MLOps Engineering on AWS Course in Sweden
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
4.78/53658 EnrolledEnrolled Learners
4.78/53658 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:
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
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
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Frequently asked questions
Our MLOps Engineering on AWS Course in Sweden is an expert-level course that trains professionals to automate machine learning operations with AWS services such as SageMaker, Lambda, and Step Functions. The course handles model deployment, pipeline automation, monitoring, and governance through the MLOps maturity model. It prepares learners to develop scalable, secure, and production-grade ML systems, facilitating an easy transition from experimentation to deployment.
While typical ML courses target model development, our course targets deployment, monitoring, versioning, and governance with AWS. It prepares professionals to operationalize ML models in production-ready enterprise environments—bridging the experimentation-deployment gap.
The course has a modular, hands-on design consisting of instructor-led classes, lab work, and live scenarios. It combines theoretical knowledge with practical application so that students are able to create, track, and grow ML solutions efficiently. Live sessions and case studies assist in bridging theory and practice.
By finishing the MLOps Engineering on AWS Course, you will acquire skills in automating ML workflows and deploying models at scale. These skill greatly improves your employment prospects in sectors dependent on machine learning, like tech, e-commerce, finance, and healthcare. The course will also prepare you for senior positions in managing end-to-end ML pipelines and developing AI solutions at scale within production environments.
MLOps Engineering on AWS Course Completion increases your power to automate, observe, and protect ML systems at scale. The certification confirms your skills for positions such as:
- MLOps Engineer
- Machine Learning Engineer
- DevOps Engineer
- AI Solutions Architect
You're in demand as an expert for organizations deploying large-scale ML systems.
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