chore: sync content to repo (#10340)

Co-authored-by: nilbuild <4921183+nilbuild@users.noreply.github.com>
2026-09-24 15:00:31 +08:00 · 2026-09-17 10:08:52 +02:00
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 # AWS / Azure / GCP
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 AWS, Azure, and GCP are the three largest cloud computing providers, each offering a wide range of services for computing, storage, networking, and machine learning. They differ in pricing, tooling, and specific service names, but cover similar core capabilities. Most companies choose one as their primary provider based on cost, existing infrastructure, or team familiarity.

 Visit the following resources to learn more:
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 # CI/CD
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 CI/CD stands for Continuous Integration and Continuous Delivery, a practice of automatically testing and shipping code changes. In an ML context, CI/CD pipelines also test data quality, validate model performance, and automate retraining or redeployment when new code or data arrives. This reduces manual steps and helps catch broken models before they go live.

 Visit the following resources to learn more:
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 # Cloud Computing
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 Cloud computing provides on-demand access to computing resources, such as servers, storage, and databases, over the internet instead of running everything on local hardware. It lets teams scale up resources for training large models or handling more traffic, then scale back down when not needed. Providers like AWS, Azure, and GCP offer these services on a pay-as-you-go basis.

 Visit the following resources to learn more:
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 # Data Engineering Fundamentals
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 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:
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 # Data Ingestion Architecture
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 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:
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 # Data Lineage
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 Data lineage is the record of where data comes from, how it moves, and how it gets transformed before reaching a model. It shows the full path from raw source to final training dataset. This helps teams trace errors back to their origin and understand the impact of a change made upstream.

 Visit the following resources to learn more:
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 # Experiment Tracking
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 Experiment tracking records the details of each model training run, such as hyperparameters, code version, dataset used, and resulting metrics. This makes it possible to compare different runs and understand which changes actually improved the model. Data scientists rely on this to avoid losing track of what was tried and what worked.

 Visit the following resources to learn more:
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 # Git
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 Git is a distributed version control system that tracks changes to files and lets multiple people collaborate on the same codebase. It works by creating commits, which are snapshots of the project at a point in time, and branches, which let people work on separate features in parallel. Almost every modern software and ML project uses Git to manage its code.

 Visit the following resources to learn more:
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 # GitHub
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 GitHub is a web platform for hosting Git repositories, adding features like pull requests, issue tracking, and code review on top of Git. Teams use it to collaborate on code, review changes before merging, and automate workflows with GitHub Actions. It has become one of the most common places to store and share code publicly or privately.

 Visit the following resources to learn more:
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 # Go
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 Go, also called Golang, is a programming language known for its simplicity, speed, and strong support for concurrent programs. Many infrastructure and DevOps tools, such as Docker and Kubernetes, are written in Go. Learning it helps when building lightweight services or contributing to tools in the cloud-native ecosystem.

 Visit the following resources to learn more:
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 # Infrastructure as Code
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 Infrastructure as Code, or IaC, means defining and managing computing infrastructure, such as servers and networks, using configuration files instead of manual setup. These files can be versioned, reviewed, and reused, which makes infrastructure changes more predictable and repeatable. It reduces the risk of manual errors when setting up environments for training or deploying models.

 Visit the following resources to learn more:
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 # Machine Learning Fundamentals
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 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:
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 # Machine Learning
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 Machine learning is a method of teaching computers to find patterns in data and make predictions or decisions without being explicitly programmed for the task. A model learns from examples during training, then applies what it learned to new, unseen data. It covers approaches like supervised, unsupervised, and reinforcement learning.

 Visit the following resources to learn more:
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 # MLOps Components
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 MLOps components are the building blocks that together form a working ML pipeline. These usually include version control, CI/CD, orchestration, experiment tracking, data lineage, model training and serving, and monitoring. Each component handles a different part of the lifecycle, and combining them lets teams move a model from an idea to a stable production system.

 Visit the following resources to learn more:
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 # MLOps Principles
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 MLOps principles are the core ideas that guide how teams build and operate ML systems. They include automation of the ML pipeline, reproducibility of experiments and results, continuous testing and monitoring, and collaboration between data scientists, engineers, and operations teams. Following these principles helps reduce manual work and makes it easier to catch problems before they reach production.

 Visit the following resources to learn more:
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 # Model Training & Serving
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 Model training is the process of teaching a machine learning model to make predictions using data, while serving is making that trained model available to handle real requests. Serving usually involves wrapping the model in an API so applications can send input and get predictions back. Together they cover the step where a model moves from a notebook experiment to something an application can actually use.

 Visit the following resources to learn more:
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 # Monitoring & Observability
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 Monitoring and observability track how a system behaves once it is running, using metrics, logs, and alerts. For ML systems, this also means watching model accuracy over time, since predictions can degrade as real-world data shifts away from the training data. Catching this early lets teams retrain or fix a model before it causes bigger problems.

 Visit the following resources to learn more:
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 # Orchestration
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 Orchestration means coordinating the different steps of an ML workflow so they run in the right order, automatically. A typical pipeline might need to pull data, preprocess it, train a model, evaluate it, then deploy it, and orchestration tools handle scheduling, dependencies, and retries for these steps. This removes the need to run each step by hand.

 Visit the following resources to learn more:
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 # Programming Fundamentals
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 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.
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 # Spark
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 Spark, or Apache Spark, is an open-source engine for processing large amounts of data across many machines at once. It supports batch and streaming data processing, along with built-in libraries for SQL queries and machine learning. Its ability to handle data that does not fit on a single machine makes it a common tool in large-scale data pipelines.

 Visit the following resources to learn more:
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 # Version Control
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 Version control tracks changes to files over time, so teams can see what changed, who changed it, and roll back if needed. In ML projects, this applies not just to code but also to datasets, model files, and configuration. Without it, reproducing a past result or debugging a regression becomes very difficult.

 Visit the following resources to learn more:
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 # What is MLOps?
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 MLOps is a set of practices that combines machine learning, DevOps, and data engineering to deploy and maintain ML models in production reliably. It covers the full lifecycle of a model, from training and testing to deployment, monitoring, and retraining. The goal is to make ML systems repeatable, scalable, and easier to manage over time, similar to how DevOps standardized software delivery.

 Visit the following resources to learn more: