chore: sync content to repo (#9622)

Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com>
2026-03-12 17:51:53 +08:00 · 2026-02-16 17:41:59 +01:00
parent 67889f2176
commit 818b06be4f
33 changed files with 55 additions and 30 deletions
--- a/src/data/roadmaps/mlops/content/airflow@AjZHJcxUY29WZbCvr3zrs.md
+++ b/src/data/roadmaps/mlops/content/airflow@AjZHJcxUY29WZbCvr3zrs.md
@@ -7,7 +7,7 @@ Visit the following resources to learn more:
 - [@official@Airflow](https://airflow.apache.org/)
 - [@official@Airflow Docs](https://airflow.apache.org/docs)
 - [@opensource@airflow](https://github.com/apache/airflow)
- [@article@What is Apache Airflow? For beginners](https://www.youtube.com/watch?v=CGxxVj13sOs)
- [@article@Building Pipelines In Apache Airflow – For Beginners](https://towardsdatascience.com/building-pipelines-in-apache-airflow-for-beginners-58f87a1512d5/)
+- [@article@Building Pipelines In Apache Airflow – For Beginners](https://towardsdatascience.com/building-pipelines-in-apache-airflow-for-beginners-58f87a1512d5/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
+- [@video@What is Apache Airflow? For beginners](https://www.youtube.com/watch?v=CGxxVj13sOs)
 - [@video@Apache Airflow Tutorial for Data Engineers](https://www.youtube.com/watch?v=y5rYZLBZ_Fw)
 - [@feed@Explore top posts about Apache Airflow](https://app.daily.dev/tags/apache-airflow?ref=roadmapsh)
--- a/src/data/roadmaps/mlops/content/cloud-native-ml-services@kbfucfIO5KCsuv3jKbHTa.md
+++ b/src/data/roadmaps/mlops/content/cloud-native-ml-services@kbfucfIO5KCsuv3jKbHTa.md
@@ -8,4 +8,6 @@ Visit the following resources to learn more:
 - [@official@Azure ML](https://azure.microsoft.com/en-gb/products/machine-learning)
 - [@official@Vertex AI Platform](https://cloud.google.com/vertex-ai?hl=en)
 - [@article@What is cloud native?](https://cloud.google.com/learn/what-is-cloud-native?hl=en)
+- [@article@Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1](https://towardsdatascience.com/azure-ml-vs-aws-sagemaker-a-deep-dive-into-scalable-model-training-part-1/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
+- [@article@AWS vs. Azure: A Deep Dive into Model Training – Part 2](https://towardsdatascience.com/aws-vs-azure-a-deep-dive-into-model-training-part-2/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@What is Cloud Native?](https://www.youtube.com/watch?v=fp9_ubiKqFU)
--- a/src/data/roadmaps/mlops/content/cml@MLob7b468ZgNX7-cURqmJ.md
+++ b/src/data/roadmaps/mlops/content/cml@MLob7b468ZgNX7-cURqmJ.md
@@ -4,6 +4,6 @@ Continuous Machine Learning (CML) is a tool designed to bring continuous integra

 Visit the following resources to learn more:

- [@article@CML](https://cml.dev/)
- [@article@Get Started with CML](https://cml.dev/doc/start)
- [@article@Continuous Machine Learning](https://towardsdatascience.com/continuous-machine-learning-e1ffb847b8da/)
+- [@official@CML](https://cml.dev/)
+- [@official@Get Started with CML](https://cml.dev/doc/start)
+- [@article@Continuous Machine Learning](https://towardsdatascience.com/continuous-machine-learning-e1ffb847b8da/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
--- a/src/data/roadmaps/mlops/content/data-ingestion-architecture@Berd78HvnulNEGOsHCf8n.md
+++ b/src/data/roadmaps/mlops/content/data-ingestion-architecture@Berd78HvnulNEGOsHCf8n.md
@@ -7,4 +7,6 @@ Lambda and Kappa architectures are two popular data ingestion architectures that
 Visit the following resources to learn more:

 - [@article@Data Ingestion Patterns](https://docs.aws.amazon.com/whitepapers/latest/aws-cloud-data-ingestion-patterns-practices/data-ingestion-patterns.html)
+- [@article@Data pipeline design patterns](https://towardsdatascience.com/data-pipeline-design-patterns-100afa4b93e3/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
+- [@article@How to Build an AI-Powered Weather ETL Pipeline with Databricks and GPT-4o: From API To Dashboard](https://towardsdatascience.com/how-to-build-an-ai-powered-weather-etl-pipeline-with-databricks-and-gpt-4o-from-api-to-dashboard/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@What is a data pipeline?](https://www.youtube.com/watch?v=kGT4PcTEPP8)
--- a/src/data/roadmaps/mlops/content/data-lakes--warehouses@wOogVDV4FIDLXVPwFqJ8C.md
+++ b/src/data/roadmaps/mlops/content/data-lakes--warehouses@wOogVDV4FIDLXVPwFqJ8C.md
@@ -5,7 +5,7 @@ Data lakes and data warehouses are both systems for storing large amounts of dat
 Visit the following resources to learn more:

 - [@article@Data Lake Definition](https://azure.microsoft.com/en-gb/resources/cloud-computing-dictionary/what-is-a-data-lake)
- [@article@Data Lake VS Data Warehouse](https://towardsdatascience.com/data-lake-vs-data-warehouse-2e3df551b800/)
+- [@article@Data Lake VS Data Warehouse](https://towardsdatascience.com/data-lake-vs-data-warehouse-2e3df551b800/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@What is a Data Lake?](https://www.youtube.com/watch?v=LxcH6z8TFpI)
 - [@video@What is a Data Warehouse?](https://www.youtube.com/watch?v=k4tK2ttdSDg)
 - [@video@Data Lake VS Data Warehouse VS Data Marts](https://www.youtube.com/watch?v=w9-WoReNKHk)
--- a/src/data/roadmaps/mlops/content/data-lineage@6XgP_2NLuiw654zvTyueT.md
+++ b/src/data/roadmaps/mlops/content/data-lineage@6XgP_2NLuiw654zvTyueT.md
@@ -6,6 +6,4 @@ Visit the following resources to learn more:

 - [@article@What is Data Lineage?](https://www.ibm.com/topics/data-lineage)
 - [@article@What is a Feature Store](https://www.snowflake.com/guides/what-feature-store-machine-learning/)
- [@article@The Ultimate Guide To Data Lineage](https://www.montecarlodata.com/blog-data-lineage/)
- [@video@What is Data Lineage?](https://www.youtube.com/watch?v=Jar5Rr_7TOU)
- [@article@What is a Feature Store](https://www.snowflake.com/guides/what-feature-store-machine-learning/)
+- [@article@How Should We Be Thinking about Data Lineage?](https://towardsdatascience.com/how-should-we-be-thinking-about-data-lineage-541ca5ab83d0/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
--- a/src/data/roadmaps/mlops/content/docker@XIdCvT-4HyyglHJLRrHlz.md
+++ b/src/data/roadmaps/mlops/content/docker@XIdCvT-4HyyglHJLRrHlz.md
@@ -6,6 +6,7 @@ Visit the following resources to learn more:

 - [@roadmap@Visit Dedicated Docker Roadmap](https://roadmap.sh/docker)
 - [@official@Docker Documentation](https://docs.docker.com/)
+- [@article@A Data Scientist’s Guide to Docker Containers](https://towardsdatascience.com/a-data-scientists-guide-to-docker-containers/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Docker Tutorial](https://www.youtube.com/watch?v=RqTEHSBrYFw)
 - [@video@Docker Simplified in 55 Seconds](https://youtu.be/vP_4DlOH1G4)
 - [@feed@Explore top posts about Docker](https://app.daily.dev/tags/docker?ref=roadmapsh)
--- a/src/data/roadmaps/mlops/content/explainable-ai@qy37ppIWDT_we-duuHwgT.md
+++ b/src/data/roadmaps/mlops/content/explainable-ai@qy37ppIWDT_we-duuHwgT.md
@@ -6,4 +6,5 @@ Visit the following resources to learn more:

 - [@article@What is Explainable AI (XAI)?](https://www.ibm.com/think/topics/explainable-ai)
 - [@article@Explainable AI (XAI) | Giskard](https://www.giskard.ai/glossary/explainable-ai-xai)
+- [@article@How to Leverage Explainable AI for Better Business Decisions](https://towardsdatascience.com/how-to-leverage-explainable-ai-for-better-business-decisions/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Explainable AI: Demystifying AI Agents Decision-Making](https://www.youtube.com/watch?v=yJkCuEu3K68)
--- a/src/data/roadmaps/mlops/content/flink@o6GQ3-8DgDtHzdX6yeg1w.md
+++ b/src/data/roadmaps/mlops/content/flink@o6GQ3-8DgDtHzdX6yeg1w.md
@@ -6,6 +6,6 @@ Visit the following resources to learn more:

 - [@official@Apache Flink Documentation](https://flink.apache.org/)
 - [@article@Apache Flink](https://www.tutorialspoint.com/apache_flink/apache_flink_introduction.htm)
- [@article@An Introduction to Stream Processing with Apache Flink](https://towardsdatascience.com/an-introduction-to-stream-processing-with-apache-flink-b4acfa58f14d/)
+- [@article@An Introduction to Stream Processing with Apache Flink](https://towardsdatascience.com/an-introduction-to-stream-processing-with-apache-flink-b4acfa58f14d/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Introduction | Apache Flink 101](https://www.youtube.com/watch?v=3cg5dABA6mo&list=PLa7VYi0yPIH1UdmQcnUr8lvjbUV8JriK0)
 - [@feed@Explore top posts about Apache Flink](https://app.daily.dev/tags/apache-flink?ref=roadmapsh)
--- a/src/data/roadmaps/mlops/content/git@06T5CbZAGJU6fJhCmqCC8.md
+++ b/src/data/roadmaps/mlops/content/git@06T5CbZAGJU6fJhCmqCC8.md
@@ -6,6 +6,7 @@ Visit the following resources to learn more:

 - [@roadmap@Visit Dedicated Git & GitHub Roadmap](https://roadmap.sh/git-github)
 - [@article@Learn Git with Tutorials, News and Tips - Atlassian](https://www.atlassian.com/git)
+- [@article@Getting Started with Git and GitHub: A Complete Tutorial for Beginner](https://towardsdatascience.com/learn-basic-git-commands-for-your-data-science-works-2a75396d530d/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@article@Git Cheat Sheet](https://cs.fyi/guide/git-cheatsheet)
 - [@video@Git & GitHub Crash Course For Beginners](https://www.youtube.com/watch?v=SWYqp7iY_Tc)
 - [@feed@Explore top posts about Git](https://app.daily.dev/tags/git?ref=roadmapsh)
--- a/src/data/roadmaps/mlops/content/github@7t7jSb3YgyWlhgCe8Se1I.md
+++ b/src/data/roadmaps/mlops/content/github@7t7jSb3YgyWlhgCe8Se1I.md
@@ -7,5 +7,6 @@ Visit the following resources to learn more:
 - [@roadmap@Visit Dedicated Git & GitHub Roadmap](https://roadmap.sh/git-github)
 - [@official@GitHub](https://github.com)
 - [@official@GitHub Documentation](https://docs.github.com/en/get-started/quickstart)
+- [@article@Comprehensive Guide to GitHub for Data Scientists](https://towardsdatascience.com/comprehensive-guide-to-github-for-data-scientist-d3f71bd320da/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@What is GitHub?](https://www.youtube.com/watch?v=w3jLJU7DT5E)
 - [@feed@Explore top posts about GitHub](https://app.daily.dev/tags/github?ref=roadmapsh)
--- a/src/data/roadmaps/mlops/content/grafana@YmRN43yAFF97c7MgNaiqs.md
+++ b/src/data/roadmaps/mlops/content/grafana@YmRN43yAFF97c7MgNaiqs.md
@@ -0,0 +1,12 @@
+# Grafana
+
+Grafana is an open-source data visualization and monitoring tool. It allows users to query, visualize, alert on, and explore metrics, logs, and traces. Grafana connects to various data sources, such as Prometheus, Graphite, Elasticsearch, and InfluxDB, to create customizable dashboards that display real-time data and historical trends.
+
+Visit the following resources to learn more:
+
+- [@official@Grafana](https://grafana.com/)
+- [@official@Grafana Docs](https://grafana.com/docs/)
+- [@official@Grafana Webinars and Videos](https://grafana.com/videos/)
+- [@article@What is Grafana?](https://www.redhat.com/en/topics/data-services/what-is-grafana)
+- [@video@Grafana Explained in Under 5 Minutes ⏲](https://www.youtube.com/watch?v=lILY8eSspEo)
+- [@video@Grafana for Beginners Ep. 1](https://www.youtube.com/watch?v=TQur9GJHIIQ&list=PLDGkOdUX1Ujo27m6qiTPPCpFHVfyKq9jT)
--- a/src/data/roadmaps/mlops/content/infrastructure-as-code@sf67bSL7HAx6iN7S6MYKs.md
+++ b/src/data/roadmaps/mlops/content/infrastructure-as-code@sf67bSL7HAx6iN7S6MYKs.md
@@ -5,5 +5,6 @@ Infrastructure as Code (IaC) is a modern approach to managing and provisioning I
 Visit the following resources to learn more:

