Basics - Multivariate testing vs A/B test

Multivariate testing - A technique for testing a hypothesis in which multiple variables are modified. A Vs B Vs C Vs D Vs E. . An MVT allows marketers to understand the impact of changing a number of elements at once (e.g. headline and image

A/B test has isolated variations made beforehand. A vs B. A/B tests are best for testing the global maximum.

• Optimizely - One of the most well-known optimization platforms that offer a wide range of testing options, including A/B testing, multivariate testing, and personalization.
• VWO (Visual Website Optimizer) - Provides a suite of testing tools, including A/B testing, split testing, multivariate testing, and other features like heatmaps, session recording, and more.
• Adobe Target - Part of Adobe's marketing cloud, Adobe Target offers A/B and multivariate testing along with personalization and is a tool used by many enterprise-level businesses.
• Google Optimize - Google's testing and personalization platform integrates with Google Analytics and offers both A/B and multivariate testing capabilities, as well as redirect tests.
• Unbounce - While primarily known for its landing page builder, Unbounce also offers A/B and multivariate testing tools specifically geared towards optimizing landing pages for higher conversions.

We used to do something similar called weblabs experimentation during my days at Amazon

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The Future of Product Innovation is Here! 🌟 #NextGenIdeas #CollaborativeMagic

• 🎯 Go niche with a Domain-Specific GPT. Why settle for generic when you can bring your own data and craft a model that knows your field inside out? #CustomizedAI #FocusedExcellence
• 💡 It's a Multimodal World! Knowledge isn't just text; it's Images + Text + Data. Embrace the power of combined data forms to receive enriched, multimodal insights that tell the complete story. #HolisticAI #MultimodalKnowledge
• 🚀 Supercharge your innovation engine with Ideas an ensemble of Models. Watch your prototyping speed take off as diverse AI models converge to refine your visions faster than ever! #SpeedyPrototyping #AIEnsemble
• 🛡️ Wave goodbye to one-size-fits-all solutions and hello to Personal Data + Private LLM. Your privacy remains intact while you enjoy tailor-made advice crafted just for you. #PersonalizedAdvice #PrivacyMatters
• 🛠️ Be part of the vanguard shaping AI Infrastructures. It's not just about building models; it's about building them right—robust, reliable, and fair. For all infrastructure providers, evaluators, and advocates for responsible AI, your insights are invaluable. #AIEthics #ResponsibleAI
• Let's merge creativity with cutting-edge tech to craft products that not only resonate but revolutionize. Your privacy, our innovation—let's create the future, together.

Make the leap into a new era of product development and #TransformativeCollaboration today! 

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Roles vs Positions vs Titles

• Developer - Ownership of module - Tech knowledge
• Sr.Developer - Ownership of code + module design + QA - Tech + QA + Domain knowledge
• Lead - Ownership of certain functionality + Support - Tech + QA + Support + Domain knowledge
• Manager - Ownership + Design + Delivery - Tech + QA + Support + Domain knowledge + Delivery
• Sr.Manager - Ownership and initiatives - Tech + QA + Support + Domain knowledge + Delivery + Architecture 
• Director - Run the Team + Envision New Initiatives + Code / Adopt New tech - Tech + QA + Support + Domain knowledge + Delivery + Architecture 

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Ideas - Solutions - New Perspectives

• Some knowledge is from what we worked
• Some knowledge is from what we read
• Some knowledge is from what we were referred
• The solution is always evolving

Ideas may not always have past references :)

Something Trendy <> Good

Donald Knuth advice to young engineers -
- Follow your own instincts.
- Prestige comes from doing good science, not just popular science.
- Deep knowledge builds confidence.
- A liberal education involves learning everything about something and something about everything. pic.twitter.com/LAqvitXvxq

— Vipul Vaibhaw (@vaibhaw_vipul) November 24, 2023

War <> Advanced Soceity

Kindness, Empathy Creativity is more important than Grudge, Crooked, Wicked intentions

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ML to predict Load, Handle requests, Anamoly + Load Forecast

Mitigating DDoS with data science using AWS Shield Advanced and AWS WAF

• Time series - events are fed into a time-series database in near real time and generate insights using machine learning (ML) models
• Generating confidence percentage that helps in defining further action, verifies consumer authenticity, and serves the request. It also blocks malicious requests at the edge. Identify malicious patterns
• Forecast Load - Derive pattern-based rate limits: Deriving rate limits based on a larger set of data—including consumer and IP address—by looking at weekly and monthly patterns.

Black Friday/Cyber Monday [BFCM] is a big deal for our users. So it’s a big deal for @stripe too.

We handled billions of $s in sales each day of BFCM. A 5 minute downtime = tens of millions in lost revenue for our users.

Here's how we achieved >99.9999% uptime. 👇 https://t.co/eDYrgy5CWb

— David Singleton (@dps) November 29, 2022

• Our data science and eng teams build rigorous models based on historical data at both a Stripe-wide and user-by-user level.
• We build resilient systems to support spikes and flash sales, and scale our systems to handle more than the predicted peak.

How Razorpay handled significant transaction bursts during events like IPL

• Rate-limiting and throttling were implemented to safeguard their system against a deluge of requests also DDoS attacks
• The machine learning system consumed payment success and failure events to predict in real-time where the payment requests should be directed.

