Techtter

How AI Agents think and respond? #ai #aiengineering

3 weeks ago | [YT] | 4

Techtter

30 Data Pipeline & Architecture Design questions are almost asked in every interviews, both at the fresher and experienced levels in FAANG companies.

𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫-𝐋𝐞𝐯𝐞𝐥 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐃𝐞𝐬𝐢𝐠𝐧
1. Design a Data Pipeline to process logs from web servers.
2. Design a batch ETL pipeline to process e-commerce transactions.
3. Design a streaming data pipeline for real-time stock prices.
4. Design a solution to ingest and store sensor data from IoT devices.
5. Design a data ingestion pipeline for CSV/JSON files from S3 to Redshift.
6. Design a user clickstream data pipeline.
7. Design a pipeline to clean and aggregate marketing campaign data.
8. Design a daily job that syncs data from MySQL to BigQuery.
9. Design a basic data lake architecture.
10. Design a system that processes and analyzes ride-sharing trip data.
11. Design a data pipeline to detect fraud in payment transactions.
12. Design a system to track real-time delivery status in a food app.
13. Design an ETL pipeline for mobile app usage metrics.
14. Design a workflow to migrate data between two cloud environments.
15. Design a pipeline to monitor and alert on data quality issues.

𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐝-𝐋𝐞𝐯𝐞𝐥 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐃𝐞𝐬𝐢𝐠𝐧
16. Design a real-time analytics platform like Uber's Michelangelo.
17. Design a scalable log aggregation and querying system like ELK.
18. Design a CDC (Change Data Capture) system using Debezium and Kafka.
19. Design a batch + streaming hybrid architecture (Lambda/Kappa).
20. Design a warehouse architecture supporting SCD.
21. Design a distributed ETL pipeline using Spark or PySpark.
22. Design a time-series data warehouse for monitoring and IoT.
23. Design an event-driven architecture for order processing using Kafka.
24. Design a metadata management system like Apache Atlas.
25. Design a data catalog and lineage tracker.
26. Design a self-healing pipeline with retry, alert, and failover.
27. Design a real-time dashboard using Kafka + Flink + Druid.
28. Design a scalable system for A/B testing analysis.
29. Design a data pipeline to feed a recommendation engine.
30. Design a multi-tenant data platform for product analytics at scale.

Start implementing to stand out in your next Data Engineer role.

#dataengineering #interviewQuestions #interviewtips

1 month ago | [YT] | 2

Techtter

🎯🚀Data Science → MLOps → AI Agents & RAG
The market moves fast. Here's what helped me the most in each area:

1️⃣ 𝐃𝐀𝐓𝐀 𝐒𝐂𝐈𝐄𝐍𝐂𝐄 𝐅𝐎𝐔𝐍𝐃𝐀𝐓𝐈𝐎𝐍

→ ISLR [Book] - lnkd.in/dTJazEiJ
→ Practical Statistics for Data Science [Book] - lnkd.in/dRYSJ9nq
→ Hands-On ML with TensorFlow & Keras [Book] - lnkd.in/dk7zMbTt
→ ML-For-Beginners [Repo] - lnkd.in/d8kZA3es
→ Neural Networks: Zero to Hero [Repo] - lnkd.in/dHsyfKNk
→ Machine Learning Interview [Repo] - lnkd.in/ddpCihcN

2️⃣ 𝐌𝐋𝐎𝐏𝐒 & 𝐏𝐑𝐎𝐃𝐔𝐂𝐓𝐈𝐎𝐍 𝐒𝐘𝐒𝐓𝐄𝐌𝐒

→ Designing Machine Learning Systems [Book] - lnkd.in/dS3vtyRG
→ Made with ML [Repo] - lnkd.in/e-XQwXqS
→ AWS Cloud Practitioner [Certification] - lnkd.in/dNX3zrSG
→ AWS ML Specialty [Certification] - lnkd.in/dEPE2ie3

3️⃣ 𝐀𝐈 𝐀𝐆𝐄𝐍𝐓𝐒 & 𝐑𝐀𝐆

→ Hands-On LLMs [Book] - lnkd.in/eV4qrgNW
→ LLM Course [Repo] - lnkd.in/dXCAmF_T
→ Prompt Engineering Guide [Repo] - lnkd.in/eu_cx4vC
→ RAG Techniques [Repo] - lnkd.in/dD4S8Cq2
→ AI Agents for Beginners [Repo] - lnkd.in/eik2btmq
→ GenAI Agents [Repo] - lnkd.in/dnhwk75V

