yes, it is here you go : https://t.co/xi1ZSjsAX0 drop a good morning message from the contact form :)🌄🌄🦈

update: the three pillars, the three core agents are ready :)

here's my review of the CHAPTER-01 of this book : PRACTICAL MLOPS by: Noah Gift & Alfredo Deza

9. MLOps is only possible when you have: ---DevOps foundation --- Data automation --- Platform automation Skip these, and you're building on sand. The culmination? A machine learning system that actually works in production. Start with the foundation.

8. What is MLOps Really?: MLOps = Automating machine learning using DevOps methodologies Machine Learning Engineering = The method of building machine learning Key insight: MLOps is a behavior, just as DevOps is a behavior It's not just tools - it's a culture and practice.

7. Many organizations use centralized data lakes as the foundation for data engineering and ML. Why? Near-infinite scale for I/O High durability High availability This makes automation possible at scale.

6. You can't skip steps. Here's the foundation needed: DevOps (base layer) Data Automation (centralized data lake) Platform Automation MLOps (true ML automation) Each layer builds on the previous one.

5. A Makefile is an ideal starting point for CI/CD automation. It runs "recipes" via the make system on Unix-based OS. It simplifies continuous integration steps and evolves as your project grows. Pro tip: Every MLOps team member should help develop and maintain the CI/CD system.

4. Monitoring MattersYou can't improve what you don't measure. Monitoring and instrumentation = data science for deployed systems Tools: New Relic, DataDog, Stackdriver This allows organizations to make informed decisions about performance and reliability.

3. Two core DevOps practices: Continuous Integration (CI): Automated testing of your software project continuously (GitHub Actions, Jenkins, CircleCI, AWS CodeBuild) Continuous Delivery (CD): Automated deployment to new environments without human intervention

2. Software engineering solved similar problems with DevOps: a set of practices that increase velocity in releasing high-quality software. Key benefits: Speed Reliability Scale Security ML is now following the same path with MLOps.

1. The Problem: The data science industry is immature compared to software engineering. ML teams often focus on "code" and technical details instead of solving actual business problems. The expression goes: "If it's not automated, it's broken"

Most ML projects fail not because of bad models, but because of everything around them: data engineering, infrastructure, and business alignment. The real challenge? Getting ML models into production. Here's what you need to know about MLOps 🧵

Diving into this wonderful book starting today, will be sharing what i learn along the way, join in if you are to curious about what does actually a MACHINE LEARNING ENGINEER DOES?

Finally started working on this project I’ve been wanting to do forever :)

Backend isn’t just APIs and CRUD. This is the roadmap companies like Google, Meta, Amazon expect you to know. DSA > Companies like Google don’t care about your projects if you fail here. > This is the real filter. > Conquer this, and every door in tech opens. > Struggle here,

Have you used agentic AI or autonomous workflows in your MLOps? What worked, and what didn’t?

What’s the most frustrating part of collaborating on ML projects? Is it code, data, models, or something else?

DevOps teams: How do you handle ML model deployments in your org? What works, and what’s a nightmare?

Data scientists and ML engineers: What’s your ideal MLOps workflow look like? What tools do you use, and what’s missing?

Which MLOps pain point hits you hardest?