The Making of Developer-Console

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LLM Notes

LLM Notes from Andre Karpathy Slides

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Instant Demo Creation - Tools and Ideas

Step #1- Tool - https://scre.io/

Step #2 - Change Format - ffmpeg -i demo1.webm -strict experimental video.mp4

Step #3 Trim it - ffmpeg -i video.mp4 -ss 00:00:08 -t 00:02:43 -async 1 cut.mp4

Step #4 - Tweak Edges - ffmpeg -i Case_Study.mp4 -ss 00:00:00 -t 00:02:37 -async 1 Case_Studyv1.mp4

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Secure Google App Engine App - Streamlit App

1. Steps to build/ deploy sample App

2. Enable IAP for App Engine

Add users

To grant access to the application, 

click "Add Principal" 

select the IAP-secured Web App User role

Securing App Engine apps with IAP

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Learning Materials - DL - ML - DE - MLOps - NLP

Ready to Learn collated examples

• machine-learning-zoomcamp
• zoomcamp-analytics
• data-engineering-zoomcamp
• mlops-zoomcamp
• MLOps project with Loan Data
• Large Language Models Course
• Google-Machine-Learning-for-Solutions-Architects
• A collection of design patterns/idioms in Python
• How to add tests to your data pipelines
• Data Pipeline Design Patterns - #1. Data flow patterns
• Data Pipeline Design Patterns - #2. Coding patterns in Python
• energy-forecasting
• How to build and automate a python ETL pipeline with airflow on AWS EC2 

Good Data related papers - Link

Neatly organized here: Link

Good Data Engineering Papers

• BtrBlocks: Efficient Columnar Compression for Data Lakes
• The Story of AWS Glue
• Building a serverless Data Lakehouse from spare parts⋆
• The Snowflake Elastic Data Warehouse

Updated list of engineering papers worth reading.

1. Google File System - https://lnkd.in/d2-wnyqZ

2. Map Reduce Big Data Algorithm - https://lnkd.in/dvE8-s8M

3. BigTable NoSQL Document Store - https://lnkd.in/drmvvSAK

4. Colossus Next Gen File Store - https://lnkd.in/dERKhwMf

5. Megastore Large Object Store - https://lnkd.in/d5JDs2-K

6. Monarch Time Series DB - https://lnkd.in/d3kH_NCp

7. Chubby Distributed Lock Management - https://lnkd.in/dYy-w5rW

8. Spanner Distributed Database - https://lnkd.in/d6Emnycp

9. Spanner - CAP theorem considerations - https://lnkd.in/dq29BAWQ

10. Dapper Tracing System - https://lnkd.in/dm36-6jn

11. Borg Cluster Management - https://lnkd.in/dnveV-HU

12. Zanzibar Authentication System - https://lnkd.in/d5Vf7sRD

13. Pregel Graph Processing - https://lnkd.in/daq4576Y

14. Napa - Data Warehousing - https://lnkd.in/dbEfsa5B

15. Napa - Partitioning Algorithm - https://lnkd.in/dkhA7efJ

16. TensorFlow - Machine Learning at Scale - https://lnkd.in/d-4NfV2Z

17. Google F1 - Fast Analytics - https://lnkd.in/dbZqEKuf

18. HALP - YouTube Content Delivery Network - https://lnkd.in/dHzJtUc7

19. Mesa - Data Warehousing - https://lnkd.in/dFJ_Jrz6

20. Google Firestore - https://lnkd.in/drtEN9qR

21. Amazon Aurora DB Architecture - https://lnkd.in/dcevpwFt

22. Dynamo DB NoSQL Database - https://lnkd.in/dMD8C_WK

23. Apple Foundation DB - NewSQL database - https://lnkd.in/dG75i_9K

24. TikTok Monolith - Embedding in real-time - https://lnkd.in/dcjBXCnc

25. Scalability at what COST - https://lnkd.in/dJ9ScYKq

26. Gorilla - Time Series DB - https://lnkd.in/d3AeN2kB

27. Cassandra - NoSQL DB - https://lnkd.in/d-_nhtED

28. FlexiRaft - Distributed Consensus Tradeoffs - https://lnkd.in/dX3nMvmt

29. Memcache - In-memory Cache at Facebook - https://lnkd.in/dKeYK67g

30. Millisampler Network Sampling - https://lnkd.in/dsj9FuD6

31. TAO Graph Database - https://lnkd.in/daasJpYf

32. MineSweeper - Root Cause Analysis - https://lnkd.in/dEsd6iwj

33. Facebook Prophet - Forecasting at Scale - https://lnkd.in/daCmAjak

34. Facebook ShardManager - https://lnkd.in/dDy9Dp2h

35. Hive - Map Reduce Jobs - https://lnkd.in/dpV8BM2R

36. Apache Thrift - Definition Language - https://lnkd.in/d7NzhP54

37. Meta Twine - Cluster Management System - https://lnkd.in/d5t7VFKE

38. Meta ServiceRouter - Service mesh - https://lnkd.in/dVnkv_bV

39. Apache Hadoop - Distributed File System - https://lnkd.in/dHsQu9FN

40. Apache Kafka - Event Bus - https://lnkd.in/dyxuKbMb

41. Apache Flink - https://lnkd.in/dn_gMvaR

NLP

NLP-Python

Advanced_NLP_Spacy

Vision Notes

The U-Net (actually) explained in 10 minutes

U-Net Implementation With PyTorch

Efficient Python Tricks and Tools for Data Scientists

mathdatasimplified

It takes time to read - learn - experiment - build expertise. Learning Never Ends !!!!

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Forecasting - nixtla

A ton of Algo's consolidated and released - nixtla

There's an ever-growing forest of related projects out there, including:

• skforecast 
• functime 
• pmdarima
• arch
• prophet
• greykite
• orbit-ml
• timeseers
• pyflux
• gluonts
• StatsForecast 
• AutoGluon Time Series
• Codes
• StatsForecast and its Ray integration

TimeGPT

• TimeGPT does zero-shot inference
• Allows forecasting of multiple time series.
• TimeGPT leverages the Transformer for forecasting tasks.

Ref - Link

Temporian is a library for safe, simple and efficient preprocessing and feature engineering of temporal data in Python.

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