4️⃣ 𝐁𝐎𝐍𝐔𝐒: 𝐈𝐍𝐓𝐄𝐑𝐕𝐈𝐄𝐖 & 𝐄𝐍𝐆𝐈𝐍𝐄𝐄𝐑𝐈𝐍𝐆

→ Tech Interview Handbook [Repo] - lnkd.in/dFMBpf2v
→ Coding Interview University [Repo] - lnkd.in/d37ACiZa

These resources will make your journey faster.
💬 Which AI wave are you riding right now? 👇

1 month ago | [YT] | 3

Techtter

Only Roadmap You Need for Al Engineering

GitHub Repo (3.6k starts): github.com/ hemansnation/Al-Engineer-Headquarters

Modules:

1 - Foundations of Al Engineering

2 - Machine Learning & MLOps Fundamentals

3 - Mastering Large Language Models (LLMs)

4 - Retrieval-Augmented Generation (RAG) in Production

5 - Fine-Tuning LLMs

6- Agentic Workflows

7 - Career Acceleration

8 - Bonus

1 month ago | [YT] | 6

Techtter

🎯🚀 5 free resources for 5 types of interviews:


Coding Interview:

0. Coding Interview University (327k stars): lnkd.in/dH9NSDNX
1. Grind 75 Practice Questions: lnkd.in/dbCrn7yw
2. Awesome Leetcode Resources: lnkd.in/dnjwGFjM
3. Neetcode App and Youtube: neetcode.io/
4. The Tech Interview Handbook: lnkd.in/desaJkhu


System Design Interview:

5. Awesome System Design Resources (by Ashish Pratap Singh): lnkd.in/dU6wFXkn
6. System Design 101 (76k stars): lnkd.in/d5itZHgQ
7. System Design Basics Playlist on Youtube: lnkd.in/dN3fpjWZ
8. System Design Github Repo (by Neo Kim): lnkd.in/dbfAK_6A
9. The System Design Roadmap (by roadmap.sh): lnkd.in/d3S79YVJ


Object Oriented Design Interview:

10. Awesome Design Patterns (44k stars): lnkd.in/dyAHdeFi
11. Design Patterns with Visuals: lnkd.in/dnyZXQ52
12. Awesome Low-Level Design: lnkd.in/d9yt9grg
13. Design Patterns Youtube Playlist: lnkd.in/dWyf3nma
14. System Design Primer (259k stars): lnkd.in/dkPScaCW


Frontend Interview:

15. The Front-End Interview Handbook (by Yangshun Tay): lnkd.in/dhXn96yC
16. Javascript Visualized Series: lnkd.in/dHz8RTGD
17. BigFrontend for Practice Questions by Companies: bigfrontend.dev/
18. Front-End Developer Interview Questions (61k stars): lnkd.in/d-bJuE6S
19. Great Frontend to Plan and Practice (aff.): lnkd.in/dEGV-ZfV


Behavioral Interview:

20. Awesome Behavioral Interviews: lnkd.in/dTBvfcau
21. The Behavioral Interview Youtube Playlist: lnkd.in/dgjmKYae
22. The STAR methodology: lnkd.in/dE8yeieq
23. Behavioral Questions with Example Answers: lnkd.in/dUpyuYKr
24. Interview Questions by Company: lnkd.in/dBRmGtaW

#interviewtips #careeradvice #techtter #interviewguidance

1 month ago | [YT] | 2

Techtter

🚀 Looking for your next career move? Meet the Career Compass AI App!

✨ Our Job Matching feature analyzes your CV and projects to instantly match you with roles that fit your skillset.
✅ See how well you align with a role.
✅ Get clear guidance on what to prepare to land the job.
✅ Optimize your CV so it stands out to recruiters (no more being overlooked!).
✅ Receive tailored tips to ace your interview.

Your dream job might be closer than you think.
👉 Check out the app: careercompassai.io/ to try it today!

