My Certification Roadmap: One Year, Six Milestones 🎯

🗓️ One-Year Certification Plan

Here’s the roadmap I’ve laid out for myself:

Goal Date: July 21st
Kubernetes is the backbone of modern DevOps and microservices. Mastering it will strengthen my ability to deploy, scale, and troubleshoot containerized applications.

Goal Date: September 15th
Databricks is transforming how we handle big data and AI workflows. This certification will deepen my knowledge in Spark, Delta Lake, and data engineering at scale.

Goal Date: November 10th
I want to validate my cloud AI knowledge either through AWS AI/ML certification or NVIDIA’s Deep Learning Institute track. This one’s all about blending cloud and intelligence.

Goal Date: January 27th
Depending on evolving interests, I’ll either focus on Google’s ML-centric data pipelines or strengthen my Azure development skills. Both are highly relevant for enterprise systems.

Goal Date: March 23rd
Snowflake remains core to my data warehousing toolkit. Recertifying ensures I’m up-to-date with new features and best practices.

Goal Date: May 18th
Whichever cloud certification I didn’t take earlier, I’ll complete it here. This will round off my cross-cloud capabilities.

Alongside the certification roadmap, I’m also sharpening my problem-solving and system design skills through daily practice and projects:

🔍 Blind 75 LeetCode – Revisiting core DSA patterns to strengthen my fundamentals
🧮 SQL + Pandas problems – Focusing on real-world data wrangling and analytics
🏗️ System Design – Studying scalable architecture patterns and distributed systems
⚡ Apache Spark Problems – Practicing transformations, RDD/DataFrame operations, and optimizations
🤖 Applied AI Course – Deep diving into ML algorithms, model deployment, and AI workflows
🧪 Kaggle Projects – Applying ML and data engineering to practical datasets and competitions

“Learning never exhausts the mind.” — Leonardo da Vinci