MLOps & AI DevOps

MLOps in ProductionCI/CD for AI Models

36h

14 modules

+500 apprenants

Docker & K8sMLflow & DVCFastAPI ServingGrafana Monitoring

Reserve my spot View full program

Investment

1 790 DH

or 3x interest-free • CPF eligible

36h of expert content (videos + interactive labs)
Dedicated cloud environment for exercises (credits included)
Infrastructure as Code (Terraform) to reproduce the full stack
Individual mentorship — 3 sessions with senior MLOps engineer
Access to private MDA MLOps community

Reserve my spot

30-day satisfaction guarantee or your money back

Full Program

6Modules

36hContent

Complete program

36h of content

|6 modules

Fundamentals & Intermediate

Modules 1–3

Module 1

MLOps Architecture Fundamentals

3h5 topics17%

MLOps maturity levels (0 → 3)

Reference end-to-end architecture

Tool overview: MLflow, DVC, Kubeflow, Seldon, BentoML

Architecture choices by team size and budget

Module 2

Data & Experiment Versioning

4h6 topics33%

DVC: versioning large-scale datasets

DVC integration with S3 / GCS / Azure Blob

MLflow Tracking: metrics, parameters, artifacts

MLflow Projects and reproducibility

Module 3★ Key

Automated ML Pipelines

5h6 topics50%

Prefect vs Metaflow vs Airflow for ML

Data pipeline: ingestion, validation, transformation

Parameterizable and reproducible training pipelines

Continuous Training: schedulers and strategies

Module 1

MLOps Architecture Fundamentals

3h5 topics17%

MLOps maturity levels (0 → 3)

Reference end-to-end architecture

Tool overview: MLflow, DVC, Kubeflow, Seldon, BentoML

Architecture choices by team size and budget

Module 2

Data & Experiment Versioning

4h6 topics33%

DVC: versioning large-scale datasets

DVC integration with S3 / GCS / Azure Blob

MLflow Tracking: metrics, parameters, artifacts

MLflow Projects and reproducibility

Module 3★ Key

Automated ML Pipelines

5h6 topics50%

Prefect vs Metaflow vs Airflow for ML

Data pipeline: ingestion, validation, transformation

Parameterizable and reproducible training pipelines

Continuous Training: schedulers and strategies

Module 1

MLOps Architecture Fundamentals

3h5 topics17%

MLOps maturity levels (0 → 3)

Reference end-to-end architecture

Tool overview: MLflow, DVC, Kubeflow, Seldon, BentoML

Architecture choices by team size and budget

Module 2

Data & Experiment Versioning

4h6 topics33%

DVC: versioning large-scale datasets

DVC integration with S3 / GCS / Azure Blob

MLflow Tracking: metrics, parameters, artifacts

MLflow Projects and reproducibility

Module 3★ Key

Automated ML Pipelines

5h6 topics50%

Prefect vs Metaflow vs Airflow for ML

Data pipeline: ingestion, validation, transformation

Parameterizable and reproducible training pipelines

Continuous Training: schedulers and strategies

Module 1

MLOps Architecture Fundamentals

3h5 topics17%

MLOps maturity levels (0 → 3)

Reference end-to-end architecture

Tool overview: MLflow, DVC, Kubeflow, Seldon, BentoML

Architecture choices by team size and budget

Module 2

Data & Experiment Versioning

4h6 topics33%

DVC: versioning large-scale datasets

DVC integration with S3 / GCS / Azure Blob

MLflow Tracking: metrics, parameters, artifacts

MLflow Projects and reproducibility

Module 3★ Key

Automated ML Pipelines

5h6 topics50%

Prefect vs Metaflow vs Airflow for ML

Data pipeline: ingestion, validation, transformation

Parameterizable and reproducible training pipelines

Continuous Training: schedulers and strategies

Module 1

MLOps Architecture Fundamentals

3h5 topics17%

MLOps maturity levels (0 → 3)

Reference end-to-end architecture

Tool overview: MLflow, DVC, Kubeflow, Seldon, BentoML

Architecture choices by team size and budget

Module 2

Data & Experiment Versioning

4h6 topics33%

DVC: versioning large-scale datasets

DVC integration with S3 / GCS / Azure Blob

MLflow Tracking: metrics, parameters, artifacts

MLflow Projects and reproducibility

Module 3★ Key

Automated ML Pipelines

5h6 topics50%

Prefect vs Metaflow vs Airflow for ML

Data pipeline: ingestion, validation, transformation

Parameterizable and reproducible training pipelines

Continuous Training: schedulers and strategies

Module 1

MLOps Architecture Fundamentals

3h5 topics17%

MLOps maturity levels (0 → 3)

Reference end-to-end architecture

Tool overview: MLflow, DVC, Kubeflow, Seldon, BentoML

Architecture choices by team size and budget

Module 2

Data & Experiment Versioning

4h6 topics33%

DVC: versioning large-scale datasets

DVC integration with S3 / GCS / Azure Blob

MLflow Tracking: metrics, parameters, artifacts

MLflow Projects and reproducibility

Module 3★ Key

Automated ML Pipelines

5h6 topics50%

Prefect vs Metaflow vs Airflow for ML

Data pipeline: ingestion, validation, transformation

Parameterizable and reproducible training pipelines

Continuous Training: schedulers and strategies

Module 1

MLOps Architecture Fundamentals

3h5 topics17%

MLOps maturity levels (0 → 3)

Reference end-to-end architecture

Tool overview: MLflow, DVC, Kubeflow, Seldon, BentoML

Architecture choices by team size and budget

Module 2

Data & Experiment Versioning

4h6 topics33%

DVC: versioning large-scale datasets

DVC integration with S3 / GCS / Azure Blob

MLflow Tracking: metrics, parameters, artifacts

MLflow Projects and reproducibility

Module 3★ Key

Automated ML Pipelines

5h6 topics50%

Prefect vs Metaflow vs Airflow for ML

Data pipeline: ingestion, validation, transformation

Parameterizable and reproducible training pipelines

Continuous Training: schedulers and strategies

Advanced & Deployment

Modules 4–6

Module 4

ML CI/CD

5h6 topics67%

GitHub Actions for ML workflows

Automated tests: data, models, performance

Model Registry and promotion (staging → production)

Secret management (Vault, AWS Secrets Manager)

Module 5

Serving & Production Infrastructure

6h7 topics83%

Kubernetes for data scientists

Seldon Core: deployment and multi-model serving

BentoML: simplified packaging and serving

Latency optimization: ONNX, TensorRT, quantization

Module 6★ Key

Monitoring, Observability & Alerting

5h7 topics100%

Model monitoring: performance, drift, bias

Evidently AI for data drift detection

Prometheus + Grafana stack for ML metrics

Structured logging and distributed tracing (OpenTelemetry)

Module 4

ML CI/CD

5h6 topics67%

GitHub Actions for ML workflows

Automated tests: data, models, performance

Model Registry and promotion (staging → production)

Secret management (Vault, AWS Secrets Manager)

Module 5

Serving & Production Infrastructure

6h7 topics83%

Kubernetes for data scientists

Seldon Core: deployment and multi-model serving

BentoML: simplified packaging and serving

Latency optimization: ONNX, TensorRT, quantization

Module 6★ Key

Monitoring, Observability & Alerting

5h7 topics100%

Model monitoring: performance, drift, bias

Evidently AI for data drift detection

Prometheus + Grafana stack for ML metrics

Structured logging and distributed tracing (OpenTelemetry)

Module 4

ML CI/CD

5h6 topics67%

GitHub Actions for ML workflows

Automated tests: data, models, performance

Model Registry and promotion (staging → production)

Secret management (Vault, AWS Secrets Manager)

