AI Skills <> AI Experience

• How to build it right = Skill
• What it takes to build it right in the first iteration = Experience

Keep Exploring!!!

Keep Saas Domain MVP Skills as Expertise

Less ambitious about titles, curious about technology, its age of knowledge, and saas beyond titles and roles - Future Careers

• Little expertise - Knowing how to use, Demos
• Deep expertise - Where to use, How it works, Where not to use
• Deep expertise is - Is this hype or relevant

 

• Getting better sometimes takes time, iterations, and cycles.
• Knowing a domain/idea, being able to code up, speak is all the skill we need :)

The core of Learning is

• Daily Habit formation
• Compare your growth over time
• Similar situations / Never materialized
• Building intuitions is the outcome of 10K hours
• Teaching helps ok at something to getting a better
• Iteration and thoughts formulation
• 10 hours to create 1-hour content
• Teaching strengthens understanding, spots gaps in knowledge

What are you working on to become a better programmer?

• The domain learning's, customer challenges will push our interests to continue to practice solving real-world problems 
• Breaking the problems down into discrete chunks that can be independently implemented
• You waste more time jumping into a program without direction by solving only interview questions
• Most people make the mistake of jumping straight to code without having the full picture in their heads
• "getting stuff done" at work and "perfecting the craft" in my spare time is an attempt to strike a balance: Think about the design, leave things nicer, but don't spend forever.
• Perfecting the craft leaves you to read up, teach, and code on weekends :)
• A balance between perfect vs good enough vs working solution

Keep Exploring!!!

Full Stack vs Deep Stack

I always feel myself an aspirational Deep Stack guy, Getting better in one focused area and expanding on related areas. I don't think it would be right to stay expert in all vs exposure in all.

• Expertise vs Exposure
• Communication vs Capability
• Consistency vs Competency

Everything will be reflected in our plans, actions, and thinking. 

Ref - Link

In my career, I prefer to be a good T in some areas and Try to be a V where to build solutions I need to learn. 70% T and 30% V, You only have limited time to keep sharpening skills vs catching up on related skills. 

As a Team, You need a mix of all ingredients

Ref - Link

Go where you Grow, Grow where you Go. 

Titles <> Knowledge

Keep Learning

Keep Thinking!!!

Experience - Exposure - Domain - Data - Diversity Thinking

• My First year - Windows98 Testing
• Second year - C/C++ Mq Adapter Design
• Third year - Application support / migration / ITSM / Deployment / Production Support
• Fourth Year - SQL Migration / Performance Testing
• Fifth Year - Biztalk / SQL Migration / Replication / Developer
• Sixth Year - OLTP SQL Developer 
• Seventh Year - BI + OLTP Developer / Warranty migration 220 million records (Supply chain 4th year :))
• Eighth Year - QA Manager / Prod support / On-call / People Management
• 9th Year - DB / QA Developer / Product Transition
• 10th Year - Forecasting Feature in Product / DB / QA / Automation
• 11th Year - Hardware Integration / Professional Services / SQL Developer
• 12th Year - AWS Scaling / Perf testing on horizontal scaling for Tag counting / Function + Perf / DB
• 13th Year - SQL 2016 Migration Analysis / Big Data Architecture Analysis
• 14th Year - AI / ML - PG / DB Dev / BI
• 15th Year - AI / ML - PG second year / DB Dev / BI / Freelancing
• 16th - AI Freelancing / Training / DB / BI Dev / Patents
• 17th  -  AI Freelancing / Training / DB / BI Dev / Patents
• 18th - Vision / Forecasting / Recommendations  / People Management / Training
• 19th - Vision / Forecasting / Recommendations  / People Management / Training

All the different parts of experiences sum up and help me in knowing the Data journey / AI journey/business challenges 

It was varied roles / multiple domains and products but everything was worth it :)

Thanks to all my previous companies/training providers / past employers/freelance offered by startups...

Multiple lenses - Development - QA - Automation - Performance - Support - BI - Database performance tuning - Computer vision - Forecasting - Recommendations - Many times I have influenced / implemented product key features based on domain expertise.

• Warranty Migration of 220 million SKUs
• 3PL touch point integration
• Spot capabilities in Product with Data & AI (What can we do with what we have :) 

Keep Exploring!!!!

Experience vs Algorithms vs Design vs Polyglot expertise

1. If I solve all algos and core data structures - Does it make it a good programmer - Yea Possibly he can solve build solutions
2. What do I do in my work ? - Understanding data, domain, customer problems, applying the lens of data + ML + BI finding potential solutions 
3. Where does this experience come from? - Similar domains, problems, building products 
4. What does experience mean ? - Collection of different roles / functions / projects / products 
5. Did I do only development or support or testing or performance? - When you build solutions you have to wear multiple hats to build them. There is no hard boundary for each role. To think from a customer perspective and building solutions is different from building solutions and how customers will use it
6. Do I remember all algos, code now ? - Now, Some I remember, Some I learn as I apply
7. Do I need to learn to practice every day? - There is no boolean way of answering for knowledge to say you know or don't know. As long as you can build solutions and code up you are good enough to solve customer solutions.

There is no one definition of skills. Do not go by what is being dictated. Building solutions takes as much time as you learn core cs basics. Expertise comes with time and experiments, not just coding standard problems.

Keep Thinking!!!

Can I master all Kubernetes, Computer Vision, Data Algos, NLP ?

