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“https://projects.prodevopsguytech.com // https://blog.prodevopsguytech.com
• We post Daily Trending DevOps/Cloud content
• All DevOps related Code & Scripts uploaded
• DevOps/Cloud Job Related Posts
• Real-time Interview questions & preparation guid...”
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Posts Archive
Let's talk about Kubernetes Gateway API.
It is a new way to manage traffic to Kubernetes services. 🤠
🔣How is it different from Ingress?
Ingress focuses on routing HTTP traffic.
While Gateway API supports a wider range of protocols, including HTTP, TCP, and gRPC.
🔣It also supports:
➡️HTTP Routing & TCP Routing
➡️HTTP Traffic Splitting (10% to service-1 and 90% to service-2)
➡️Cross-Namespace Routing
➡️Role-Based Access Control
➡️Enhanced Secuirty Controls
✉️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
☁️ AWS now provides an API to programmatically track free-tier usage.
🔣It provides:
a. actual usage
b. forecasted usage
c. no data if usage is greater than the limit.
d. free-tier limit
Though it can be done via alerts, it is helpful to programmatically limit resource consumption based on the usage data provided and an understanding of what the amount will be after the free tier limit is reached.
Once, I forgot to shut down an EC2 machine and one DocumentDB instance and the final bill was $$$. 😀
🔗 https://lnkd.in/g5Ve8-6e
✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
👉Continuous deployment as sumes that every product change or update is deployed automatically to production without any manual supervision from a DevOps engineer.
💡 Continuous Delivery:
- Automates the release process.
- Ensures readiness for deployment at any time.
- Allows manual deployment when needed.
💡 Continuous Deployment:
- Automates deployment of every successful code change.
- Directly deploys to production without human intervention.
- Requires high confidence in automated testing.
✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
🔣Exploring Kubernetes 🚀 Let's talk about scaling applications!
📌 When it comes to running applications on Kubernetes, we don’t run an individual pod. Because Kubernetes is all about 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐚𝐧𝐝 𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧𝐢𝐧𝐠 the availability of pods.
📌 So if you run a single pod, it will be a 𝐬𝐢𝐧𝐠𝐥𝐞 𝐩𝐨𝐢𝐧𝐭 𝐨𝐟 𝐟𝐚𝐢𝐥𝐮𝐫𝐞. Because the Pods themselves cannot be directly scaled.
📌 we need controllers like Replicaset to ensure the desired number of pods are running at all times. Kubernetes has different types of objects associated with pods for different use cases.
The following are important pod-associated objects.
📍𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐬𝐞𝐭 : To maintain a stable set of Pods replicas running at any given time.
📍𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 : To run stateless applications like web servers, APIs, etc
📍𝐒𝐭𝐚𝐭𝐞𝐟𝐮𝐥𝐒𝐞𝐭𝐬 : To run stateful applications like distributed databases.
📍𝐃𝐚𝐞𝐦𝐨𝐧𝐬𝐞𝐭𝐬 : To run agents on all the Kubernetes nodes.
📍𝐉𝐨𝐛𝐬 : For batch processing.
📍𝐂𝐫𝐨𝐧𝐉𝐨𝐛𝐬 : Scheduled Jobs.
✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
⚡️ 𝐀 𝐐𝐮𝐢𝐜𝐤 𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭❗️
In today's tech-driven world, selecting the perfect cloud service can be a game-changer for your business.
To make your decision a bit easier, I've put together a quick comparison cheat sheet of some popular cloud providers: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
♾ CI/CD Triggers: Cron Job vs. Poll SCM vs. Webhook
These triggers are responsible for initiating the execution of automated build processes based on specific events or schedules.
⏩ Cron Job: A cron job is a scheduled task or command that is executed at specified intervals according to the cron schedule.
⏩ Poll SCM: It is a mechanism used by CI/CD systems to periodically check the source code repository (SCM) for changes.
⏩ Webhook: It is used for automatically triggering actions when certain events occur.
✔️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
🔴 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐰𝐢𝐭𝐡 𝐆𝐫𝐚𝐟𝐚𝐧𝐚, 𝐋𝐨𝐤𝐢, 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐆𝐫𝐚𝐟𝐚𝐧𝐚 𝐀𝐠𝐞𝐧𝐭
Visualizing logs, metrics, and traces has never been easier!