Module 5

Serving & Production Infrastructure

6h7 topics83%

Kubernetes for data scientists

Seldon Core: deployment and multi-model serving

BentoML: simplified packaging and serving

Latency optimization: ONNX, TensorRT, quantization

Module 6★ Key

Monitoring, Observability & Alerting

5h7 topics100%

Model monitoring: performance, drift, bias

Evidently AI for data drift detection

Prometheus + Grafana stack for ML metrics

Structured logging and distributed tracing (OpenTelemetry)

Module 4

ML CI/CD

5h6 topics67%

GitHub Actions for ML workflows

Automated tests: data, models, performance

Model Registry and promotion (staging → production)

Secret management (Vault, AWS Secrets Manager)

Module 5

Serving & Production Infrastructure

6h7 topics83%

Kubernetes for data scientists

Seldon Core: deployment and multi-model serving

BentoML: simplified packaging and serving

Latency optimization: ONNX, TensorRT, quantization

Module 6★ Key

Monitoring, Observability & Alerting

5h7 topics100%

Model monitoring: performance, drift, bias

Evidently AI for data drift detection

Prometheus + Grafana stack for ML metrics

Structured logging and distributed tracing (OpenTelemetry)

Module 4

ML CI/CD

5h6 topics67%

GitHub Actions for ML workflows

Automated tests: data, models, performance

Model Registry and promotion (staging → production)

Secret management (Vault, AWS Secrets Manager)

Module 5

Serving & Production Infrastructure

6h7 topics83%

Kubernetes for data scientists

Seldon Core: deployment and multi-model serving

BentoML: simplified packaging and serving

Latency optimization: ONNX, TensorRT, quantization

Module 6★ Key

Monitoring, Observability & Alerting

5h7 topics100%

Model monitoring: performance, drift, bias

Evidently AI for data drift detection

Prometheus + Grafana stack for ML metrics

Structured logging and distributed tracing (OpenTelemetry)

Module 4

ML CI/CD

5h6 topics67%

GitHub Actions for ML workflows

Automated tests: data, models, performance

Model Registry and promotion (staging → production)

Secret management (Vault, AWS Secrets Manager)

Module 5

Serving & Production Infrastructure

6h7 topics83%

Kubernetes for data scientists

Seldon Core: deployment and multi-model serving

BentoML: simplified packaging and serving

Latency optimization: ONNX, TensorRT, quantization

Module 6★ Key

Monitoring, Observability & Alerting

5h7 topics100%

Model monitoring: performance, drift, bias

Evidently AI for data drift detection

Prometheus + Grafana stack for ML metrics

Structured logging and distributed tracing (OpenTelemetry)

Module 4

ML CI/CD

5h6 topics67%

GitHub Actions for ML workflows

Automated tests: data, models, performance

Model Registry and promotion (staging → production)

Secret management (Vault, AWS Secrets Manager)

Module 5

Serving & Production Infrastructure

6h7 topics83%

Kubernetes for data scientists

Seldon Core: deployment and multi-model serving

BentoML: simplified packaging and serving

Latency optimization: ONNX, TensorRT, quantization

Module 6★ Key

Monitoring, Observability & Alerting

5h7 topics100%

Model monitoring: performance, drift, bias

Evidently AI for data drift detection

Prometheus + Grafana stack for ML metrics

Structured logging and distributed tracing (OpenTelemetry)

Hover to pause

Standard module

Key module

Prerequisites

4 items

Advanced Python (decorators, typing, modules, tests)

Practical machine learning (model training and evaluation)

Basic Linux / command line skills

Intermediate Git (branches, PRs, hooks)

Target Audience

4 items

Experienced Data Scientists and ML Engineers

DevOps and Platform Engineers working with ML teams

Tech Leads wanting to industrialize their AI stack

Solution Architects designing enterprise AI platforms

MLOps & AI DevOps

MLOps in Production

CI/CD for AI Models

1 790 DH

36h14 modules

Docker & K8s
MLflow & DVC
FastAPI Serving
Grafana Monitoring

BOOK MY SPOT

✦ 30-day satisfaction guarantee · Priority support included