A very good read - link

Copying a few lines/summary from it from the perspective that echo's my views

Perception -  I believed that Kubernetes was essential to the DS/ML workflow.

Experience - However, as I learned more about low-level infrastructure, I realized how unreasonable it is to expect data scientists to know about it

Fact / Reality - In theory, you can learn both sets of skills. In practice, the more time you spend on one means the less time you spend on another.

My perspective - We can know few things in-depth and need to master them with multiple experiments. You can master few areas and have a broad understanding of the rest of them. Compile knowledge vs Customize knowledge vs Solve in your own way is different.

Interesting Analogy -  I became a data scientist because I wanted to spend more time with data, not with spinning up AWS instances, writing Dockerfiles, scheduling/scaling clusters, or debugging YAML configuration files.

Recommendations

• Have a separate team to manage production
• Infrastructure abstraction kubeflow, metaflow, google vertex is useful for non-trivial workflows, and multiple models in production.

It's a good thread. 

Keep Going!!!

Learning vs Knowing vs Experimenting Vs Measure of Skills

A project work X needs 10 different things

• 4 Things you worked in multiple projects, You know how it works
• 3 things you did a hello world and you know basics
• 3 things you read up stack overflow and fill the gaps

The goal is to get a working implementation of the idea. You know few things but didn't deep dive. You implemented few things and did a deep dive as you worked on it in multiple projects. 

We may not master all 10 or remember all 10, We cannot wait to master all 10 to build our idea. The measure of knowledge is the ability to experiment, build, it's not just familiarity with all 10 tools or technology. Time to change the perspective we look at skills.

Keep Thinking!!!

What is productivity ? Am I Good Developer ?

The measure we share with Employers / Coworkers 

• 40 hours attendance 
• Responding to emails
• Accounting of tasks
• Attending meetings
• Quickly assemble something working with experiments/assumptions/learning's 

What is productivity at Individual-level?

• Accountability
• Satisfaction
• Preparedness 
• Connecting problems and previous experiences
• Solving newer problems with comfort
• Sharing learnings
• Feeding the learning bus

Ideas take time to implement with the domain, technology, and implementation learnings. Tools are ways to measure/track productivity. In the end, it boils down to an individual level to understand if we really productive?

Learning Cycle needs a lot of perspectives, focus, and consistent efforts.

• Some things I know 'How it works' because I have read about it, observed the working pattern
• Some things I know because 'I have tried and it worked'
• Some things I assume 'It works this way'
• Some things I learn the fundamentals and build my working knowledge on top of it
• The more you learn, the more it pushes you to learn and connect the dots

Am I a Good developer?

Whenever I hear about skill in JD, 

Example Keras - I correlate it to, I have used Deep Learning in this project. Did I master everything in Keras? No. 

How do I know I have learned everything in Keras?

I use Keras to achieve my Deep Learning model, finetune it. The perspective of learning is more towards a good quality MVP, Production code. Getting good quality in terms of performance and scalability needs learning. The focus is not on mastering technology but learning to solve the required Pieces.

What I do about Architecture evaluation?

Way back in 2016 / 2008, Evaluating cloud stack for Retail Analytics, Warranty Redesign, Salesforce Email Integration for XBOX. I was able to pull out a complete end to end architectural components on how the big picture will look like. Did I do hands-on implementation? No, but I was fairly convinced with patterns and use cases and learning How it works or why it works and successful in this context

I am a Prototype developer?

Yes, certain use cases to demonstrate working implementation had to build a working end to end prototype, ML pieces were working pieces, the mobile, web would be mockups built to show end to end flow.

Prototypes to production architecture refinement?

Taking prototypes to production, the Warranty approach to production is the best use case. Able to sell, migrate, implement the new approaches. Initially, Microsoft gave a 1 billion free warranty. We managed to save this hidden cost by ensuring warranty data is recalculated and it's more customer-driven.

Migration analysis

Migrating from old to a new system. Ensuring data is cleaned up. 

Development in all these perspectives is my experience. Somehow my perspective of technology starts from business to technology, I do like data structures, spending time, and learning different implementations. My success or satisfaction is from solving business problems, not from technology learning. Build your vision, future. Keep Going!!!

Learning is Summary of

• Applied Knowledge
• Learned Knowledge
• Experimented Knowledge
• Assumption based Knowledge

I find that number of meetings is actually a good measure of productivity, it's just inversely proportional 😉

— Clare Liguori (@clare_liguori) May 28, 2020

I believe some form of futuristic vision + design thinking is my approach 

What are mathematical thinking and computational thinking and what is the relationship between them? #mathschat #MTBoShttps://t.co/fSWBlH5qAN pic.twitter.com/c3PncZmoBJ

— CambridgeMathematics (@CambridgeMaths) July 13, 2020

Design Thinking : quand coeur, tête et main s'entrecroisent. Un aperçu rapide et quelques principes #DesignThinking #creation #creativité https://t.co/rqPDD7oyWo pic.twitter.com/iiOWKdqro0

— Michèle Drechsler (@mdrechsler) August 1, 2019

Our leading consultant is sharing his experience in Data Science. He suggests applying Design Thinking into Data Science applications. #datascience #designthinking https://t.co/BTHJBVYgGu Y

— Smart Data Institute Limited (@SmartDataInsti1) July 14, 2020

More Read - Link

Keep thinking!!!