#CareerGrowth #JobSearch #AIForCareers #CareerCompass #JobMatching #FutureOfWork #CareerDevelopment #AIinRecruitment #CVTips #InterviewPreparation #CareerSuccess #JobOpportunities #RecruitmentTech #CareerJourney #AIJobs

1 month ago | [YT] | 0

Techtter

🚨 Google just made it possible for anyone to build their own AI assistant without touching a single line of code.

It’s free to start, and it works right inside Gemini.

These assistants are called “Gems,” and you can create one in just a few minutes by giving it a name, telling it how it should behave, and adding files or notes for it to learn from.

Here’s how you can set one up:

1. Open Gemini
→ Go to [gemini dot google dot com]
→ Click on Gem Manager and hit "New Gem". 
→ You’ll also see some ready-made Gems like Brainstormer, Career Guide, and Coding Partner if you want to test those out.

2. Define your Gem
→ Give it a name, write down what you want it to do, and use the Magic Pencil to polish your instructions.

3. Feed it knowledge
→ Upload up to 10 PDFs or images that your Gem can use as a reference. 
→ You can swap them out whenever you want.

4. Test and adjust
→ Use the preview window to see how it responds. 
→ Tweak the instructions until it feels right.

5. Keep it flexible
→ You can always go back, edit your Gem, and update its instructions or knowledge.

Once ready, your Gem can live inside Gemini or show up in Google tools like Docs, Slides, Gmail, and Drive.

That means you can call on it to help with writing, planning, coding, or anything else you need while you work.

This is another step toward making AI feel personal, practical, and easy to use in everyday life. And now, anyone can create their own assistant in minutes.

--------

Credit: heygurisingh | X

#ai #geminiai #google #aiagents #agenticai #techtter

2 months ago | [YT] | 4

Techtter

🚨 NEW: Google offers completely free AI courses.

It is not necessary for you to have prior knowledge or pay any fee to learn AI.

Check out 10 must-see courses:

1 - Introduction to Generative AI

A simple and quick course on Generative AI.
Shows how to develop AI applications using Google tools.
It can be completed in just 45 minutes!

Link: lnkd.in/djD5Q32f

2 - Introduction to Large Language Models

Quick course for understanding large language models (LLMs).
It presents the applications of LLMs and methods to improve them.
Lasting 45 minutes, it teaches you how to create apps with AI!

Link: lnkd.in/dGvUvqn6

3 - Introduction to Responsible AI

Introductory course that teaches you how Google implements responsible AI in its products.
It presents the company's 7 AI principles.

Link: lnkd.in/dfHw4u4i

4 - Fundamentals of Generative AI

It combines the Introduction to Large Language Models and Introduction to Responsible AI courses.

Link: lnkd.in/dpakjh6C

5 - Introduction to Image Generation

Introduces diffusion models, a promising category of machine learning models in the field of imaging.

Link: lnkd.in/dvRMbqXh

6 - Encoder-Decoder Architecture

Explains the Encoder-Decoder Architecture used in tasks such as machine translation and summaries.

Link: lnkd.in/dhUGQMjr

7 - Attention Mechanism

Introduction to the attention mechanism, which allows neural networks to focus on specific parts of an input sequence.
The course takes approximately 45 minutes to complete.

Link: lnkd.in/dkNxSdqw

8 - Transformer and BERT models

By completing this course, it is possible to earn a special badge!

Link: lnkd.in/dQKKDrhh

9 - Creating Image Caption Templates

Teaches how to create models to caption images using deep learning.

Link: lnkd.in/dTEy-nGm

10 - Introduction to Generative AI Studio

It explains what Generative AI Studio is, its features, and how to use it through hands-on demonstrations.

Link: lnkd.in/dFtt-HYs
#ai #aiagents #googleai #careertips #careerguidance #techtter

2 months ago | [YT] | 6

Techtter

🚀 20 GenAI and AI Agents courses and curated 3 courses per Persona taught by Microsoft, NVIDIA, Google, and Hugging Face and ...👇

1. AI Agents for Beginners
lnkd.in/gSNnSK2k
Who: Devs and researchers starting from zero
11-lesson GitHub based course on agents, RAG, memory, and multi-agent systems
Why: Teaches real-world workflows with actual code

2. Hugging Face Agents Course
lnkd.in/gvkBA2vX
Who: Builders who want to learn by building
Interactive training with LangChain, LlamaIndex, memory, evals, and observability
Why: Feels like a playground. Open-source and community-driven

3. Generative AI Explained (NVIDIA)
lnkd.in/g4yNPvgK
Who: Curious minds, PMs, and analysts
Covers GenAI concepts, use cases, and applications without code
Why: Fast, clear, and digestible. Perfect before you start coding

4. Intro to Vibe coding🚀
bit.ly/46wv7Mq
Learn to Build Full-Stack Apps with AI
In this free 4-week course, master vibe coding—just describe your idea, and AI helps turn it into a working app.

