MLOPS ENGINEERING ON AWS

MLOps Engineering on AWS extends DevOps practices to machine learning (ML) workflows, focusing on building, training, and deploying ML models ... Show more

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AcumenITTrainingInc

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Course Overview

Introduction

This course offers a comprehensive guide to implementing MLOps practices on AWS, helping organizations automate and streamline the deployment and maintenance of machine learning models at scale. It’s critical for businesses aiming to integrate AI-driven solutions into their operations effectively.

Business Relevance

By adopting MLOps, organizations can ensure faster deployment, more robust model management, and more efficient collaboration between data scientists and operations teams. This course enhances IT efficiency and aligns machine learning initiatives with business goals.

Target Area

This training supports the area of Cloud Management by leveraging AWS services to ensure seamless and scalable MLOps pipelines, boosting the reliability of AI solutions in production environments.

What You’ll Learn & Who Should Enroll

Key Topics Covered:

MLOps on AWS: Understanding the MLOps lifecycle, key AWS services, and automation best practices.
AWS SageMaker Integration: Using Amazon SageMaker for model development, deployment, and management.
Version Control and Automation: Implementing CI/CD pipelines for machine learning models.
Model Monitoring and Management: Using AWS tools to monitor and manage deployed models.
Security and Compliance in MLOps: Ensuring secure and compliant machine learning practices on AWS.

Ideal Participants:

This course is designed for:

Data Scientists & Machine Learning Engineers: Learn how to integrate MLOps practices into the ML model lifecycle, from development to deployment and maintenance.
DevOps Engineers: Gain expertise in automating machine learning workflows and implementing continuous integration and continuous delivery (CI/CD) pipelines for ML models.
Cloud Engineers: Expand your knowledge of cloud-based MLOps tools and learn to manage scalable and secure machine learning environments on AWS.
IT Operations Teams: Learn how to monitor and manage machine learning models in production using AWS services to ensure optimal performance and security.

Business Applications & Next Steps

Key Business Impact:

Enhanced Security & Compliance: Safeguard ML models and data by adhering to security protocols and industry standards.
Improved IT Governance: Streamline the management of ML models, ensuring transparency and efficiency.
Operational Efficiency: Automate workflows to reduce manual intervention, accelerating model deployment and monitoring.

Next-Level Training:

To further build expertise, consider:

AWS Certified Machine Learning – Specialty: A deep dive into advanced machine learning concepts and best practices on AWS.
AWS Certified DevOps Engineer – Professional: Expand your knowledge of DevOps practices that support scalable, automated ML pipelines.

Why Choose Acumen IT Training?

Enterprise-Focused Curriculum: Designed specifically for the challenges and demands of corporate IT environments.
Qualified-Led Training: Learn from seasoned industry professionals with extensive real-world experience.
Business-Driven Learning: Benefit from practical applications that directly impact your organization’s performance.
Flexible Training Options: Choose from Online, Hybrid Training, Instructor-Led On-Site (at your location or ours), and Corporate Group Sessions.

For the full course outline, schedules, and private corporate training inquiries, contact us at Acumen IT Training.

Course Outline

COURSE OBJECTIVES

Describe machine learning operations
Understand the key differences between DevOps and MLOps
Describe the machine learning workflow
Discuss the importance of communications in MLOps
Explain end-to-end options for automation of ML workflows
List key Amazon SageMaker features for MLOps automation
Build an automated ML process that builds, trains, tests, and deploys models
Build an automated ML process that retrains the model based on change(s) to the model code
Identify elements and important steps in the deployment process
Describe items that might be included in a model package, and their use in training or inference
Recognize Amazon SageMaker options for selecting models for deployment, including support for ML frameworks and built-in algorithms or bring-your-own-models
Differentiate scaling in machine learning from scaling in other applications
Determine when to use different approaches to inference
Discuss deployment strategies, benefits, challenges, and typical use cases
Describe the challenges when deploying machine learning to edge devices
Recognize important Amazon SageMaker features that are relevant to deployment and inference
Describe why monitoring is important
Detect data drifts in the underlying input data
Demonstrate how to monitor ML models for bias
Explain how to monitor model resource consumption and latency
Discuss how to integrate human-in-the-loop reviews of model results in production

TRAINING INCLUSIONS

Comprehensive training materials and reference guides.
Hands-on lab exercises with real-world AWS MLOps scenarios.
MLOps Engineering on AWS Certificate of Training Completion.
Access to AWS machine learning and automation tools during training.
30 Days Post-Training Support.

