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course | Advanced AI/ML Deployment and Platform Engineering

Join the Advanced AI/ML ... course taught in English

DIGTR-3169 | Advanced AI/ML Deployment and Platform Engineering

Course Sector : Digital Transformation and Innovation

Course dates

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Duration	Date from	Date to	Course Venue	Course fees	Book a course
5 Days	13/09/2026	17/09/2026	Online	$2,150	Book now
5 Days	23/11/2026	27/11/2026	Dubai	$4,250	Book now
5 Days	29/03/2027	02/04/2027	Lisbon	$4,950	Book now
5 Days	11/04/2027	15/04/2027	Riyadh	$4,250	Book now

Course Introduction

Deploying AI and ML applications in production requires deep technical expertise in platform engineering, CI/CD pipelines, and cloud-native orchestration. This program covers the design, deployment, and maintenance of AI/ML systems at scale, emphasizing platform engineering, Kubernetes orchestration, and CI/CD for AI/ML workloads.

Participants will gain a comprehensive understanding of production-ready AI/ML pipelines, ensuring reliability, scalability, and maintainability of models and applications.

Course objective

Design and implement robust AI/ML production pipelines.
Apply platform engineering principles to deploy scalable ML workloads.
Use Kubernetes for AI/ML orchestration and workload management.
Implement CI/CD pipelines for AI/ML application lifecycle management.
Optimize AI/ML deployments for performance, reliability, and scalability.

Course audience

Machine Learning Engineers
AI Engineers / Applied AI Engineers
MLOps Engineers

Course Outline | DAY 01

Platform Engineering for AI/ML Workloads

Fundamentals of platform engineering
Designing scalable, modular AI/ML platforms
Resource management and orchestration considerations
Integration with enterprise infrastructure
Platform monitoring and observability

Course Outline | Day 02

Kubernetes for AI/ML Deployment

Kubernetes architecture and core concepts
Deploying AI/ML workloads using pods, deployments, and services
ConfigMaps, secrets, and resource management
Scaling workloads and managing clusters
Security best practices for AI/ML in Kubernetes

Course Outline | Day 03

CI/CD for AI/ML Applications

Continuous integration pipelines for model training
Continuous deployment for AI applications
Automated testing and validation for ML models
Version control and reproducibility in ML workflows
Deployment monitoring and rollback strategies

Course Outline | Day 04

Enterprise MLOps

MLOps lifecycle and best practices
Model versioning, registry, and reproducibility
Monitoring model performance and detecting drift
Automation of retraining and deployment cycles
Governance and compliance in MLOps

Course Outline | Day 05

Advanced AI/ML Deployment Strategies

Cloud-native vs hybrid deployment models
Multi-model orchestration and inference pipelines
Optimization for low-latency and high-throughput workloads
Case studies: production AI/ML systems at scale
Capstone discussion: designing an enterprise AI/ML deployment architecture

Course Certificates

BOOST’s Professional Attendance Certificate “BPAC”

BPAC is always given to the delegates after completing the training course,and depends on their attendance of the program at a rate of no less than 80%,besides their active participation and engagement during the program sessions.

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An ML Platform Engineer builds and maintains the infrastructure, tools, and workflows that enable machine learning models to be developed, deployed, monitored, and scaled efficiently. They typically work with MLOps, cloud platforms, Kubernetes, Docker, CI/CD, and data pipelines.

Yes, you can learn the fundamentals of machine learning in about three months with consistent study and practice. During this time, you can cover Python, statistics, supervised and unsupervised learning, scikit-learn, and basic model evaluation. Becoming job-ready generally requires additional hands-on projects and experience.

Yes. Machine Learning is one of the highest-paying technology fields. Roles such as Machine Learning Engineer, AI Engineer, Applied Scientist, and MLOps Engineer are in strong demand and often offer competitive salaries, especially for professionals with practical experience and cloud or AI deployment skills.

An AI Platform Engineer designs, builds, and manages the infrastructure that supports AI applications. Their responsibilities include developing scalable AI platforms, integrating cloud services, automating model deployment, managing AI pipelines, and ensuring the reliability, security, and performance of AI systems.

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Since 2001, we’ve been at the forefront of professional training in the Middle East — shaping the future of learning and development one success story at a time. With a vision rooted in innovation and excellence, we help individuals, teams, and organizations reach their highest potential through integrated, future-ready training solutions. Our comprehensive programs combine global best practices with local insights, empowering people to grow, lead, and make a lasting impact in their industries.

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