This diagram illustrates the seamless integration between Grafana, Loki, and the Grafana Agent, enabling you to collect, visualize, and analyze all your observability data in one place.
➡️ 𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬:
✅ 𝐆𝐫𝐚𝐟𝐚𝐧𝐚 𝐀𝐠𝐞𝐧𝐭: Collects logs from various sources, including your firewall, filesystem, applications, and Kubernetes clusters. It also scrapes Prometheus metrics and discovers Prometheus targets and rules.
✅ 𝐆𝐫𝐚𝐟𝐚𝐧𝐚 𝐋𝐨𝐤𝐢: Centralizes log storage and management, allowing you to query and analyze your logs efficiently.
✅ 𝐆𝐫𝐚𝐟𝐚𝐧𝐚: Provides a powerful and user-friendly interface for visualizing all your logs, metrics, and traces. You can create dashboards and alerts to monitor your system health and performance in real-time.
𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐮𝐬𝐢𝐧𝐠 𝐆𝐫𝐚𝐟𝐚𝐧𝐚, 𝐋𝐨𝐤𝐢, 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐆𝐫𝐚𝐟𝐚𝐧𝐚 𝐀𝐠𝐞𝐧𝐭 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫:
✅ 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐝 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Gain a deeper understanding of your system's health and performance by visualizing all your data in one place.
✅ 𝐅𝐚𝐬𝐭𝐞𝐫 𝐭𝐫𝐨𝐮𝐛𝐥𝐞𝐬𝐡𝐨𝐨𝐭𝐢𝐧𝐠: Quickly identify and diagnose issues with the help of centralized logs and real-time monitoring.
✅ 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝 𝐝𝐚𝐭𝐚 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Streamline your data collection and analysis workflows with a unified platform.
✔️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
📌 https://harshhaa.hashnode.dev/how-to-deploy-daemonsets-service-in-kubernetes-k8s
🔗 More DevOps Blogs : HERE
🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩
Follow 🍩 Like 👍 Share 👍 Comment Your thoughts 💬
⭐️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy & @devopsdocs 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!
☁️ Git Branching Strategies: Navigate the Options for a Robust CI/CD Pipeline
Choosing the right Git branching strategy is crucial for streamlining your CI/CD pipeline and maintaining a healthy codebase.
Here's a breakdown of popular strategies, their differences, and how to select the best fit for you:
𝟭. 𝗚𝗶𝘁𝗙𝗹𝗼𝘄:
➡️Focus: Structured workflow with separate branches for features, releases, hotfixes, and development.
➡️Pros: Well-defined roles for each branch, reduces merge conflicts, suitable for large teams.
➡️Cons: Overhead of managing many branches, complex for smaller teams, potential merge fatigue.
𝟮. 𝗚𝗶𝘁𝗵𝘂𝗯 𝗙𝗹𝗼𝘄:
➡️Focus: Simpler approach, primarily relies on feature branches and pull requests.
➡️Pros: Lightweight, easy to use, encourages collaboration and code review.
➡️Cons: Can lead to merge conflicts if not managed carefully, not ideal for complex releases.
𝟯. 𝗧𝗿𝘂𝗻𝗸-𝗯𝗮𝘀𝗲𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 (𝗚𝗶𝘁𝗟𝗮𝗯 𝗙𝗹𝗼𝘄):
➡️Focus: Continuous integration directly onto the main branch, using feature flags for experimentation.
➡️Pros: Faster deployments, reduces merge friction, encourages frequent testing.
➡️Cons: Requires stricter discipline to avoid breaking changes, less suitable for projects with high risk of regressions.
𝟰. 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗕𝗿𝗮𝗻𝗰𝗵 𝗙𝗹𝗼𝘄:
➡️Focus: Similar to Github Flow, but with dedicated release branches for deployments.
➡️Pros: Balances simplicity with some release control, good for teams comfortable with feature branches.
➡️Cons: Adds complexity compared to Github Flow, not as structured as GitFlow.
𝟱. 𝗚𝗶𝘁𝗞𝗿𝗮𝗸𝗲𝗻 𝗙𝗹𝗼𝘄:
➡️Focus: Integrates GitFlow concepts with elements of Github Flow, allowing for flexible customization.
