MLOps Engineering on AWS Course in South Africa

Upcoming sessions

Curriculum

• Processes • People • Technology • Security and governance • MLOps maturity model

• Bringing MLOps to experimentation • Setting up the ML experimentation environment • Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio • Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog • Workbook: Initial MLOps

• Managing data for MLOps • Version control of ML models • Code repositories in ML Module 4: Repeatable MLOps: Orchestration • ML pipelines • Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines

• End-to-end orchestration with AWS Step Functions • Hands-On Lab: Automating a Workflow with Step Functions • End-to-end orchestration with SageMaker Projects • Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects • Using third-party tools for repeatability • Demonstration: Exploring Human-in-the-Loop During Inference • Governance and security • Demonstration: Exploring Security Best Practices for SageMaker • Workbook: Repeatable MLOps

• Scaling and multi-account strategies • Testing and traffic-shifting • Demonstration: Using SageMaker Inference Recommender • Hands-On Lab: Testing Model Variants • Hands-On Lab: Shifting Traffic • Workbook: Multi-account strategies

• The importance of monitoring in ML • Hands-On Lab: Monitoring a Model for Data Drift • Operations considerations for model monitoring • Remediating problems identified by monitoring ML solutions • Workbook: Reliable MLOps • Hands-On Lab: Building and Troubleshooting an ML Pipeline

Meet your Trainer

Our Trainers

Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.

We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.

Our Trainers

Learning Outcomes

Upon finishing the training, you will:

1

Prepare participants to manage ML operations using AWS tools like SageMaker and Kubernetes for streamlined workflows

2

Gain hands-on experience in automating ML workflows, model deployment, and continuous monitoring

3

Develop expertise in securing ML models, scaling solutions, and integrating human-in-the-loop for model reviews

4

Understand the MLOps maturity model and adopt efficient deployment practices for scalable ML solutions

5

Equip learners with the skills to monitor models, detect data drift, and implement security best practices

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Learners Point Certificate

Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.

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KHDA Certificate

Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.

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Frequently asked questions

Our MLOps Engineering on AWS course in South Africa trains professionals to apply MLOps best practices with AWS products like SageMaker, Kubernetes, and AWS Step Functions. Automating machine learning pipelines, deploying models, and monitoring their performance in production are topics covered in this intermediate-level course. It emphasizes guaranteeing the scalability, security, and governance of ML solutions to allow data scientists and DevOps engineers to manage ML operations at scale in an efficient manner.

Our course encompasses the end-to-end MLOps life cycle on AWS—from experimentation environment building to model deployment at scale. Topics covered are SageMaker Pipelines, Step Functions, versioning of models, security & compliance, and real-time monitoring. The training also explores performance tuning, human-in-the-loop feedback reviews, and governance best practices.

Our course is AWS-specific, industry-oriented, and practical. It specifically combines cloud engineering, machine learning, and DevOps best practices. You learn with actual AWS tools and work on real-world examples—positioning you for high-growth positions in AI and cloud infrastructure.

This training equips you for the following jobs:

MLOps Engineer
Cloud ML Architect
Machine Learning Engineer
DevOps for AI/ML
DataOps Specialist

You may also consider leadership roles in AI infrastructure, governance, and automation in tech-oriented firms.

Industries that hire certified MLOps experts are:

IT
Healthcare
Financial Services
E-commerce
Telecom
Government Agencies

Such industries appreciate the skill to scale AI operations effectively and securely on cloud platforms such as AWS.

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