Decisions vs Perspectives

 

Steve Jobs On How To Resolve Conflict:

(1) Don't try to get someone to 'buy into a decision' because you should hire people so phenomenal they tell YOU what to do. Thus, if you're trying to get them to buy into a decision, you've hired them to do what they think is right (likely… pic.twitter.com/8h26DaVr1u

— Big Brain Business (@BigBrainBizness) December 11, 2024

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Career Perspectives

Few key things that apply to me :)

• Build key relationships by networking and offering something valuable - ideas/playbook / POC.
• Fifth, select challenging projects that promise significant early wins, even if they require extra effort initially.
• Finally, at the end of the month, create a reflective document detailing your observations to better understand and improve your approach.

Ref - CAREER ADVICE: First 30 days as an exec.

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My Consulting Journey - AI - DL - GenAI Projects

As I wrap up my consulting tenure, I reflect on my success stories in the past 4 years. Here are some key projects that serve as my badges of success:

Bundle Recommendations Project #1 - Bundle recommendations for a specialty retailer of children’s apparel, from newborns to pre-teens (2020) Work/Impact - Transitioned from automated merchandiser-based recommendations to ML-based bundle recommendations. Achieved a 100% match with the ML approach. For a category level, we analyzed 6 months of transactions, comprising 1.5 million orders, and generated recommendations in 15 minutes.

Performance Optimization Project #2 (2021) - For a multinational mining company, optimized an existing app, more akin to a trading app, deployed between OLAP vs. OLTP. Applied a blend of DB/user and usage analysis/patterns/ML-based techniques to provide a list of recommendations to optimize.

GenAI + Vision Project #3 (2023-2024) - For a British multinational fast-moving consumer goods company, My key contribution is solution architecture based on Vision + GenAI for product detection and personalized recommendations, for its customers' products and brands.

Plants Classification Project #4 - Developing vision-based state-of-the-art classification models for the world's leading gardening charity. This work involved data curation, augmentation, and training, and ended as a paper :). Link

GenAI and CX improvement Project #5 - For a US-based leading specialty retailer of organizing solutions, custom spaces, and in-home services, leveraging GenAI + Vision to improve the customer journey. Pitched/deployed selected use cases. This is similar to what you see in Amazon/Swiggy GenAI Changes.

Forecasting Project #6 - Domain played a key role for me to contribute. For a leading South American beauty retailer, developing forecast models.

I had a mix of responsibilities as a Solution Architect, DB, and ML Engineer. I relied mostly on SA/DB/ML. In all projects, The team was a mix of platform, MLOps, and ML engineers. Sometimes the platform is a vendor cloud or an in-prem solution.

Hoping to undertake a few more similar projects in my next self-employed consulting roles.

If you are a startup, or SMB looking to apply AI/ML in your solutions, We can connect and collaborate on your AI Strategy. My coordinates [sivaram2k10][at][gmail]

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Data - Use Case - Iterative Thinking - Evolving solutions

Many times learning comes from people around us. For vehicle PPF it gave a lot of insights

• While removing all the door cladding/mirrors, the 0-2yrs exp people were putting all screws together
• While fitting on screws, Same dimension screws were applied for two parts, There was confusion on pending screws
• The PPF person was very focused, and the sun film person was a separate person

Three things to build a solution

Many times learning comes from people around us. For vehicle PPF it gave a lot of insights

Observation #1

• Fresher Lens -  While removing all the door cladding/mirrors, the 0-2yrs exp people were putting all screws together
• Experience Lens  - The person (lead) he asked them group based on the parts
• Lesson - group problems, data logically to debug / build solution

Observation #2

• Fresher Lens - While fitting on screws, Same dimension screws were applied for two parts One screw was perfect silver, and another was perfect black, When all screws were filled, juniors were clueless about where it had to be fitted
• Experience Lens  - The lead was able to provide clarity to fit the connecting dots
• Lesson - Leadership is solving with what you have, not afraid to look back and rework where it is needed

Observation #3

• The PPF person was very focused, the sun film person was a separate person
• Lesson - Build relationships, cannot solve all problems all alone
• Be good at a few, use expertise when you need a great product

What does it imply in the AIML context

• Inexperienced team members initially consolidated all components indiscriminately during disassembly. The team leader guided them to categorize components systematically, akin to structuring data for efficient algorithmic problem-solving.
• Different screws of identical size were used interchangeably during the assembly process, leading to confusion. The leader demonstrated critical thinking, using available resources to retrospectively address the issue, a trait essential for refining machine learning models.
• Task specialization was evident; individuals focused on PPF or sun film application roles. This mirrors the need for specialization and collaboration in AIML, leveraging cross-disciplinary expertise to enhance overall model performance.

