MLOps Engineering on AWS Course in Bahrain
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
4.8/54.8/5Overview
What our training includes:
- 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, optimise, 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
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.
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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Frequently asked questions
Our MLOps Engineering on AWS course in Bahrain teaches professionals how to implement MLOps best practices using AWS tools such as SageMaker, Kubernetes, and AWS Step Functions. This intermediate-level course covers automating machine learning workflows, deploying models, and monitoring their performance in production environments. It focuses on ensuring the scalability, security, and governance of ML solutions, enabling data scientists and DevOps engineers to efficiently manage ML operations at scale.
Yes, our course will teach you how to automate an entire retraining cycle by using AWS SageMaker Pipelines, event triggers, and model versioning strategies. You will learn how to detect data drift, evaluate performance metrics, and trigger retraining workflows automatically, which enables the models to stay accurate and production-ready without constant manual oversight. This helps the ML teams in Bahrain maintain faster workflows and more reliable AI systems.
Certainly, the training in essential MLOps security practices covers role-based access control, encrypted data handling, audit trails, and model governance. You will also learn how to deploy models securely using SageMaker, EKS, and AWS IAM. These practices ensure your ML solutions comply with strict security and regulatory standards within Bahrain's financial sector landscape.
From testing to repeatable and dependable deployment in production, the MLOps maturity model aids in assessing the various phases of ML workflow development. It offers a structure for evaluating and enhancing MLOps procedures.
The model will help organisations understand their current position, whether in manual, partial automation, or full automation stages of ML. It will also guide teams on what to improve next, such as versioning, monitoring, governance, or automation, to achieve higher reliability and efficiency in ML operations.
After completing the MLOps Engineering on AWS Course, you can pursue several rewarding career paths:
1. MLOps Engineer
2. Machine Learning Engineer
3. DevOps Engineer
4. AI Solutions Architect
These roles are in high demand across industries like finance, tech, and healthcare.
Our course in Bahrain focuses specifically on AWS tools, providing hands-on experience with the platform’s powerful services like SageMaker and Step Functions. Unlike other MLOps courses, it emphasises end-to-end deployment and monitoring in a production environment, ensuring you gain practical, industry-relevant skills.
Yes. Our training equips you to apply MLOps best practices to practical projects, maximising model deployment and monitoring while guaranteeing production environments' scalability and security.
You will use AWS tools such as SageMaker, CloudWatch, and Lambda to create pipelines that reflect real business use cases. By the end of this course, you will be able to take ML models from experimentation into production, maintain them over time, and collaborate effectively with data science and engineering teams.
Yes, our certification program is available in locations across GCC regions. We offer comprehensive instruction and training globally, which helps students to gain this important certification.
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