[Avg. reading time: 3 minutes]
Introduction
AI/ML are no longer just research topics - they drive industry, innovation, and jobs.
GenAI has shifted expectations: businesses want faster solutions with production-grade reliability.
MLOps ensures ideas → working models → deployed systems.
Evolution of the Field
2010s: Big Data + early ML adoption (scikit-learn, Spark MLlib).
2015-2022: Deep learning boom (Neural Networks, NLP with BERT).
2022: Generative AI (GPT, diffusion models).
MLOps is critical for scaling, governance, monitoring.
Where MLOps Fits in the Data/AI Journey
MLOps is part of all of this.
Without MLOps, many models stay as “academic projects.”
Today’s hiring market looks for hybrid skills (data + ML + cloud + ops).
Course Positioning
Not too heavy on topics covered in other courses such as ML algorithms or NLP or Deep Learning or LLM.
This course is heavy on CICD - MLOps, Pipelines, versioning, monitoring, cloud platforms and related toolsets.
Course Focus = Industry Readiness
