Machine Learning and Deep Learning for Software Engineers
This specialization empowers software engineers, backend developers, and full-stack professionals to integrate, deploy, and maintain machine learning models...
By Board Infinity on Coursera
About This Course
This specialization empowers software engineers, backend developers, and full-stack professionals to integrate, deploy, and maintain machine learning models within production software systems. You will approach ML through an engineering lens — emphasizing software design, APIs, scalability, and maintainability rather than theory alone. Starting with applied ML fundamentals, you will build and train models using Scikit-learn, TensorFlow, and PyTorch while writing modular, testable ML code. As you progress, you will convert ML models into production-ready APIs using FastAPI and Flask, design scalable microservices for inference, and manage model versioning and performance optimization. The third course introduces MLOps foundations — covering reproducibility, experiment tracking, and version control using Git, DVC, and MLflow. The final course brings everything together with CI/CD pipelines, continuous delivery of models, monitoring inference performance and data drift, and implementing retraining and rollback strategies. By the end, you will have the engineering competencies to build, serve, operate, and maintain ML-powered applications across the full production lifecycle.
Topics Covered
Frequently Asked Questions
How much does Machine Learning and Deep Learning for Software Engineers cost?
Machine Learning and Deep Learning for Software Engineers costs $49. Check the course page for current pricing and available discounts.
Who teaches Machine Learning and Deep Learning for Software Engineers?
Machine Learning and Deep Learning for Software Engineers is taught by Board Infinity, Board Infinity.
What skill level is Machine Learning and Deep Learning for Software Engineers for?
This course is designed for beginner learners.
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