 - [@article@What is Infrastructure as Code?](https://www.redhat.com/en/topics/automation/what-is-infrastructure-as-code-iac)
+- [@article@Automatically Managing Data Pipeline Infrastructures With Terraform](https://towardsdatascience.com/automatically-managing-data-pipeline-infrastructures-with-terraform-323fd1808a47/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Terraform Course for Beginners](https://www.youtube.com/watch?v=SLB_c_ayRMo)
 - [@video@8 Terraform Best Practices](https://www.youtube.com/watch?v=gxPykhPxRW0)
--- a/src/data/roadmaps/mlops/content/jenkins@75mlW2JoUqSNFK2IDfqFP.md
+++ b/src/data/roadmaps/mlops/content/jenkins@75mlW2JoUqSNFK2IDfqFP.md
@@ -7,4 +7,5 @@ Visit the following resources to learn more:
 - [@official@Jenkins Website](https://www.jenkins.io/)
 - [@official@Jenkins Getting Started Guide](https://www.jenkins.io/doc/pipeline/tour/getting-started/)
 - [@article@Jenkins Tutorial](https://octopus.com/devops/jenkins/jenkins-tutorial/?utm_source=roadmap&utm_medium=link&utm_campaign=devops-ci-cd-gitlab-ci)
+- [@article@From DevOps to MLOPS: Integrate Machine Learning Models using Jenkins and Docker](https://towardsdatascience.com/from-devops-to-mlops-integrate-machine-learning-models-using-jenkins-and-docker-79034dbedf1/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Learn Jenkins! Complete Jenkins Course - Zero to Hero](https://www.youtube.com/watch?v=6YZvp2GwT0A)
--- a/src/data/roadmaps/mlops/content/kafka@fMNwzhgLgHlAZJ9NvKikR.md
+++ b/src/data/roadmaps/mlops/content/kafka@fMNwzhgLgHlAZJ9NvKikR.md
@@ -5,6 +5,7 @@ Kafka is a distributed, fault-tolerant, high-throughput streaming platform. It's
 Visit the following resources to learn more:

 - [@official@Apache Kafka Quickstart](https://kafka.apache.org/quickstart)
- [@article@Read Articles about Kafka](https://towardsdatascience.com/tag/apache-kafka/)
+- [@article@What is Apache Kafka?](https://aws.amazon.com/what-is/apache-kafka/)
+- [@article@End-to-End Data Engineering System on Real Data with Kafka, Spark, Airflow, Postgres, and Docker](https://towardsdatascience.com/end-to-end-data-engineering-system-on-real-data-with-kafka-spark-airflow-postgres-and-docker-a70e18df4090/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Apache Kafka Fundamentals](https://www.youtube.com/watch?v=B5j3uNBH8X4)
 - [@feed@Explore top posts about Kafka](https://app.daily.dev/tags/kafka?ref=roadmapsh)
--- a/src/data/roadmaps/mlops/content/kubeflow@QEayqA8zYsJn0rQYz-4zv.md
+++ b/src/data/roadmaps/mlops/content/kubeflow@QEayqA8zYsJn0rQYz-4zv.md
@@ -7,5 +7,6 @@ Visit the following resources to learn more:
 - [@official@Kubeflow](https://www.kubeflow.org/)
 - [@opensource@kubeflow](https://github.com/kubeflow/kubeflow)
 - [@article@What is Kubeflow?](https://cloud.google.com/discover/what-is-kubeflow?hl=en)
+- [@article@Tutorial – Basic Kubeflow Pipeline From Scratch](https://towardsdatascience.com/tutorial-basic-kubeflow-pipeline-from-scratch-5f0350dc1905/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Kubeflow Explained for Beginners](https://www.youtube.com/watch?v=hvzEPlRdJ2Q)
 - [@video@Intro to Kubeflow Pipelines](https://www.youtube.com/watch?v=_AY8mmbR1o4&list=PLIivdWyY5sqLS4lN75RPDEyBgTro_YX7x)
--- a/src/data/roadmaps/mlops/content/lime@pVSlVHXIap0unFxLGM-lQ.md
+++ b/src/data/roadmaps/mlops/content/lime@pVSlVHXIap0unFxLGM-lQ.md
@@ -1,10 +1,9 @@
-# LIME
+# Airflow

-LIME (Local Interpretable Model-agnostic Explanations) is a technique used to understand the predictions of machine learning models. It works by approximating the complex model with a simpler, interpretable model (like a linear model) in the vicinity of a specific prediction. This local approximation helps to understand which features are most important for that particular prediction, providing insights into why the model made that decision.
+Airflow is a platform to programmatically author, schedule, and monitor workflows. Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command-line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.

