsoft Teams joining link will be shared via email 2 hours before the session.
• Please ensure you register with a valid email address.
• Seats may be limited.

​​👉 Click Register to reserve your seat.