The Rise of SLMs: What ML & Data Professionals Need to Know

Larger doesn’t always mean better. While LLMs have dominated the AI conversation in the last few years (think GPT-4, Claude, Gemini), small language models (SLMs) are emerging as a more effective option in many use cases. 

These specialized models use fewer parameters, require less energy, compute, and memory resources, and can run on local devices like laptops for faster processing, better security, reduced latency, and significantly lower costs. And organizations deploying SLMs are already seeing ROI of 200-400% within the first year.

This Career Trends webinar is designed specifically for ML engineers and data scientists who want to understand what SLMs are and why they are gaining ground. It will show how they differ from LLMs in real-world deployment scenarios, and what this shift means for their roles, skill sets, and career trajectories in 2026.

It also provides an introduction to the Professional Certificate in AI and Machine Learning program from Michigan Engineering Professional Education, in collaboration with IBM, and delivered by Simplilearn.

What You’ll Learn in This Webinar

• SLMs Defined: Learn what these models are and how they differ from LLMs
• Enterprise Shift: Understand why organizations are transitioning to SLMs for production deployments
• Use Cases: Explore specific use cases where SLMs outperform LLMs and where they don’t
• Career Impact: Learn how the adoption of SLMs is changing ML engineers’ and data scientists' daily work
• SLM Skill Stack: Discover what skills, tools, and frameworks you’ll need to master to work with SLMs
• Career Prep: Get a practical overview of how to position yourself ahead of this shift in your career and on your resume 

About the Speaker

Kennedy Ganda - Senior AI/ML Engineer | ex-AWS

Kennedy Ganda is an AI/ML engineer and trainer with experience across data science, generative AI, machine learning, and data engineering. He currently works at CloudBees, following prior roles at Arity and Amazon Web Services (AWS), where he built and deployed scalable machine learning systems.

His expertise includes generative AI, MLOps, deep learning, reinforcement learning, and graph neural networks, as well as tools such as Databricks, Apache Spark, and cloud platforms. His background in computer science and mathematics supports his focus on developing production-ready AI solutions.

Why You Should Join This Live Session

• You’re an ML engineer who wants to understand how SLMs are changing model development and deployment
• You’re a data scientist exploring how SLMs will affect your modeling and experimentation workflows
• You’re an AI/ML professional who wants to stay ahead of the production AI landscape in 2026
• You’re a tech professional considering a move into an ML or data science role
• You’re an engineering manager or tech lead making AI architecture and tooling decisions

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