5. Generative AI for Beginners (Microsoft)
lnkd.in/gKBNqFDQ
Who: Full-stack devs and curious tinkerers
21 lessons from prompt design to deployment
Why: Structured, example-rich, and project-based

6. Learn generative AI by Amazon Web Services (AWS)
lnkd.in/gG8Gvqnq
Who: Devs who want a structured on ramp to AWS AI services
Learning modules covering prompt engineering, AWS Bedrock agents, guardrails with hands-on labs.
Why: Learn how to build GenAI products using Amazon AI stack bridge theory and cloud implementation

7. Google Cloud Skills Boost: GenAI Track
lnkd.in/gYFcZmqb
Who: Professionals upskilling with certifications
Labs and badges covering LLMs, prompting, and safety
Why: Makes your resume GenAI-ready with cloud context


👇 Which path fits you best?

✔️The Student → Future AI Engineer
Goal: Learn GenAI fundamentals, hands-on coding, and build a portfolio
1-Generative AI for Beginners (Microsoft)
2-AI Agents for Beginners (Microsoft)
3-Into to Vibe coding

✔️The Entrepreneur → AI Product Builder
Goal: Build MVPs fast and evaluate GenAI platforms
1-Hugging Face Agents Course
2-Building RAG Agents (NVIDIA)
3-Into to Vibe coding

✔️The Educator or Researcher
Goal: Gain conceptual depth and teach others
1-AI for All (NVIDIA)
2-Generative AI Explained (NVIDIA)
3-AI Agents for Beginners (Microsoft)

✔️The Product Manager or Strategist
Goal: Evaluate tools and shape AI strategy
1-Google Cloud Skills Boost
2-Hugging Face Agents
3-Generative AI for Beginners (Microsoft)

Learn by doing, don’t wait for the perfect moment
Pick one small task, apply what you learn

2 months ago | [YT] | 10

Techtter

25 blogs, 25 data engineering concepts 👇


You want to learn Data Engineering from the ground up? Here’s a list that covers all the key concepts you will use in real projects.

Here are 25 blogs to guide you through every important concept 👇

1. Data Lake vs Data Warehouse
→ lnkd.in/gEpmTyMS
2. Delta Lake Architecture
→ lnkd.in/gk5x5uqR
3. Medallion Architecture
→ lnkd.in/gmyMpVpT
4. ETL vs ELT
→ lnkd.in/gvg3hgqe
5. Apache Airflow Basics
→ lnkd.in/gGwkvCXd
6. DAG Design Patterns
→ lnkd.in/gHTKQWyR
7. dbt Core Explained
→ lnkd.in/g5mQi8-y
8. Incremental Models in dbt
→ lnkd.in/gS25HCez
9. Spark Transformations vs Actions
→ lnkd.in/g2RRCGMW
10. Partitioning in Spark
→ lnkd.in/g5fXjSJD
11. Window Functions in SQL
→ lnkd.in/gupxmxvu
12. Slowly Changing Dimensions (SCD)
→ lnkd.in/gVFQmnuf
13. Data Modeling (Star vs Snowflake)
→ lnkd.in/gEP6Dacb
14. Data Quality with Great Expectations
→ lnkd.in/g84tGjBA
15. Data Lineage & Cataloging
→ lnkd.in/gT-GcF3a
16. Apache Kafka 101
→ lnkd.in/gHfDGa2d
17. Batch vs Stream Processing
→ lnkd.in/gPZt-pwd
18. PySpark Optimization Tips
→ lnkd.in/gQ6DXgDU
19. Auto Loader in Databricks
→ lnkd.in/gJiuYCQU
20. Delta Live Tables
→ lnkd.in/gn3AuZep

#dataengineering #snowflake #spark #dbt #techtter

2 months ago | [YT] | 12