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Startup Objectives

Startup Objectives 

• Think problems not tools
• Clear Goals
• Team / Solopreneur
• Tools / Inputs
• Data to Collect  / External Sources
• Data Collection

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My Consulting + Product Journey

• AI Roadmap - Data Analysis, Availability, Staged use case Adoption, Roadmap for 3PL, Beauty, Fashion, Retail, Media
• Products - Conceptualized, Solution Development, Implementation of Vision-based products in Agriculture, Fashion, FMCG
• Training - SME for AI / ML Training for product managers. Completed 4 batches covering use cases in Retail, Fashion, FinTech, Energy
• Domains - 3PL, Reverse Logistics, Retail - Planogram, Inventory Management, Loss Prevention, E-commerce
• Production Implementation - Recommendations, Vision Solutions, Forecasting, GenAI Adoption

Will share a few more specific examples coming weeks :)

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70 hours work week VS Thinking perspective VS Consulting vs Domain Knowledge

Numbers do not reflect the quality of outcome rather consistency, ideas, and experimentation matters

Steve Jobs on Continuous Process Improvement

• The theory behind the question of why we do ?
• Ways of doing things? Question the basics? Shift to an Optimistic point of view of Relooking options?
• The shift of perspective / optimistic point of view?
• That's the way it's done vs Finding new ways/opportunities

Steve Jobs on Consulting

• Owning/working extended period few years
• See-through actions / accumulate scar tissues
• Learning a fraction vs Not owning results
• Picture of a banana vs 2D vs Experience of doing vs 3D views
• Knowing is a 2D view vs doing is a 3D view (Hands-on Matters)
• Take responsibility for work

This was the key lesson for Why I was able to rewrite the warranty engine in Microsoft for 224 million serial numbers

Team - Teamwork 

"My business model is The Beatles. They were four guys who kept each other's kind of negative tendencies in check. They balanced each other, and the total was greater than the sum of the parts. That's how I see business: Great things in business are never done by one person, they're done by a team of people."

Ideas to Reality

This lies in process / constant innovation

It is compounding value over the years :)

Hire people trustworthy plus have complementary skills. Aligned on vision but diverse in skills

Arrogance - Keep a Tab

The biggest enemy of continuous growth and progress is arrogance.

“All of us need to be on guard against arrogance, which knocks at the door whenever you are successful.” —Steve Jobs pic.twitter.com/NOLL1MLjgl

— Vala Afshar (@ValaAfshar) December 9, 2023

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What fails and Why it fails

What is Learning in understanding/delivering solutions

• Solving the real issue, Looking beyond one-liners
• Spending time on data, people, process to know ideal vs actuals
• Connecting with business to know their unspoken needs
• Be ready to share your though and accept mistakes to learn

What are mistakes in implementation / Experience vs. Expertise

• My way is the only way, Highly opinionated
• Flexibility between tech vs. business views
• Solve to root cause vs. namesake
• One-stop solution for everything vs. fixing what matters most

Microsoft / Wipro / Nestle / Amazon / Tyco has taught me more patience/product mindset vs. solving problems that matter!!!!

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Consulting - People Relationships

Consulting - People Relationships

• Be transparent of skills - Do not cook up what you don't possess
• Document what you know, Be aligned with assumptions
• Frame better and be clear on the formula
• When you do not know something, Provide context with some prework than blanket statements
• Know when to disagree with respect, When to ask for help, and When to provide help
• Apologize if you made a mistake and call out how you avoid it
• When dealing with touch people be slow before you try to defend
• No matter what some people will hate you for no reason, Just don't focus on them

What makes a Team Player

• Not a red flag raiser every time
• Be clear-cut in your deliverables
• Ability to prepare/ask questions upfront
• Proactive to connect dots 
• Think with clarity or take time to connect the dots
• Take vacation but ensure people don't get blocked in your absence
• Focus on few things than faking everything

Knowledge vs Experience

• I always use to say common sense and domain knowledge helps me more than ML out of box techniques
• Leveraging any out of box techniques is good to solve the problem but being able to communicate to business is key
• I would pick a domain + common sense solution before applying any technique which I cannot justify in business-acceptable discussions
• Consulting makes you take multiple roles - product manager - data engineer - tester - program manager - developer - ML engineer endless :) :) 
• Goal-based not task-based, Ability to foresee what others don't see makes an experienced engineer :)

An incomplete list of skills senior engineers need, beyond coding

• How to run a meeting, and no, being the person who talks the most in the meeting is not the same thing as running it
• How to write a design doc, take feedback, and drive it to resolution, in a reasonable period of time
• How to explain a technical concept behind closed doors to a senior person too embarrassed to openly admit that they don’t understand it
• How to lead a project even though you don’t manage any of the people working on the project
• How to give up your baby, that project that you built into something great, so you can do something else
• How to craft a project proposal, socialize it, and get buy-in to execute it
• How to tell someone they’re wrong without making them feel bad
• How to pick your battles

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3.5 years of AI / ML work and some key projects

Some key moments of the past 3.5 years of work 

• Setting up / Driving a Virtual NRF - Driving everything from India
• Strategy roadmaps based on the current state of multiple customers in  Retail, Logistics, Beauty, etc..
• Vision-based products / Roadmaps / Production Architecture - Virtual Tryon
• DAM / Stylitics / Try on Solutions 
• Vision Products / Projects - Skin Care / Leaf-based plant classification
• Forecasting projects - 300K products for Beauty product retailer
• Bundle recommendations - For a clothing retailer, Figuring out what sells well
• 2 months AI + Analysis + Troubleshooting a slow-performing Trading App (Air Crash Investigation type work :))
• Image Search Engine, Image Catalog creation
• A ton of tech reviews, architecture discussions
• Code up as and when needed, Code / Learn / Handle both tech + business audience
• Three batches of training AI / ML for 150+ product managers, Publishing POV
• Reporting feedback/improvements for AzureOpenAI, Google partners, etc.
• Currently in LLM, GenAI Mode
• External Talk in One of the Conferences for Virtual Try on
• Almost all good moments, except a few situations where I would be cautious if I spot such symptoms
• Lot of coding/teaching for Upgrad :) to get better at basics :)
  

In the next 5 years how we code, will it be mostly prompt-based or idea based looks like a lot of new approaches in development :)

• Tyco and Microsoft taught me a lot of domain
• Even after a master's in ML, Domain knowledge + Common sense helps me more than ML views
• Still could recollect key tables at least 100 tables in Reverse Logistics work - Product, Warranty, Msops, SST Tracking, Repair, Warranty etc..
• The warranty rewrite work is still memorable and applicable till today
• The 3PL touch point connectivity is still relevant in supply chain visibility
• Sensormatic gave the RFID + EAS + People counting + Instore retail operations
• The heart and soul of instore operations is based on store planning + planogram + store layout + real-time alerts + cycle counting + a lot of real-time opportunities with Vision
• Now if I look back a lot of ML is applicable and I will rework If I have to redo those problems yet again :), Classifying the type of customer issues in XBOX, using NLP to address customer issues with GenAI, Past had a lot of data
• Now all data can generate signals - Video, Audio, Text on top of RDBMS

Learned a few things (New start)

• Optimization opportunities
• Exploring pyomo / other relevant connected opportunities
• Azure data curate features/masking/removing/compliance
• A ton of training / long term - only to learn more 'persistence'
• A ton of mentoring internship projects 
• Reviewing/panelist in several events
• Tyco was a bit relaxed with work, This is a marathon

Wishlist

• Need a break!!!
• Write a book
• Take a break
• Try building some ideas/products
• Business + Tech is always essential to see the big picture / Explore options / Freelance

My perspective

Ref - Link

Why AI / ML ideas Fail in Large Enterprises?

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Consulting - Pros / Cons - Interesting Read

• High-IQ, high-octane outsiders with specialist skills and new ideas - Sometimes you may have all pieces to solve the problem but could not be solved due to internal disputes
• Know less than they claim, cost more than they seem to - Perspective vs Competency vs Capability
• Consultants’ work is often opaque and feeds into broader processes - Working in long term to ensure it is aligned vs Consulting for short term

Mariana Mazzucato: ‘The McKinseys and the Deloittes have no expertise in the areas that they’re advising in’

My Experience :)

• Consulting - Good to learn, hard to get domain expertise
• Product - Good at domain expertise may end up on forecast/recommendation/vision / nlp but not all

Mixing career products and consulting will make you crazy. Learn and can't apply. Know and can't deep dive. Sometimes it's a tradeoff to focus on where we move deep vs shallow.

• Problems in production vs POV are different
• Perspective from customer vs Perspective from Sales 
• Tech vs Customer views differ. Short term consulting projects will not align in terms of strategy / challenges / incremental evolution opportunities

My Experiences

#consulting is #tech / #demo drive, #product is #domain driven. When you switch sides you learn the other side of domain/tech. 

• For #Vision work my #vision learnings helped
• For #Forecasting my #domain experience helped me
• For #Recommendations work my #SQL experience helped me

Both consulting + domain gives you a unique advantage. In some ways, past 3 years I am becoming more SaaS Dev + Product Manager :)

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DBT Readings

Digital business transformation if done right will transform a caterpillar into a butterfly. If not done right it will be a faster caterpillar.

• D - Defend your market share
• D - Defeat your competition
• D - Disrupt the future

Nokia was great in its own market but didn't prepare for the future. Consulting aims at embracing and preparing for tomorrow. 

• S - Strategy, What is your definition of success
• P - Product, How do you build competency to match your vision
• E - Engineering it to scale
• E - Experience to get domain perspective and bring it to value creation
• D - Data to improve / meet goals / improve on operational efficiency / add value to customers / add value to business

Being a consultant - Able to see the future, spot gaps in the current system, identify the incremental changes to reach the destination

Technology is a enabler of value creation for customers, business. Being able to see 10K view, 3K view and drill down to aspects of incremental vision defines a consultant mindset :)

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Learning / Selling / Coding

Consulting has multiple hats. Some of the experiences summarizing below :)

• You are with ok with hearing a 'No'
• Source ideas/inspirations from domain/products / what really happing in this space/domain
• Read papers to find connecting dots of learning :)
• Start selling those ideas, Building MVP / Production grade implementation

Getting all three is a challenge - Productive, Gratified, and Satisfied :)

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