Introduction to MLOPs

“My model worked perfectly on my laptop… then failed spectacularly in production.” 😅

No versioning, no updates, no teamwork; just pure chaos between data scientists and engineers.

That’s exactly the kind of scenario we unpacked during my virtual lecture on MLOps with over 70 second-year Software Engineering students from Makerere University! 🎓

We explored how MLOps saves us from this madness by bringing structure, automation, and collaboration to the entire machine learning lifecycle. From versioning data to deploying models that actually work outside your laptop. 😂

It was my second public lecture on the topic, and I’m so grateful to Dr. Galiwango Marvin for always giving me the opportunity to share knowledge with such eager minds. 🙏

Here’s to more students building not just cool models, but production-ready ones that don’t ghost us at deployment. 🚀

#MLOps

Transcript

1. I N T R O D U C T I O N T O M L O P S Bridging Machine Learning and Operations for Real-World Impact 27 Oct 2025 | Ronnie Atuhaire
2. Wait...Who Am I ?? A tech enthusiast, innovator, and social entrepreneur. Co-founder of MpaMpe, a crowdfunding startup promoting transparency and accountability through blockchain and AI, and the Lead Researcher at Farm Zenith, an initiative leveraging technology to drive climate-resilient and data- driven farming. Actively fosters innovation through community tech events & passionate about empowering communities and leveraging technology for positive change.
3. Combines ML, software engineering, and DevOps to deploy, monitor, and maintain models in production. Key Goals: Ensure reproducibility, scalability, and reliability of ML systems. Automate workflows (data → model → deployment). Why It Matters: 85% of ML projects fail without MLOps What is MLOps?
4. The ML Lifecycle
5. DevOps: Automates app deployment (code → build → test → deploy) MLOps Extensions: Data as Code: Version datasets (DVC) and track lineage 16. Training Pipelines: Replace builds with model training 16. Validation: Metrics like accuracy, fairness, and drift detection Startups & MLOps: Use cloud platforms (AWS, Azure, Render) for cost-effective scaling. Focus on rapid experimentation and automated pipelines. DevOps Vs MLOps
7. In a nutshell...
8. Eg..Grafana Monitoring
9. T O O L S
10. LLMOps: Managing large language models (e.g., GPT-4). AutoML: Automated model selection and tuning (e.g., H2O.ai). Ethical AI: Monitoring bias and fairness. Data-Centric AI: Focus on data quality over model complexity Current Trends
11. Google Cloud: https://cloud.google.com/architecture/mlops- continuous-delivery-and-automation-pipelines-in- machine-learning IBM: https://www.ibm.com/think/topics/mlops Databricks: https://www.databricks.com/glossary/mlops More++ R E S O U R C E S - T A B
12. Beyond Classroom: Transitioning from prototypes to revenue-generating products. Example: Netflix’s recommendation system saved $1B . IEEE + Student Hackathons + Conferences + CFPs Awards for Students... LinkedIn Startups & Research Contributions: Use cloud platforms (Render, GCP, AWS, Azure) for cost- effective scaling. Focus on rapid experimentation and automated pipelines. Model Commercialization
13. Let's Connect