- M.Tech, AI & ML — BITS Pilani (2023–2025)
- M.Sc, Information Technology — Punjab Technical University (2000–2001)
- B.Sc, Computer Science — Andhra University (1994–1997)
- B.Sc, Sciences (Maths, Physics, Chemistry) — Andhra University (1998–1999)
- 🌱 Learning deeper concepts in data engineering, ML pipelines, and scalable systems
- 👯 Interested in collaborations across Digital Engineering, Geospatial AI, and Decision Intelligence
- 🧠 Polyglot developer working across Java, Scala, and Python
- ☁️ Advocate for cloud-native architecture, containers, and secure engineering
- 🧪 Passionate about clean code, rapid prototyping, and microservice design
- 🚴 Enjoy cycling, minimalism, green growth, and nature-connected living
📬 Reach me at preetam.balijepalli@gmail.com
🔗 Connect on LinkedIn
🌐 Visit balijepalli.com
| Category | Tools & Technologies |
|---|---|
| Languages | Java (Spring), Scala (Big Data), Python (ML/Scripting) |
| Frontend | React, Angular, Jinja2, Admin Dashboards |
| APIs & Eventing | GraphQL (Hasura), REST APIs, Webhooks, Kafka, SNS, SQS |
| Databases & Storage | MySQL, PostgreSQL, MongoDB, Sybase, Aurora, Redshift, Athena, DynamoDB, DuckDB, Druid, S3 |
| Frameworks & Patterns | Spring Boot, Spring Cloud, Spring Batch, Liquibase, Flyway, CQRS, Event Sourcing |
| Cloud & Infrastructure | AWS (EKS, Fargate, EMR, Lambda, SageMaker, CloudWatch, SES, CloudFormation), IaC (Terraform/CDK), DevOps |
| Data & ML Pipelines | Kedro, Airflow, Spark (EMR), MLflow, Cloudera, Hortonworks, Data Mesh |
| Model Serving | Seldon Core, KFServing, BentoML, SageMaker Inference, REST/gRPC endpoints |
| CI/CD & VCS | GitHub, GitLab, Bitbucket, Gerrit; CI/CD pipelines with model testing & deployment |
- 🍽️ Retail AI — Demand prediction & inventory analytics
- 🌾 AgriTech — Yield optimization, remote sensing (3+ yrs)
- 🏦 BFSI — Risk modeling, fraud detection (10+ yrs)
- 🚦 Mobility — Smart traffic and fleet platforms (4 yrs)
- 🗺️ Geospatial AI — Location intelligence & mapping APIs (3+ yrs)
📖 Currently Reading
- Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples by Serg Masís (⭐️4.33)
- Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow by Hannes Hapke (⭐️3.49)
- Designing Data-Intensive Applications by Martin Kleppmann (⭐️4.71)
- Building Machine Learning Powered Applications: Going from Idea to Product by Emmanuel Ameisen (⭐️4.21)
- Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith by Sam Newman (⭐️4.27)
- Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema by Lawrence Corr (⭐️4.04)
- Agile Testing: A Practical Guide for Testers and Agile Teams by Lisa Crispin (⭐️3.81)
- Growing Object-Oriented Software, Guided by Tests by Steve Freeman (⭐️4.19)
📗 Recently Read
- AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide: The definitive guide to passing the MLS-C01 exam on the very first attempt — Somanath Nanda (⭐⭐⭐⭐)
- Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow — Saket Mengle (⭐⭐⭐⭐)
- Data Algorithms: Recipes for Scaling Up with Hadoop and Spark — Mahmoud Parsian (unrated)
- Programming Interviews Exposed: Secrets to Landing Your Next Job (Programmer to Programmer) — John Mongan (unrated)
- Planning for Big Data: A CIO's Handbook to the Changing Data Landscape — Edd Wilder-James (unrated)
- [Enterprise Integration Architecture: EAI vs ESB vs SOA - a Modern Perspective](http://balijepalli.com/2025/04/21/eai-vs-esb-vs-soa/) - Apr 21, 2025- [Nine Things Successful People Do Differently](http://balijepalli.com/2011/06/07/nine-things-successful-people-do-differently-heidi-grant-halvorson-the-conversation-harvard-business-review/) - Jun 7, 2011
From: 22 September 2015 - To: 09 December 2025
Total Time: 1,179 hrs 26 mins
Python 617 hrs 28 mins █████████████░░░░░░░░░░░░ 52.35 %
Other 118 hrs 15 mins ██▓░░░░░░░░░░░░░░░░░░░░░░ 10.03 %
TypeScript 72 hrs 28 mins █▓░░░░░░░░░░░░░░░░░░░░░░░ 06.14 %
YAML 63 hrs 49 mins █▒░░░░░░░░░░░░░░░░░░░░░░░ 05.41 %
Text 50 hrs 59 mins █░░░░░░░░░░░░░░░░░░░░░░░░ 04.32 %
Docker 40 hrs 15 mins █░░░░░░░░░░░░░░░░░░░░░░░░ 03.41 %
Bash 39 hrs ▓░░░░░░░░░░░░░░░░░░░░░░░░ 03.31 %
Markdown 36 hrs 57 mins ▓░░░░░░░░░░░░░░░░░░░░░░░░ 03.13 %
JSON 27 hrs 55 mins ▓░░░░░░░░░░░░░░░░░░░░░░░░ 02.37 %
JavaScript 24 hrs 59 mins ▓░░░░░░░░░░░░░░░░░░░░░░░░ 02.12 %