 Visit the following resources to learn more:

- [@opensource@lime](https://github.com/marcotcr/lime)
- [@article@LIME Unveiled: A Deep Dive into Explaining AI Models for Text, Images, and Tabular Data](https://medium.com/@shree144/lime-unveiled-a-deep-dive-into-explaining-ai-models-for-text-images-and-tabular-data-046c7c3b4e9f)
- [@article@Explainable AI - Understanding and Trusting Machine Learning Models](https://www.datacamp.com/tutorial/explainable-ai-understanding-and-trusting-machine-learning-models)
- [@video@Understanding LIME | Explainable AI](https://www.youtube.com/watch?v=CYl172IwqKs)
+- [@official@Airflow](https://airflow.apache.org/)
+- [@official@Airflow Documentation](https://airflow.apache.org/docs)
+- [@feed@Explore top posts about Apache Airflow](https://app.daily.dev/tags/apache-airflow?ref=roadmapsh)
--- a/src/data/roadmaps/mlops/content/machine-learning-fundamentals@ulka7VEVjz6ls5SnI6a6z.md
+++ b/src/data/roadmaps/mlops/content/machine-learning-fundamentals@ulka7VEVjz6ls5SnI6a6z.md
@@ -5,6 +5,7 @@ Machine learning fundamentals encompass the key concepts and techniques that ena
 Visit the following resources to learn more:

 - [@roadmap@Visit the Dedicated Machine Learning Roadmap](https://roadmap.sh/machine-learning)
+- [@article@Everything I Studied to Become a Machine Learning Engineer (No CS Background)](https://towardsdatascience.com/everything-i-studied-to-become-a-machine-learning-engineer-no-cs-background/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@course@Fundamentals of Machine Learning - Microsoft](https://learn.microsoft.com/en-us/training/modules/fundamentals-machine-learning/)
 - [@course@MLCourse.ai](https://mlcourse.ai/)
 - [@course@Fast.ai](https://course.fast.ai)
--- a/src/data/roadmaps/mlops/content/machine-learning@6uGYeXZn5fW2qPfSb8GJb.md
+++ b/src/data/roadmaps/mlops/content/machine-learning@6uGYeXZn5fW2qPfSb8GJb.md
@@ -8,4 +8,4 @@ Visit the following resources to learn more:
 - [@book@Machine Learning: The Basics](https://alexjungaalto.github.io/MLBasicsBook.pdf)
 - [@article@What is Machine Learning (ML)?](https://www.ibm.com/topics/machine-learning)
 - [@video@What is Machine Learning?](https://www.youtube.com/watch?v=9gGnTQTYNaE)
- [@video@Complete Machine Learning in One Video | Machine Learning Tutorial For Beginners 2025 | Simplilearn](https://www.youtube.com/watch?v=PtYRUoJRE9s)
+- [@video@Complete Machine Learning in One Video | Machine Learning Tutorial For Beginners 2025 | Simplilearn](https://www.youtube.com/watch?v=PtYRUoJRE9s)
--- a/src/data/roadmaps/mlops/content/maths--statistics@C4KbkZ7c8TspXa3RBOCug.md
+++ b/src/data/roadmaps/mlops/content/maths--statistics@C4KbkZ7c8TspXa3RBOCug.md
@@ -1,6 +1,6 @@
 # Maths & Statistics

-Mathematics and statistics provide the foundational principles for understanding and building machine learning models. These disciplines offer the tools to analyze data, quantify uncertainty, and optimize model performance. Key areas include linear algebra for data representation and manipulation, calculus for optimization algorithms, probability theory for handling uncertainty, and statistical inference for drawing conclusions from data.
+Mathematics and statistics provide the foundational principles for understanding and building machine learning models. These disciplines offer the tools to analyze data, quantify uncertainty, and optimize model performance. Key areas include linear algebra for data representation and manipulation, calculus for optimization algorithms, probability theory for handling uncertainty, and statistical inference for concluding data.

 Visit the following resources to learn more:

