Certified Machine Learning Engineer (CMLE)
Cloud & DevOps · 4 days (32 hours) or self-paced (100–130 hours) · AI Certification Council (AICC)
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Course Overview
Validates engineering skills for deploying, monitoring, and scaling ML models in production. Covers MLOps, model versioning, CI/CD pipelines for ML, feature stores, and infrastructure management.
Strong programming skills (Python); experience with cloud platforms and Docker.
Key Takeaways
- 1Validates engineering skills for deploying, monitoring, and scaling ML models in production. Covers MLOps, model versioning, CI/CD pipelines for ML, feature stores, and infrastructure management.
Who Should Attend?
- Teams in Machine Learning Engineering
- Teams in DevOps
- Teams in Cloud Computing
- Teams in IT
About the Certificate
This programme aligns with the standards set by AI Certification Council (AICC). Delivery is by verified Skillware providers.
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FAQ
What language will the course be taught in?
Courses are delivered in English unless otherwise stated. A working professional level of English is recommended.
What formats are courses offered in?
Most programmes run as face-to-face classroom sessions, virtual live-instructor sessions, or in-house cohorts on-site for your team.
Are courses accredited?
Where the programme is accredited, the body is listed in the “About the Certificate” section above.
Are CPD points or PDUs provided?
Where the accreditation body issues CPD/PDU points, the certificate of attendance will state the eligible amount.
How can I register?
Click “Request training” at the top. A verified provider will reach out to confirm seats, schedule, and invoicing.
Are there group discounts?
Yes — discounts are available for cohorts of 3 or more from the same organization. Submit a quote request to see the group rate.