Cloud-Native Machine Learning & MLOps: From Pipelines to Production
About This Course
This 5-day hands-on course builds on foundational ML and data engineering skills to help learners design, deploy, and monitor machine learning workflows using managed cloud services. Working with cloud-native tools from AWS, Azure, or GCP, learners will set up notebooks, train models, use AutoML, deploy secured endpoints, and automate pipeline orchestration. The course emphasizes production-readiness with versioning, model monitoring, and retraining logic. Labs are adaptable to the client’s cloud provider. On the final day, learners complete written and practical assessments to validate full MLOps workflow delivery.
What You'll Learn
2. Access the feasibility of AI applications for products and maintenance processes based on algorithm design and implementation requirements.
3. Analyze AI application algorithms by applying performance effectiveness concepts to evaluate their operational efficiency.
4. Evaluate various AI applications using standard effectiveness methods to compare their strengths and limitations in product integration.
5. Improve AI product by applying established process management evaluation methods to measure their impact on product effectiveness.
6. Correlate AI algorithm design with operational efficiency to determine the applicability of AI solutions throughout the product development track.
Entry Requirements
• The candidate should hold a diploma and have at least 2 years of experience working as a developer/programmer.
• Candidates who do not meet the above requirements will be assessed on case to case basis.