Live Online Training
MLOps & AI in Production Course
Twelve weeks of live instruction on the engineering discipline that separates ML research from ML that actually ships. CI/CD for ML, Docker and Kubernetes, AWS SageMaker, Azure ML, GCP Vertex AI, monitoring and drift detection. Built for ML engineers ready to take work into production.
Live Online Training
AWS, Azure & GCP
Kubernetes for ML
Production Engineering
Trusted by Learners & Professionals from Top Companies
WHY LEARN MLOPS TODAY?
Where ML Investment Turns Into ML Returns
- Most models built never make it to production — MLOps is the discipline that fixes that
- MLOps engineers are among the most consistently in-demand US technical roles
- Learn CI/CD, Docker, Kubernetes, and all three major clouds in one program
- Cloud lab environments included — no separate AWS/Azure/GCP account required
- The natural follow-on after the ML Bootcamp for production-focused practitioners
WHAT YOU'LL LEARN
CI/CD for Machine Learning
Data & Model Versioning
Docker for ML Workloads
Kubernetes for ML
AWS SageMaker
Azure ML & GCP Vertex AI
Monitoring & Drift Detection
Feature Stores in Production
TOOLS & PLATFORMS YOU'LL MASTER
Docker
Container fundamentals for ML
Kubernetes
Orchestration for production ML
MLflow
Open-source ML lifecycle management
DVC
Data version control for reproducibility
Weights & Biases
Experiment tracking
AWS SageMaker
Amazon's managed ML platform
Azure Machine Learning
Microsoft's ML platform
Google Vertex AI
GCP's ML platform
FastAPI
API framework for model serving
GitHub Actions
CI/CD for ML pipelines
Terraform
Infrastructure as code
Great Expectations
Data quality monitoring
COURSE CURRICULUM
01
02
03
04
05
Week 1
Reproducibility & CI/CD
- Reproducibility for ML in practice
- Git workflows for ML projects
- Testing ML code
- GitHub Actions for ML
- DVC for data versioning
Week 4
Docker & Kubernetes
- Docker for ML workloads
- Multi-stage builds
- Kubernetes fundamentals
- Deployment patterns
- GPU workloads on K8s
Week 6
Model Serving
- FastAPI for ML services
- TorchServe and TF Serving
- KServe and BentoML
- A/B testing
- Canary deployments
Week 8
Cloud Platforms
- AWS SageMaker deep dive
- Azure ML in depth
- GCP Vertex AI
- Feature stores in production
- Model registries
Week 11
Monitoring & Drift
- Data, concept, and prediction drift
- Production logging for ML
- Performance monitoring
- Retraining strategies
- Incident response for ML
What You Will Learn
The Twelve-Week Curriculum
The curriculum is organized into four arcs across twelve weeks, covering the full lifecycle of a production ML system from CI/CD through deployment, monitoring, and maintenance.
The first arc establishes the engineering foundation that production ML rests on. Most ML practitioners come from notebook-based workflows that are not reproducible, are not testable, and do not scale to production.
Topics include reproducibility for ML (harder than in regular software), Git workflows for ML projects, testing ML code including stochastic outputs, CI/CD for machine learning (GitHub Actions, GitLab CI), DVC and data versioning, and experiment tracking with MLflow and Weights & Biases.
The arc ends with the first project: a complete ML training pipeline with CI/CD, experiment tracking, and data versioning.
You will Explore
Reproducibility in Practice
Git Workflows for ML
CI/CD Pipelines
Experiment Tracking
The second arc covers the deployment infrastructure that production ML systems run on. The course is opinionated about the value of containerization and orchestration for ML work and teaches both at production depth.
Topics include Docker for ML (with attention to large images, dependency complexity, GPU support); Kubernetes fundamentals for ML engineers; deployment patterns (batch, real-time, async); model serving frameworks (FastAPI, TorchServe, TensorFlow Serving, KServe, BentoML); A/B testing and gradual rollout patterns; and GPU workloads in Kubernetes.
The arc ends with the second project: a containerized ML service deployed to Kubernetes with proper observability and rollback capability.
You will Explore
Docker for ML
Kubernetes Fundamentals
Deployment Patterns
Model Serving Frameworks
The third arc covers the managed ML platforms on AWS, Azure, and GCP. Most US organizations build their ML platforms on one of these three clouds, and practitioner familiarity with the major managed services has become an expected skill.
Topics include AWS SageMaker in depth (Training, Endpoints, Pipelines, Feature Store, Model Registry); Azure Machine Learning with the broader Azure integration; Google Cloud Vertex AI; choosing between managed and self-hosted; feature stores in production (Feast, Tecton, managed options); and model registries and artifact management.
This arc is heavily hands-on. Learners work in real cloud accounts (with cost-controlled lab environments provided).
You will Explore
AWS SageMaker
Azure ML
GCP Vertex AI
Feature Stores
The final arc covers what happens after deployment — the monitoring, drift detection, retraining, and incident response work that determines whether deployed models actually deliver sustained value.
Topics include model monitoring in depth (distinguishing data, concept, and prediction drift); production logging for ML; performance monitoring and SLOs; data quality monitoring with Great Expectations; retraining strategies (triggered, scheduled, continuous); incident response for ML; and cost management.
Model Drift Detection
Data Quality Monitoring
Retraining Strategies
Cost Management
The final two weeks include the capstone project.
Capstone Project
Build • Deploy • Monitor
A complete ML pipeline from data ingestion through deployment, monitoring, and retraining. Recent capstones: fraud detection with online monitoring, recommendation systems with A/B testing infrastructure, forecasting systems with drift detection and triggered retraining.
By the end of twelve weeks, you'll have shipped a real production ML system — the kind hiring teams for MLOps roles specifically look for.