COURSE OUTLINE

Module 1: Introduction to MLOps

Module 2: MLOps Development

Module 3: MLOps Deployment

Module 4: Model Monitoring and Operations

Module 5: Wrap-up

For FULL COURSE OUTLINE, please contact us.
Inquire now for schedules and private class bookings.

FAQs

What is MLOps Engineering on AWS training?
This course covers automating, deploying, and monitoring machine learning (ML) models on AWS using MLOps best practices.
Who should take this course?
Data scientists, ML engineers, DevOps professionals, and software developers interested in streamlining ML workflows.
Do I need prior experience?
Basic knowledge of machine learning and DevOps is recommended.
What AWS services will I learn?
The course covers Amazon SageMaker, AWS Lambda, Amazon S3, AWS Step Functions, AWS CodePipeline, and Amazon CloudWatch.
How long is the training?
The training typically lasts 3 to 5 days, depending on the format.
Is this training available online?
Yes, it is available in both online and in-person formats.
Does this training include an official AWS certification?
No, but it prepares you for AWS machine learning and DevOps certifications.
How will this training help my career?
It enhances your skills in automating ML pipelines, making you more valuable in AI and cloud-based roles.
What real-world problems can I solve with this training?
You’ll learn how to automate ML workflows, improve deployment efficiency, and scale AI solutions.
How does AWS simplify MLOps?
AWS provides pre-built tools, automation, and monitoring services to streamline ML development and deployment.

Real-World Applications of MLOps Engineering on AWS

✅ Case Study 1: Automating ML Model Deployment for Financial Services

Challenge: A financial company struggled with manual deployment of ML models, leading to delays and errors.

Solution: They implemented AWS Step Functions and Amazon SageMaker Pipelines to automate model training and deployment.

Result:
✔ 80% reduction in deployment time
✔ Improved model accuracy and performance monitoring
✔ Faster fraud detection and risk assessment

✅ Case Study 2: Real-Time AI Predictions for E-commerce

Challenge: A retail company wanted to provide real-time product recommendations based on customer behavior.

Solution: They integrated Amazon SageMaker and AWS Lambda to automate recommendation model updates.

Result:
✔ 35% increase in sales due to personalized recommendations
✔ Faster real-time updates without manual intervention
✔ Improved customer satisfaction and engagement

✅ Use Case 1: Scalable ML Pipelines for Healthcare AI

Hospitals use AWS MLOps to train and deploy AI models for medical imaging and diagnosis.

✔ Faster disease detection with AI-assisted scans
✔ HIPAA-compliant data security
✔ Reduced manual workload for radiologists

✅ Use Case 2: Automated Defect Detection in Manufacturing

Factories deploy computer vision models on AWS to detect product defects in real-time.

✔ 50% improvement in quality control
✔ Reduced waste and production costs
✔ Improved operational efficiency

Why These Case Studies Matter for You

By taking this training, you’ll gain practical MLOps skills, allowing you to automate, deploy, and monitor AI solutions at scale. Whether you’re in finance, healthcare, retail, or manufacturing, AWS MLOps can help streamline AI operations.

🔗 Enroll today and take the next step in your machine learning automation journey with AWS!

Testimonials: What Professionals Say About Our MLOps Engineering on AWS Training

⭐ ⭐ ⭐ ⭐ ⭐ “Made My ML Workflows More Efficient!”
“I learned how to automate model training and deployment, which saved me hours of work!”
— Kevin R., Machine Learning Engineer

⭐ ⭐ ⭐ ⭐ ⭐ “Perfect for Scaling AI Solutions!”
“This course helped my team set up a robust MLOps pipeline on AWS, improving deployment speed.”
— Catherine L., Data Scientist

⭐ ⭐ ⭐ ⭐ ⭐ “A Must-Have for AI Professionals!”
“MLOps on AWS is a game-changer. This training showed me the best practices for scaling ML models!”
— Joel M., AI Developer