chore: sync content to repo (#10496)

Co-authored-by: nilbuild <4921183+nilbuild@users.noreply.github.com>

roadmaps/mlops/content/cloud-computing@00GZcwe25QYi7rDzaOoMt.md

@@ -7,4 +7,4 @@ Visit the following resources to learn more:
 - [@roadmap@Visit the Dedicated Cloud Computing Roadmap](https://roadmap.sh/cloud-computing)
 - [@article@Cloud Computing - IBM](https://www.ibm.com/think/topics/cloud-computing)
 - [@article@What is Cloud Computing? - Azure](https://azure.microsoft.com/en-gb/resources/cloud-computing-dictionary/what-is-cloud-computing)
-- [@video@What is Cloud Computing? - Amazon Web Services](https://www.youtube.com/watch?v=mxT233EdY5c)
+- [@video@What is Cloud Computing? - Amazon Web Services](https://www.youtube.com/watch?v=mxT233EdY5c)

roadmaps/mlops/content/data-engineering-basics@VykbCu7LWIx8fQpqKzoA7.md

@@ -0,0 +1,10 @@
+# Data Engineering Fundamentals
+
+Data engineering fundamentals cover how data is collected, stored, and moved so it can be used for analysis or model training. This includes designing pipelines that pull data from various sources, clean it, and load it into a place where it can be accessed reliably. Good data engineering practices make sure ML models are trained on accurate and up-to-date data.
+
+Visit the following resources to learn more:
+
+- [@roadmap@Visit the Dedicated Data Engineer Roadmap](https://roadmap.sh/data-engineer)
+- [@article@Data Engineering 101](https://www.redpanda.com/guides/fundamentals-of-data-engineering)
+- [@article@How to Become a Data Engineer](https://towardsdatascience.com/how-to-become-a-data-engineer-c0319cb226c2/)
+- [@video@Fundamentals of Data Engineering](https://www.youtube.com/watch?v=mPSzL8Lurs0)

roadmaps/mlops/content/data-ingestion-arch@Berd78HvnulNEGOsHCf8n.md

@@ -0,0 +1,10 @@
+# Data Ingestion Architecture
+
+Data ingestion architecture describes how data flows into a system from its original sources, such as databases, APIs, or streaming platforms. It defines whether data arrives in batches or in real time, and how it gets validated and stored along the way. A well-designed architecture keeps data reliable even as sources and volumes grow.
+
+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)

roadmaps/mlops/content/ml-fundamentals@ulka7VEVjz6ls5SnI6a6z.md

@@ -0,0 +1,11 @@
+# Machine Learning Fundamentals
+
+Machine learning fundamentals cover the core ideas needed to build predictive models, including how algorithms learn patterns from data and make predictions on new data. This includes concepts like training, testing, overfitting, and evaluation. A solid grasp of these fundamentals makes it easier to pick the right approach for a given problem.
+
+Visit the following resources to learn more:
+
+- [@roadmap@Visit the Dedicated Machine Learning Roadmap](https://roadmap.sh/machine-learning)
+- [@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)
+- [@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)

roadmaps/mlops/content/orchestration@5VuCUCfb4DEi0HKW11PIX.md

@@ -0,0 +1,8 @@
+# Orchestration & Deployment
+
+Orchestration and deployment involve automating the process of taking a trained machine learning model and making it available for use in a production environment. This includes managing the workflow of model building, testing, and releasing, as well as handling the infrastructure needed to serve the model and scale it to meet demand. It ensures that models are reliably and efficiently integrated into applications and systems.
+
+Visit the following resources to learn more:
+
+- [@article@What is orchestration?](https://www.redhat.com/en/topics/automation/what-is-orchestration)
+- [@video@What is Data Orchestration?](https://www.youtube.com/watch?v=iyw9puEmTrA)

roadmaps/mlops/content/programming-basics@_7uvOebQUI4xaSwtMjpEd.md

@@ -0,0 +1,3 @@
+# Programming Fundamentals
+
+Programming fundamentals are the basic concepts needed to write and understand code, such as variables, loops, functions, and data structures. These concepts apply across languages and form the base for writing scripts, building pipelines, and automating tasks. Without them, working with any ML or data tool becomes much harder.