➡️Pros: Adaptable to various team sizes and workflows, promotes continuous integration and testing.
➡️Cons: Requires more configuration and understanding compared to simpler strategies.
✔️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
🆘 How do we manage configurations in a system?
The diagram shows a comparison between traditional configuration management and IaC (Infrastructure as Code).
⭐ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
☄️ Here is the process for how Projects/companies build a successful project outcomes.....
❤️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
🛠 Implementation of the Entire Advanced CI/CD Pipeline with Major DevOps Tools 🛠
➡️ Project Link : HERE
💥 Included Step by Step procedure
💥 Easy Understanding guide
💥 Used DevOps advanced Tools
💥 Each & Every Commands used in project are Included
💥 Tools used in Project :
✅ Jenkins
✅ Docker
✅ Kubernetes
✅ Ansible
✅ Terraform
✅ Prometeous
✅ Maven
✅ AWS
✅ SonarQube
✅ SonarCloud
✅ JFrog
Hit the Star! 🌟 & Follow me on GitHub for more like this
If you are planning to use this repo for learning, please hit the star.
❤️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
❤️🔥 What is the best way to learn SQL?
In 1986, SQL (Structured Query Language) became a standard. Over the next 40 years, it became the dominant language for relational database management systems. Reading the latest standard (ANSI SQL 2016) can be time-consuming. How can I learn it?
There are 5 components of the SQL language:
➡️DDL: data definition language, such as CREATE, ALTER, DROP
➡️DQL: data query language, such as SELECT
➡️DML: data manipulation language, such as INSERT, UPDATE, DELETE
➡️DCL: data control language, such as GRANT, REVOKE
➡️TCL: transaction control language, such as COMMIT, ROLLBACK
For a backend engineer, you may need to know most of it. As a data analyst, you may need to have a good understanding of DQL. Select the topics that are most relevant to you.
❤️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
⚙️ 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗗𝗲𝘃𝗢𝗽𝘀: 𝗔 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗦𝘂𝗰𝗰𝗲𝘀𝘀
1️⃣. Foundational Understanding
2️⃣. Continuous Integration (CI)
3️⃣. Infrastructure as Code (IaC)
4️⃣. Containerization and Orchestration
5️⃣. Continuous Deployment (CD)
6️⃣. Monitoring and Logging
7️⃣. Security in DevOps
8️⃣. Collaboration and Communication
Foster a culture of continuous learning and improvement.
❤️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
💡 10x your Docker learnings
Find out about a Magical command-line interface that has revolutionized the way docker images and container are created.
📌 Imagin you just point to your source code and run some magical words and get a docker setup created for your application automatically, well thats what docker init actually does.
This is not your ordinary Docker Command, Docker Desktop provides the docker init CLI command. Run docker init in your project directory to be walked through the creation of the following files with sensible defaults for your project:
.dockerignore Dockerfile compose.yaml README❤️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
In the world of #DevOps and #Networking , load balancing plays a crucial role in distributing traffic across servers efficiently. One of the most effective algorithms used for this purpose is the Least Time Algorithm.
🎯 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐋𝐞𝐚𝐬𝐭 𝐓𝐢𝐦𝐞 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦?
The Least Time Algorithm, also known as the Minimum Response Time Algorithm, directs incoming traffic to the server with the least expected response time. This is achieved by constantly monitoring servers' response times and routing requests accordingly.
🔑 𝐊𝐞𝐲 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬:
𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: By directing traffic to the server with the shortest response time, the algorithm minimizes overall latency and improves user experience.
𝐀𝐝𝐚𝐩𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: It dynamically adjusts to changing server loads and network conditions, ensuring optimal performance.
𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: By distributing traffic evenly, it prevents overloading of any single server, leading to better resource utilization.
🛠 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧:
𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐋𝐞𝐚𝐬𝐭 𝐓𝐢𝐦𝐞 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐬:
Monitoring tools to track response times.
A load balancer that can analyze and route traffic based on these metrics.
Configuration settings to fine-tune the algorithm based on application requirements.
🚀 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧:
Imagine a scenario where an e-commerce website experiences a sudden surge in traffic due to a flash sale. A load balancer using the Least Time Algorithm can ensure that incoming requests are distributed to servers with the shortest response times, preventing downtime and ensuring a smooth shopping experience for customers.