REAL-WORLD PROJECTS
Reproducible ML Pipeline
CI/CD, experiment tracking, and data versioning end-to-end.
Containerized ML Service
Docker + Kubernetes deployment with proper observability.
SageMaker Deployment
Full production deployment on AWS with monitoring.
Drift Detection System
Real-world monitoring for a deployed model.
Full Production Capstone
End-to-end pipeline with retraining and incident response.
Course Format and Delivery
The course is delivered fully online through live, instructor-led sessions taught by working MLOps practitioners from US technology companies.
01
Schedule
Three live sessions per week over twelve weeks, in evening and weekend slots that accommodate working professionals across US time zones.
- Each session runs approximately 90 minutes.
02
Cloud Lab Environments
Cost-controlled cloud lab environments on AWS, Azure, and GCP. No separate cloud accounts required.
- Real clouds. No extra costs.
03
Live Infrastructure Work
Sessions blend instruction with hands-on practice in real command lines, Kubernetes clusters, and cloud consoles.
- Learn by shipping.
04
Code Reviews
Major project submissions and the capstone receive direct code review with written feedback.
- Ship-quality feedback.
05
Cohort Discussion & Office Hours
Active discussion channel and weekly office hours for architectural conversations.
- Peer learning at production depth.
Prerequisites And Technical Requirements
This is an advanced course. Prerequisites are real:
ML foundation — training models, evaluation workflows (ML Bootcamp recommended)
Strong Python including packaging and dependencies
Comfort with Git, command line, and basic Linux
Laptop with 16 GB RAM, modern OS, stable internet
Why Mlops Skills Matter In The Us Job Market
The Bridge From Model to Business Value.
Most models built never make it to production. MLOps engineers are the practitioners who close that gap. The demand reflects the reality: every organization investing in ML needs someone who can take work from notebook to reliable system.
Consistent, Steady Demand.
MLOps engineer, ML platform engineer, and ML infrastructure engineer roles are among the most consistently in-demand US technical positions. The demand is less cyclical than pure data science because it reflects operational necessity.
Compensation That Reflects the Scarcity.
Entry-level MLOps and ML platform roles commonly start in the low-to-mid six-figure range. Mid-career reaches high six-figures. Senior practitioners at large employers and AI-focused companies regularly reach into upper six-figure and seven-figure territory.
Learn From Industry Leaders
Michael Anderson
Lead Instructor — AI & Cloud
- AWS Certified
- 10+ Years Industry Experience
- Published AI Researcher
Senior AI engineer with extensive experience designing machine learning platforms and deploying enterprise AI solutions.
David Miller
Cloud & MLOps Instructor
- Cloud Architect
- Google Cloud Certified
- Enterprise MLOps Specialist
Cloud Architect specializing in scalable AI infrastructure, MLOps automation, and multi-cloud deployments for enterprise.
INDUSTRY RECOGNIZED CERTIFICATE
Receive an industry-recognized certificate after successful course completion.
- Industry Recognized
- LinkedIn Shareable
- Verifiable Certificate
- Career Boost
Career Impact
What Our Learners Say
The AI for Data Analytics course gave me the exact tools needed to interpret complex data streams quickly. The practical framework for integrating modern AI directly into existing analytical workflows was immediately applicable.
The multi-agent simulations in the Advanced Python for LLM & Multi Agent Systems course completely transformed our engineering team's approach to complex system design. Exceptional depth and real-world utility.
Enrolling our product team in Agentic AI & Multi Agent Autonomous Systems provided the strategic edge we were missing. The hands-on project structures made deploying autonomous agents far more straightforward.
After taking NLP with LLMs, I was able to fine-tune and deploy a custom retrieval-augmented model in weeks. The instructors cover production-grade deployment strategies that you rarely find in standard online courses.
The Executive Diploma in Deep AI & Cloud Intelligence offered a clear, high-level strategic overview without glossing over technical rigor. It helped our leadership team align our enterprise cloud architecture with modern AI solutions.
Bridging the gap between prototype models and production infrastructure can be tough, but MLOps & AI in Production laid out the best practices clearly. Highly recommended for any engineering group scaling AI services.
Ready To Ship Production ML?
Join a cohort of engineers taking ML work from notebook to production.
Frequently Asked Questions
No. The course teaches Docker, Kubernetes, and cloud platforms from a working starting point, paced for ML practitioners without prior DevOps depth. Learners with a DevOps background will move faster through the infrastructure arcs.
Major clouds have converged enough on ML offerings that proficiency in any one transfers reasonably to the others. The course covers all three at working depth. Most learners develop a primary cloud with working familiarity in the other two.
General patterns apply to LLM deployment, but the specific challenges (large artifacts, GPU memory, inference optimization, streaming) are not the primary focus. Combine this with Advanced Python for LLM & Multi Agent Systems and NLP with LLMs for LLM production work.
MLOps is built on DevOps foundations but adds ML-specific concerns: data versioning, model artifact handling, training pipelines, drift monitoring. DevOps practitioners need to add the ML side. ML practitioners need to add the DevOps foundations.
No. The course provides cost-controlled lab environments on all three cloud platforms.
Major cloud ML services and core MLOps tools (Kubernetes, Docker, MLflow, DVC) are stable enough that foundational material remains valid. Specific offerings are updated each cohort.
Sequential recommended, not parallel. Both are 3–4 sessions per week with substantial workload.
Yes, one of the most common profiles. Software engineers with strong infrastructure backgrounds need to add ML-specific concerns and ML-adjacent tooling.
- Live Instruction
- AWS, Azure & GCP Labs
- Kubernetes Deep Dive
- Production Capstone