Pros
Real-World Experience
You work on actual products or client projects—not just academic tasks.
Team Collaboration
You learn how ML integrates with backend, frontend, and DevOps teams.
Mentorship
You may get to work under experienced ML engineers or data scientists.
Exposure to Tools
You get hands-on with industry tools like Git, Docker, MLFlow, and frameworks (PyTorch, TensorFlow, Hugging Face).
Portfolio Building
Your work can be added to your resume and maybe even showcased (if not under NDA).
Job Opportunities
Internships often lead to full-time offers if you perform well.
Cons
No such cons in this company