Remove Mage sponsor references and fix FastAPI→Flask in course articles

- MLOps Zoomcamp: replace FastAPI with Flask, remove Mage/Airflow/Prefect
  in favor of generic orchestration concepts across article, FAQ, and
  structured data
- Data Engineering Zoomcamp: replace Mage.AI with Kestra across article,
  structured data, and free courses list
- Guide article: update MLOps and DE Zoomcamp sections accordingly

Author: alexeygrigorev

_data/faqs/mlops-zoomcamp.yml

@@ -2,7 +2,7 @@
   answer: |
     The MLOps Zoomcamp is a free, community-driven program by [DataTalks.Club](/) that teaches core MLOps skills through hands-on project work.
     
-    This 3-month course covers a comprehensive [curriculum](#course-curriculum) with all materials open and available anytime on [GitHub](https://github.com/DataTalksClub/mlops-zoomcamp). You'll work with an industry-standard stack including MLflow, Docker, AWS, Prometheus, Grafana, Mage, and GitHub Actions and earn a [certificate](#can-i-get-a-certificate).
+    This 3-month course covers a comprehensive [curriculum](#course-curriculum) with all materials open and available anytime on [GitHub](https://github.com/DataTalksClub/mlops-zoomcamp). You'll work with an industry-standard stack including MLflow, Docker, AWS, Prometheus, Grafana, and GitHub Actions and earn a [certificate](#can-i-get-a-certificate).
 
 - question: "What does zoomcamp mean?"
   answer: |
@@ -76,7 +76,7 @@
 
 - question: "What tools and technologies will I learn?"
   answer: |
-    The course covers essential MLOps tools and platforms including MLflow for experiment tracking, Docker for containerization, AWS services (including Kinesis), Prometheus and Grafana for monitoring, Mage for ML pipeline orchestration, and GitHub Actions for CI/CD.
+    The course covers essential MLOps tools and platforms including MLflow for experiment tracking, Docker for containerization, AWS services (including Kinesis), Prometheus and Grafana for monitoring, ML pipeline orchestration concepts, and GitHub Actions for CI/CD.
 
 - question: "How does MLOps experiment tracking work in this course?"
   answer: |
@@ -92,7 +92,7 @@
 
 - question: "What MLOps training does this course provide?"
   answer: |
-    This comprehensive training covers the complete machine learning operations lifecycle. You'll receive hands-on training in experiment tracking with MLflow, containerization with Docker, ML pipeline orchestration with Mage, model deployment (batch, real-time, and streaming), monitoring with Prometheus and Grafana, and CI/CD with GitHub Actions. The course includes 6 core technical modules, weekly homework assignments, and a final end-to-end project. This practical training prepares you for real-world production ML systems and is taught by expert instructors including Cristian Martinez, Alexey Grigorev, and Emeli Dral.
+    This comprehensive training covers the complete machine learning operations lifecycle. You'll receive hands-on training in experiment tracking with MLflow, containerization with Docker, ML pipeline orchestration concepts, model deployment (batch, real-time, and streaming), monitoring with Prometheus and Grafana, and CI/CD with GitHub Actions. The course includes 6 core technical modules, weekly homework assignments, and a final end-to-end project. This practical training prepares you for real-world production ML systems and is taught by expert instructors including Cristian Martinez, Alexey Grigorev, and Emeli Dral.
 
 - question: "Is this a free MLOps course with certificate?"
   answer: |

_includes/course-structured-data/data-engineering-zoomcamp-structured-data.html

@@ -14,7 +14,7 @@
     "Build production-grade data pipelines from start to finish",
     "Set up development environment with Docker and PostgreSQL",
     "Master infrastructure-as-code using Terraform",
-    "Implement data pipeline orchestration with Mage.AI",
+    "Implement data pipeline orchestration with Kestra",
     "Build data warehouses with BigQuery",
     "Transform raw data into analytics-ready models using dbt",
     "Process large-scale data with Apache Spark",
@@ -27,7 +27,7 @@
     "PostgreSQL",
     "Terraform",
     "Google Cloud Platform",
-    "Mage.AI",
+    "Kestra",
     "Google Cloud Storage",
     "BigQuery",
     "dbt",
@@ -47,7 +47,7 @@
     },
     {
       "name": "Workflow Orchestration",
-      "description": "Master data pipeline orchestration with Mage.AI. Implement and manage Data Lakes using Google Cloud Storage. Build automated, reproducible workflows.",
+      "description": "Master data pipeline orchestration with Kestra. Implement and manage Data Lakes using Google Cloud Storage. Build automated, reproducible workflows.",
       "timeRequired": "P1W"
     },
     {

_includes/course-structured-data/mlops-zoomcamp-structured-data.html

@@ -27,9 +27,7 @@
     "MLflow",
     "MLflow Tracking",
     "MLflow Model Registry",
-    "Mage",
-    "Airflow",
-    "Prefect",
+    "ML Pipeline Orchestration",
     "Flask",
     "AWS Lambda",
     "AWS Kinesis",
@@ -58,7 +56,7 @@
     },
     {
       "name": "Orchestration & ML Pipelines",
-      "description": "Create reproducible pipelines and manage dependencies end-to-end using Mage, Airflow, or Prefect.",
+      "description": "Learn orchestration concepts and best practices for creating reproducible pipelines and managing dependencies end-to-end.",
       "timeRequired": "P1W"
     },
     {

