10 Projects for ML Engineers in 2026

1. 1Build a production-ready ML model using MLOps tools
2. 2Deploy a large language model (LLM) on a cloud platform
3. 3Configure a multimodal AI system for real-world applications
4. 4Test and evaluate the performance of a deployed ML model
5. 5Apply continuous integration and continuous deployment (CI/CD) pipelines to an ML project
6. 6Compare the performance of different ML models on a given dataset

ML engineers and data scientists can benefit from these projects to improve their skills in deployment, MLOps, and production-ready systems

💡 Hands-on projects are essential for mastering machine learning deployment and MLOps

🚀 10 hands-on ML projects to master deployment, MLOps, LLMs, and multimodal AI in 2026! 💻