⭐ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
♾ Continuous Delivery vs. Continuous Deployment
➡️ Continuous Delivery: It ensures that your code changes are always deployable, providing a reliable and automated process for building, testing, and preparing for release. However, the deployment to production is a manual step, allowing for human intervention and control over when changes go live.
➡️ Continuous Deployment: It takes automation to the next level by automatically deploying every successful change to production. This means that once code passes all tests and checks, it's automatically pushed into production without the need for manual intervention.
⭐ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
📣 The machine learning (ML) lifecycle can seem complex and intimidating, but it's essentially a journey from asking a business question to getting a model into production and delivering real-world value.
Today, we'll break down this journey into 5 key stages:
𝟭. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮 𝗔𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻:
➡️Business Question: It all starts with a clear business question that ML can help answer. What problem are you trying to solve, or what opportunity are you trying to seize?
➡️Data Acquisition: Once you have a question, you need the data to answer it. This involves identifying, collecting, and cleaning relevant data sources.
𝟮. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴:
➡️Data Preparation: Raw data is rarely ready for ML models. This stage involves cleaning, transforming, and formatting the data to make it usable.
➡️Feature Engineering: Extracting meaningful features from the data is crucial for model performance. This may involve creating new features, combining existing ones, or using feature selection techniques.
𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 & 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻:
➡️Model Training: Here's where the magic happens! You choose an ML algorithm and train it on your prepared data. This involves iterating on different models and hyperparameters to find the best fit.
➡️Model Evaluation: Don't fall in love with your first model! Rigorously evaluate its performance using relevant metrics and compare it to other models or baselines.
𝟰. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴:
➡️Deployment: Once you have a winning model, it's time to put it to work! This involves deploying the model to a production environment where it can make real-time predictions.
➡️Monitoring: Even in production, models need monitoring. Track the model's performance, identify and address any issues, and ensure it's delivering value.
𝟱. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 & 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁:
➡️Feedback Loop: The ML lifecycle is not linear. Continuously gather feedback from the business and stakeholders, use it to improve the model's performance, and re-evaluate the business question.
➡️Continuous Improvement: Machine learning is an iterative process. As new data becomes available and business needs evolve, be ready to adapt and improve your models.
📌 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿, 𝘁𝗵𝗲 𝗠𝗟 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗶𝘀 𝗮 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗲𝗳𝗳𝗼𝗿𝘁.
Data scientists, engineers, business stakeholders, and domain experts all play crucial roles in bringing successful ML projects to life.
By following these stages and fostering a collaborative culture, you can unlock the true potential of ML and turn your business questions into real-world impact.
⭐ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
🔔 𝐍𝐞𝐭𝐟𝐥𝐢𝐱'𝐬 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐨𝐧 𝐚 𝐌𝐚𝐬𝐬𝐢𝐯𝐞 𝐒𝐜𝐚𝐥𝐞
Netflix's database infrastructure is a true marvel! They use a combination of several cutting-edge technologies to ensure content is available 24/7, without buffering or interruptions.
Netflix's engineering team leverages a diverse array of databases to deliver top-notch service. Here's a glimpse into their database selection:
🔍 Relational Databases: For billing transactions, subscriptions, taxes, and revenue, Netflix chooses MySQL. They also harness CockroachDB to support multi-region active-active architecture, global transactions, and data pipeline workflows.
📊 Columnar Databases: Netflix turns to Redshift and Druid for structured data storage, Spark and data pipeline processing, and Tableau for data visualization, especially for analytics purposes.
🔑 Key-Value Databases: Netflix's trusted companion for over a decade is EVCache, built on top of Memcached. It's the go-to for caching various data, powering the Netflix Homepage, and delivering personalized recommendations.
📚 Wide-Column Databases: Cassandra takes the stage for almost everything, from Video/Actor information to User Data, Device details, and Viewing History.
🎮 Time-Series Databases: Netflix's innovation shines with Atlas, an open-source in-memory database designed for metrics storage and aggregation.
⏸ Unstructured Data: When it comes to storing Image/Video/Metrics/Log files, Netflix relies on S3 as the default choice. They also harness the power of Apache Iceberg with S3 for big data storage.
⭐ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devopsdocs