@@ -8,6 +8,7 @@ Visit the following resources to learn more:
 - [@article@Computer Science 70, 001 - Spring 2015 - Discrete Mathematics and Probability Theory](http://www.infocobuild.com/education/audio-video-courses/computer-science/cs70-spring2015-berkeley.html)
 - [@article@Discrete Mathematics By IIT Ropar NPTEL](https://nptel.ac.in/courses/106/106/106106183/)
 - [@article@Introduction to Statistics](https://imp.i384100.net/3eRv4v)
+- [@article@How to Learn the Math Needed for Data Science](https://towardsdatascience.com/how-to-learn-the-math-needed-for-data-science-86c6643b0c59/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Lec 1 | MIT 6.042J Mathematics for Computer Science, Fall 2010](https://www.youtube.com/watch?v=L3LMbpZIKhQ&list=PLB7540DEDD482705B)
 - [@video@Discrete Mathematics by Shai Simonson (19 videos)](https://www.youtube.com/playlist?list=PLWX710qNZo_sNlSWRMVIh6kfTjolNaZ8t)
 - [@video@tatistics - A Full University Course on Data Science Basics](https://www.youtube.com/watch?v=xxpc-HPKN28)
--- a/src/data/roadmaps/mlops/content/mlflow@93MPXEulKgPnuRlp7F-Nj.md
+++ b/src/data/roadmaps/mlops/content/mlflow@93MPXEulKgPnuRlp7F-Nj.md
@@ -8,6 +8,6 @@ Visit the following resources to learn more:
 - [@official@MLFlow Docs](https://mlflow.org/docs/latest/)
 - [@opensource@mlflow](https://github.com/mlflow/mlflow)
 - [@article@Streamline Your Machine Learning Workflow with MLFlow](https://www.datacamp.com/tutorial/mlflow-streamline-machine-learning-workflow)
- [@article@Comprehensive Guide to MlFlow](https://towardsdatascience.com/comprehensive-guide-to-mlflow-b84086b002ae/)
+- [@article@Comprehensive Guide to MlFlow](https://towardsdatascience.com/comprehensive-guide-to-mlflow-b84086b002ae/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@MLFlow Tutorial | ML Ops Tutorial](https://www.youtube.com/watch?v=6ngxBkx05Fs)
 - [@video@MLflow for Machine Learning Development - Video Introduction](https://www.youtube.com/watch?v=5pPflDSdFLg&list=PLQqR_3C2fhUUOmaeowgv4WquvH515zVmo)
--- a/src/data/roadmaps/mlops/content/model-evaluation@nS4igSghzVFwamhVOwCjm.md
+++ b/src/data/roadmaps/mlops/content/model-evaluation@nS4igSghzVFwamhVOwCjm.md
@@ -6,5 +6,5 @@ Visit the following resources to learn more:

 - [@article@What is Model Evaluation](https://domino.ai/data-science-dictionary/model-evaluation)
 - [@article@Model Evaluation Metrics](https://www.markovml.com/blog/model-evaluation-metrics)
- [@article@Read Articles about Model Evaluation](https://towardsdatascience.com/tag/model-evaluation/)
+- [@article@How to Evaluate the Performance of Your ML/ AI Models](https://towardsdatascience.com/how-to-evaluate-the-performance-of-your-ml-ai-models-ba1debc6f2fa/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@How to evaluate ML models | Evaluation metrics for machine learning](https://www.youtube.com/watch?v=LbX4X71-TFI)
--- a/src/data/roadmaps/mlops/content/model-training--serving@zsW1NRb0dMgS-KzWsI0QU.md
+++ b/src/data/roadmaps/mlops/content/model-training--serving@zsW1NRb0dMgS-KzWsI0QU.md
@@ -6,5 +6,5 @@ Visit the following resources to learn more:

 - [@opensource@What is model training?](https://www.ibm.com/think/topics/model-training)
 - [@article@What Is AI Model Training & Why Is It Important?](https://www.oracle.com/uk/artificial-intelligence/ai-model-training/)
- [@article@KServe Tutorial](https://towardsdatascience.com/kserve-highly-scalable-machine-learning-deployment-with-kubernetes-aa7af0b71202)
+- [@article@KServe Tutorial](https://towardsdatascience.com/kserve-highly-scalable-machine-learning-deployment-with-kubernetes-aa7af0b71202/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Five Steps to Create a New AI Model](https://www.youtube.com/watch?v=jcgaNrC4ElU&t=172s)
--- a/src/data/roadmaps/mlops/content/monitoring--observability@r4fbUwD83uYumEO1X8f09.md
+++ b/src/data/roadmaps/mlops/content/monitoring--observability@r4fbUwD83uYumEO1X8f09.md
@@ -5,4 +5,5 @@
 Visit the following resources to learn more:

 - [@article@ML Monitoring vs ML Observability](https://medium.com/marvelous-mlops/ml-monitoring-vs-ml-observability-understanding-the-differences-fff574a8974f)
+- [@article@Building a Robust Data Observability Framework to Ensure Data Quality and Integrity](https://towardsdatascience.com/building-a-robust-data-observability-framework-to-ensure-data-quality-and-integrity-07ff6cffdf69/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@ML Observability vs ML Monitoring: What's the difference?](https://www.youtube.com/watch?v=k1Reed3QIYE)
--- a/src/data/roadmaps/mlops/content/orchestration@fes7M--Y8i08_zeP98tVV.md
+++ b/src/data/roadmaps/mlops/content/orchestration@fes7M--Y8i08_zeP98tVV.md
@@ -5,6 +5,6 @@ ML orchestration refers to the process of managing and coordinating the various
 Visit the following resources to learn more:

 - [@article@An Introduction to Data Orchestration: Process and Benefits](https://www.datacamp.com/blog/introduction-to-data-orchestration-process-and-benefits)
- [@article@A Complete Guide to Understanding Data Orchestration](https://towardsdatascience.com/a-complete-guide-to-understanding-data-orchestration-87a20b46297c/)
+- [@article@A Complete Guide to Understanding Data Orchestration](https://towardsdatascience.com/a-complete-guide-to-understanding-data-orchestration-87a20b46297c/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@article@Data Orchestration Tools (Quick Reference Guide)](https://www.montecarlodata.com/blog-11-data-orchestration-tools)
 - [@video@What is Data Orchestration?](https://www.youtube.com/watch?v=iyw9puEmTrA)
--- a/src/data/roadmaps/mlops/content/python@Vh81GnOUOZvDOlOyI5PwT.md
+++ b/src/data/roadmaps/mlops/content/python@Vh81GnOUOZvDOlOyI5PwT.md
@@ -1,6 +1,6 @@
 # Python

-Python is a widely-used programming language known for its clear syntax and extensive libraries. It's a versatile tool that can handle many tasks, from simple scripting to complex software development. Its ease of use and the availability of specialized libraries for data analysis, machine learning, and automation make it a popular choice for building and deploying machine learning systems.
+Python is a widely used programming language known for its clear syntax and extensive libraries. It's a versatile tool that can handle many tasks, from simple scripting to complex software development. Its ease of use and the availability of specialized libraries for data analysis, machine learning, and automation make it a popular choice for building and deploying machine learning systems.

 Visit the following resources to learn more:

--- a/src/data/roadmaps/mlops/content/pytorch@Zh7AfH6hJ6MMXiqSnQn7G.md
+++ b/src/data/roadmaps/mlops/content/pytorch@Zh7AfH6hJ6MMXiqSnQn7G.md
@@ -7,5 +7,6 @@ Visit the following resources to learn more:
 - [@official@PyTorch](https://pytorch.org/)
 - [@official@PyTorch Docs](https://pytorch.org/docs/stable/index.html)
 - [@article@What is PyTorc? | IBM](https://www.ibm.com/think/topics/pytorch)
+- [@article@PyTorch Explained: From Automatic Differentiation to Training Custom Neural Networks](https://towardsdatascience.com/the-basics-of-deep-learning-with-pytorch-in-1-hour/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@PyTorch in 100 seconds](https://www.youtube.com/watch?v=ORMx45xqWkA)
 - [@video@PyTorch for Deep Learning & Machine Learning – Full Course](https://www.youtube.com/watch?v=V_xro1bcAuA)
--- a/src/data/roadmaps/mlops/content/scikit-learn@T-pybEZVvvXopCTbDykR-.md
+++ b/src/data/roadmaps/mlops/content/scikit-learn@T-pybEZVvvXopCTbDykR-.md
@@ -7,6 +7,5 @@ Visit the following resources to learn more:
 - [@official@scikit-learn: machine learning in Python](https://scikit-learn.org/)
 - [@opensource@scikit-learn](https://github.com/scikit-learn/scikit-learn)
 - [@article@What is Scikit-Learn (Sklearn)?](https://www.ibm.com/think/topics/scikit-learn)
- [@article@Read Articles about scikit-learn](https://towardsdatascience.com/tag/sklearn/)
 - [@video@How to train and test a neural network using scikit-learn and Keras in Jupyter Notebook](https://www.youtube.com/watch?v=_JG71FIP1rk)
 - [@video@Scikit-learn Crash Course - Machine Learning Library for Python](https://www.youtube.com/watch?v=0B5eIE_1vpU)
--- a/src/data/roadmaps/mlops/content/shap@JQKEWz0-vIJoLimLKvIzz.md
+++ b/src/data/roadmaps/mlops/content/shap@JQKEWz0-vIJoLimLKvIzz.md
@@ -7,4 +7,5 @@ Visit the following resources to learn more:
 - [@official@Welcome to the SHAP documentation](https://shap.readthedocs.io/en/latest/)
 - [@opensource@shap](https://github.com/shap/shap)
 - [@article@Explainable AI - Understanding and Trusting Machine Learning Models](https://www.datacamp.com/tutorial/explainable-ai-understanding-and-trusting-machine-learning-models)
+- [@article@When Shapley Values Break: A Guide to Robust Model Explainability](https://towardsdatascience.com/when-shapley-values-break-a-guide-to-robust-model-explainability/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@SHAP values for beginners | What they mean and their applications](https://www.youtube.com/watch?v=MQ6fFDwjuco)
--- a/src/data/roadmaps/mlops/content/spark@UljuqA89_SlCSDWWMD_C_.md
+++ b/src/data/roadmaps/mlops/content/spark@UljuqA89_SlCSDWWMD_C_.md
@@ -6,6 +6,6 @@ Visit the following resources to learn more:

 - [@official@ApacheSpark](https://spark.apache.org/documentation.html)
 - [@article@Spark By Examples](https://sparkbyexamples.com)
- [@article@First Steps in Machine Learning with Apache Spark](https://towardsdatascience.com/first-steps-in-machine-learning-with-apache-spark-672fe31799a3/)
- [@article@Read articles about Apache Spark](https://towardsdatascience.com/tag/apache-spark/)
+- [@article@First Steps in Machine Learning with Apache Spark](https://towardsdatascience.com/first-steps-in-machine-learning-with-apache-spark-672fe31799a3/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
+- [@article@Complete Guide to Spark and PySpark Setup for Data Science](https://towardsdatascience.com/complete-guide-to-spark-and-pyspark-setup-for-data-science-374ecd8d1eea/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Apache Spark Architecture - EXPLAINED!](https://www.youtube.com/watch?v=iXVIPQEGZ9Y)
--- a/src/data/roadmaps/mlops/content/tensorflow@so5IQDxKibaZlgoCh8tpQ.md
+++ b/src/data/roadmaps/mlops/content/tensorflow@so5IQDxKibaZlgoCh8tpQ.md
@@ -6,6 +6,6 @@ Visit the following resources to learn more:

 - [@official@Tensorflow](https://www.tensorflow.org/)
 - [@official@Tensorflow Documentation](https://www.tensorflow.org/learn)
- [@article@astering Deep Learning with TensorFlow: From Beginner to Expert](https://towardsdatascience.com/an-introduction-to-tensorflow-fa5b17051f6b/)
+- [@article@Mastering Deep Learning with TensorFlow: From Beginner to Expert](https://towardsdatascience.com/an-introduction-to-tensorflow-fa5b17051f6b/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Tensorflow in 100 seconds](https://www.youtube.com/watch?v=i8NETqtGHms)
 - [@video@Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial](https://www.youtube.com/watch?v=VtRLrQ3Ev-U)
--- a/src/data/roadmaps/mlops/content/terraform@dA9AE-3lTB6lN-X-_Byd2.md
+++ b/src/data/roadmaps/mlops/content/terraform@dA9AE-3lTB6lN-X-_Byd2.md
@@ -7,5 +7,5 @@ Visit the following resources to learn more:
 - [@roadmap@Visit the Dedicated Terraform Roadmap](https://roadmap.sh/terraform)
 - [@official@Terraform](https://developer.hashicorp.com/terraform)
 - [@article@What is Terraform?](https://www.ibm.com/think/topics/terraform)
- [@article@Automatically Managing Data Pipeline Infrastructures With Terraform](https://towardsdatascience.com/automatically-managing-data-pipeline-infrastructures-with-terraform-323fd1808a47/)
+- [@article@Automatically Managing Data Pipeline Infrastructures With Terraform](https://towardsdatascience.com/automatically-managing-data-pipeline-infrastructures-with-terraform-323fd1808a47/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@Terraform Course - Automate your AWS cloud infrastructure](https://www.youtube.com/watch?v=SLB_c_ayRMo)
--- a/src/data/roadmaps/mlops/content/what-is-mlops@IghGpwAFVB067uOosmoDC.md
+++ b/src/data/roadmaps/mlops/content/what-is-mlops@IghGpwAFVB067uOosmoDC.md
@@ -7,8 +7,8 @@ Visit the following resources to learn more:
 - [@book@MLOps Engineering at Scale - Carl Osipov](http://103.203.175.90:81/fdScript/RootOfEBooks/E%20Book%20collection%20-%202025%20-%20C/CSE%20%20IT%20AIDS%20ML/MLOps%20Engineering%20at%20Scale%20-%20Carl%20Osipov%20(Manning,%202022).pdf)
 - [@article@What is MLOps?](https://aws.amazon.com/what-is/mlops/)
 - [@article@MLOps Explained: A Deep Dive into Machine Learning Operations](https://hermanmotcheyo.medium.com/mlops-explained-a-deep-dive-into-machine-learning-operations-ab9342c5c90d)
+- [@article@Machine Learning Operations (MLOps) For Beginners](https://towardsdatascience.com/machine-learning-operations-mlops-for-beginners-a5686bfe02b2/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@article@MLOps vs DevOps: Differences, Overlaps, and Use Cases](https://www.datacamp.com/blog/mlops-vs-devops)
- [@article@Machine Learning Operations (MLOps) For Beginners](https://towardsdatascience.com/machine-learning-operations-mlops-for-beginners-a5686bfe02b2/)
 - [@article@MLOps: What It Is, Why It Matters, and How to Implement It](https://neptune.ai/blog/mlops)
 - [@video@What is MLOps?](https://www.youtube.com/watch?v=OejCJL2EC3k)
 - [@video@MLOps Explained - What It Is, Why You Need It and How It Works](https://www.youtube.com/watch?v=biqYkVf-a7Y)