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Catalog / All levels / Machine Learning Engineering – MLOps and Model Deployment Skills with 10 Exams
Mammoth Club All levels 6 sections 63 lectures

Machine Learning Engineering – MLOps and Model Deployment Skills with 10 Exams

These tutorials combine human expertise, AI innovation and human editor oversight with features like chat support and quizzes. Building a model is just the start—getting it into production is where machine learning becomes impactful.

MC
Created by Team Mammoth
·
63 lectures
01
Skill level
All levels
02
Sections
6
03
Lectures
63
04
Instructor
Team Mammoth
What's inside

This course includes.

✓
6
Sections
✓
17
Quizzes
✓
Certificate of completion
Included
✓
Mobile and desktop access
Included
✓
AI learning assistance
Included
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Course content

Curriculum & lectures.

6 sections · 63 lectures
+ 01 MLOps Foundations and Lifecycle Management 17 lectures
01 Definition and importance of MLOps Locked
02 MLOps vs. DevOps: Key Differences Locked
03 Business Value and ROI of MLOps Practices Locked
04 MLOps Roles and Responsibilities Locked
05 Problem definition and requirements gathering Locked
06 Data Collection and Validation Locked
07 Model Development and Experimentation Locked
08 Model Evaluation and Selection Locked
09 Deployment and Monitoring Phases Locked
10 MLOps Maturity Levels - Level 0: Manual processes Locked
11 MLOps Maturity Levels -Level 1: ML Pipeline Automation Locked
12 MLOps Maturity Levels - The Transition Challenge to Level 2 Locked
13 Assessment Framework for MLOps Maturity Locked
14 MLflow Tracking Fundamentals Locked
15 Logging Parameters, Metrics, and Artifacts Locked
16 Comparing Experiments and Model Versions Locked
17 MLflow Projects for Reproducible Runs Locked
+ 02 Data Engineering for MLOps 16 lectures
01 ETL vs. ELT Methodologies Locked
02 Batch vs. Streaming Data Processing Locked
03 Data Pipeline Orchestration Patterns Locked
04 Error Handling and Recovery Strategies Locked
05 Data Quality Dimensions and Metrics Locked
06 Data Validation Frameworks Locked
07 Great Expectations for Data Testing Locked
08 Automated Data Quality Monitoring Locked
09 The Foundation of Production Feature Management Locked
10 Online vs. Offline Feature Serving Locked
11 Feature Versioning and Lineage Locked
12 Feature Pipeline Automation Locked
13 Understanding Version Control for Datasets Locked
14 Data Pipeline Automation with DVC Locked
15 Reproducible Data Workflows Locked
16 Integration with Git Workflows Locked
+ 03 Containerization and Infrastructure 15 lectures
01 Docker Fundamentals for ML Locked
02 Containers: Running Instances of ML Services Locked
03 Multi-Stage Builds: Optimizing ML Container Images Locked
04 Docker Compose for Multi-Service ML Applications Locked
05 Breaking Down Monolithic ML Applications Locked
06 Service Communication Patterns for ML Systems Locked
07 API Design for ML Services Locked
08 Load Balancing and Scaling Strategies Locked
09 Infrastructure Automation Principles Locked
10 Configuration Management Locked
11 Environment Consistency Locked
12 Environment Isolation and Dependency Management Locked
13 Virtual Environments vs. Containers Locked
14 Resource Allocation and Optimization Locked
15 GPU Acceleration for ML Workloads Locked
+ 04 CI/CD and Automated Testing for ML 7 lectures
01 Git Workflows for ML Projects Locked
02 Branch Strategies for Model Development Locked
03 Collaborative Development Practices Locked
04 Code Review Processes for ML Code Locked
05 Testing Machine Learning Code Locked
06 CI/CD Pipeline Design Locked
07 Automated Model Validation Locked
+ 05 Model Deployment Strategies 4 lectures
01 Deployment Patterns Locked
02 Model Serving Frameworks Locked
03 Scalability and Performance Locked
04 Model Registry and Versioning Locked
+ 06 Monitoring, Maintenance, and Production Operations 4 lectures
01 ML Model Monitoring Locked
02 Drift Detection and Management Locked
03 Automated Retraining Locked
04 MLOps Governance Locked
Description

About this course.

This course explores the engineering side of ML, from managing pipelines to deploying models in real-world environments.


✅ Explore workflows for model deployment and monitoring

✅ Learn MLOps practices that ensure reliability at scale

✅ Automate processes for training, testing, and delivery

✅ Reinforce concepts with 10 structured exams


Machine learning engineering connects research with real-world impact, ensuring that models remain accurate, scalable, and useful beyond experimentation.


🎁 Learn to bridge the gap between models and production systems.

Ready to start building?

These tutorials combine human expertise, AI innovation and human editor oversight with features like chat support and quizzes. Building a model is just the start—getting it into production is where machine learning becomes impactful.

Buy lifetime access →