_posts/2023-11-18-data-engineering-zoomcamp.md

@@ -124,8 +124,8 @@ The course follows a logical progression from infrastructure setup to advanced d
 </tr>
 <tr style="background-color: #f8f9fa;">
 <td style="padding: 12px; border: 1px solid #dee2e6;">2. Workflow Orchestration</td>
-<td style="padding: 12px; border: 1px solid #dee2e6;">• Master data pipeline orchestration with Mage.AI<br>• Implement and manage Data Lakes using Google Cloud Storage<br>• Build automated, reproducible workflows</td>
-<td style="padding: 12px; border: 1px solid #dee2e6;">Mage.AI, Google Cloud Storage</td>
+<td style="padding: 12px; border: 1px solid #dee2e6;">• Master data pipeline orchestration with Kestra<br>• Implement and manage Data Lakes using Google Cloud Storage<br>• Build automated, reproducible workflows</td>
+<td style="padding: 12px; border: 1px solid #dee2e6;">Kestra, Google Cloud Storage</td>
 </tr>
 <tr>
 <td style="padding: 12px; border: 1px solid #dee2e6;">3. Data Warehouse</td>

_posts/2024-03-07-mlops-zoomcamp.md

@@ -58,7 +58,7 @@ If you're an ML engineer, data scientist, or software developer working with ML
 
 MLOps Zoomcamp is a free MLOps course that takes you from experiment tracking to production deployment in 6 modules plus a portfolio project.
 
-You'll learn infrastructure setup with Docker and AWS, experiment tracking with MLflow, pipeline orchestration with Mage, model deployment (batch, real-time, and streaming), monitoring with Prometheus and Evidently AI, and testing/CI/CD best practices.
+You'll learn infrastructure setup with Docker and AWS, experiment tracking with MLflow, pipeline orchestration concepts, model deployment (batch, real-time, and streaming), monitoring with Prometheus and Evidently AI, and testing/CI/CD best practices.
 
 The course culminates in a real-world project where you build, deploy, and monitor a complete ML pipeline that you can showcase to employers.
 
@@ -83,7 +83,7 @@ If you're comfortable with the command line and Python, and you have prior expos
 ## Course Curriculum
 
 <figure>
-<img src="/images/posts/2024-03-07-mlops-zoomcamp/mlops-zoomcamp-course-curriculum-overview.png" alt="MLOps Zoomcamp course overview - complete journey through modern MLOps tools including Docker, AWS, MLflow, Mage, Prometheus, and Evidently AI for production machine learning" title="MLOps Zoomcamp Course Overview" loading="lazy" style="max-width: 100%; height: auto; border: 1px solid #ddd; border-radius: 4px;" />
+<img src="/images/posts/2024-03-07-mlops-zoomcamp/mlops-zoomcamp-course-curriculum-overview.png" alt="MLOps Zoomcamp course overview - complete journey through modern MLOps tools including Docker, AWS, MLflow, Prometheus, and Evidently AI for production machine learning" title="MLOps Zoomcamp Course Overview" loading="lazy" style="max-width: 100%; height: auto; border: 1px solid #ddd; border-radius: 4px;" />
 <figcaption><p>Course overview: a complete journey through modern MLOps tools and technologies</p></figcaption>
 </figure>
 
@@ -115,7 +115,7 @@ The curriculum follows a logical progression from experimentation to production
 <td style="padding: 12px; border: 1px solid #dee2e6; font-weight: 600;">3</td>
 <td style="padding: 12px; border: 1px solid #dee2e6;">Orchestration & ML Pipelines</td>
 <td style="padding: 12px; border: 1px solid #dee2e6;">Create reproducible pipelines and manage dependencies end-to-end</td>
-<td style="padding: 12px; border: 1px solid #dee2e6;">Mage, Airflow, Prefect</td>
+<td style="padding: 12px; border: 1px solid #dee2e6;">Pipeline orchestration concepts and best practices</td>
 </tr>
 <tr style="background-color: #f8f9fa;">
 <td style="padding: 12px; border: 1px solid #dee2e6; font-weight: 600;">4</td>
@@ -146,7 +146,7 @@ You'll:
 
 * **Choose a dataset** that interests you
 * **Train a model** and **track experiments** using MLflow or Weights & Biases
-* **Build an automated training pipeline** using tools like Mage, Airflow, or Prefect
+* **Build an automated training pipeline** using your preferred orchestration tool
 * **Deploy your model** as a batch job, web service, or streaming system
 * **Set up monitoring** with Evidently AI, Prometheus, or Grafana
 * **Implement CI/CD workflows** using GitHub Actions or GitLab CI/CD

_posts/2024-04-11-guide-to-free-online-courses-at-datatalks-club.md

@@ -77,7 +77,7 @@ Here's a table that summarizes the key information about each course. Use it to
 <td style="padding: 12px; border: 1px solid #dee2e6;">Intermediate to Advanced</td>
 <td style="padding: 12px; border: 1px solid #dee2e6;">1+ year programming, ML exposure, Python, Docker</td>
 <td style="padding: 12px; border: 1px solid #dee2e6;">Experiment tracking, pipelines, deployment, monitoring</td>
-<td style="padding: 12px; border: 1px solid #dee2e6;">MLFlow, FastAPI, AWS, Mage, Evidently AI</td>
+<td style="padding: 12px; border: 1px solid #dee2e6;">MLFlow, Flask, AWS, Evidently AI</td>
 <td style="padding: 12px; border: 1px solid #dee2e6;">Automated ML deployment system with monitoring & alerts</td>
 </tr>
 <tr style="background-color: #f8f9fa;">
@@ -277,7 +277,7 @@ Throughout the 9-week program, you'll master essential tools like Docker for con
 
 <figure>
 <img src="/images/posts/2024-04-11-guide-to-free-online-courses-at-datatalks-club/mlops-zoomcamp-overview.png" alt="MLOps Zoomcamp course overview diagram" title="MLOps Zoomcamp Course Overview - Complete Machine Learning Operations Lifecycle" loading="lazy" style="max-width: 100%; height: auto; border: 1px solid #ddd; border-radius: 4px;" />
-<figcaption><p>MLOps Zoomcamp course overview illustrating the complete machine learning operations lifecycle: experiment tracking with MLflow, orchestration and ML pipelines with Mage, model deployment with FastAPI and AWS, and production monitoring with Evidently AI</p></figcaption>
+<figcaption><p>MLOps Zoomcamp course overview illustrating the complete machine learning operations lifecycle: experiment tracking with MLflow, orchestration and ML pipelines, model deployment with Flask and AWS, and production monitoring with Evidently AI</p></figcaption>
 </figure>
 
 <table style="width: 100%; border-collapse: collapse; margin-bottom: 1.5em;">
@@ -308,7 +308,7 @@ Throughout the 9-week program, you'll master essential tools like Docker for con
 </tr>
 <tr>
 <td style="padding: 12px; border: 1px solid #dee2e6; font-weight: 600;">Tools/tech stack</td>
-<td style="padding: 12px; border: 1px solid #dee2e6;">MLFlow, FastAPI, AWS, Mage, Evidently AI</td>
+<td style="padding: 12px; border: 1px solid #dee2e6;">MLFlow, Flask, AWS, Evidently AI</td>
 </tr>
 <tr style="background-color: #f8f9fa;">
 <td style="padding: 12px; border: 1px solid #dee2e6; font-weight: 600;">Who it's for</td>
@@ -331,7 +331,7 @@ Throughout the 9-week program, you'll master essential tools like Docker for con
 
 [MLOps Zoomcamp](https://datatalks.club/blog/mlops-zoomcamp.html) is a free MLOps course that covers the entire MLOps lifecycle: from experiment tracking and model management to deployment and monitoring. Designed for data scientists and ML engineers, the course teaches you how to operationalize machine learning models at scale.
 
-You'll learn to use MLflow for experiment tracking, build automated training pipelines with Mage, deploy models using FastAPI and AWS services, and set up comprehensive monitoring with Evidently AI. The course emphasizes best practices for testing, CI/CD integration, and maintaining ML systems in production, preparing you to build and manage reliable ML infrastructure.
+You'll learn to use MLflow for experiment tracking, build automated training pipelines, deploy models using Flask and AWS services, and set up comprehensive monitoring with Evidently AI. The course emphasizes best practices for testing, CI/CD integration, and maintaining ML systems in production, preparing you to build and manage reliable ML infrastructure.
 
 <div style="text-align: center; margin: 2em 0;">
     <div style="display: inline-block; background: #28a745; padding: 0.5em 2em; border-radius: 8px; box-shadow: 0 4px 6px rgba(50, 50, 93, 0.11), 0 1px 3px rgba(0, 0, 0, 0.08); transition: all 0.15s ease;">

_posts/2025-12-10-free-data-engineering-courses.md

@@ -239,7 +239,7 @@ Fully free data‑engineering courses that grant both complete access to materia
 3. **Level:** Intermediate (beginner‑friendly)
 4. **Prerequisites:** Comfort with the command line and basic SQL; Python experience helpful but not mandatory.
 5. **Key topics covered:** Infrastructure & prerequisites; workflow orchestration; data warehousing; analytics engineering; batch & stream processing; capstone project.
-6. **Tools/tech stack:** Docker, PostgreSQL, GCP & Terraform, Mage.AI, Google Cloud Storage, BigQuery, dbt, BI tools, Apache Spark & Spark SQL, Kafka, KSQL, Faust.
+6. **Tools/tech stack:** Docker, PostgreSQL, GCP & Terraform, Kestra, Google Cloud Storage, BigQuery, dbt, BI tools, Apache Spark & Spark SQL, Kafka, KSQL, Faust.
 7. **Format:** Free (open materials & certificate)
 8. **Duration:** 9‑week structured program
 9. **Certificate:** Free certificate after completion