KodeKloud
4 weeks to complete at 10 hours a week
Master the complete machine learning lifecycle—from data ingestion to production deployment. This hands-on Specialization equips ML engineers with critical skills in data engineering, model deployment, monitoring, and governance to build reliable, scalable ML systems.
Learners are recommended to have intermediate Python proficiency, Linux command line familiarity, and basic SQL querying skills.
This Specialization teaches MLOps (Machine Learning Operations)—the practice of bringing machine learning models from development to production reliably and efficiently. You will build end-to-end MLOps pipelines that automate the ML lifecycle from data ingestion to model deployment, deploy production-ready ML models using industry-standard serving frameworks, implement CI/CD/CT workflows specifically designed for machine learning systems, design and manage data engineering pipelines using Apache Spark, Kafka, Airflow, and modern data processing tools, monitor ML models in production, detect data drift, and implement automated retraining workflows, apply governance frameworks and ensure compliance with GDPR, HIPAA, and PCI regulations, optimize ML infrastructure for cost-efficiency across CPU and GPU resources, and troubleshoot and debug production ML systems.
KodeKloud is an innovative online learning platform dedicated to offering the most effective, hands-on experience in DevOps education. Founded by industry experts, we have quickly become a go-to resource for professionals looking to enhance their skills, solve immediate technical challenges, and accelerate their career growth.
This Specialization prepares you for high-growth, high-demand roles including MLOps Engineer, Machine Learning Engineer, ML Platform Engineer, and Data Engineer. These skills, including Automation, Machine Learning, and Continuous Deployment, position you for roles at technology & software, financial services & fintech, healthcare & biotech, e-commerce & retail, insurance, manufacturing, telecommunications, and cybersecurity industries.
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