MLOps Engineering on AWS Course in South Africa

Transform ML from Experimentation to Seamless Deployment

Streamline Orchestration, Scaling, and Version Control

Unlock Top Security and Governance Practices for ML

Drive Reliable, High-Quality ML Operations with the MLOps Maturity Model

Convenient and hassle-free payment plans

Flexible learning options

4.78/5

3658 EnrolledEnrolled Learners

4.78/5

3658 EnrolledEnrolled Learners

Overview

What you will learn:

Learn MLOps with AWS tools like SageMaker, Lambda, and S3 for seamless ML operations
Gain hands-on experience in building, deploying, and monitoring scalable ML models
Master automating ML workflows, model versioning, and efficient retraining techniques
Prepare for implementing production-level MLOps strategies in real-world environments
Understand security, governance, and compliance best practices in machine learning
Acquire skills to scale, optimize, and manage complex ML workloads effectively
Get ready for certification preparation in AWS MLOps and elevate your expertise

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.

Learning Outcomes

Upon finishing the training, you will:

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

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

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

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

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

Ready to get started?

Learners Point Certificate

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

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.

Overall ratings by our students

Related courses

Learn now, pay later

Dive into your course now and pay in installments

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: