AI Agents

 Agentic World

• Customer support executive = Customer support agent
• Database administrator = Database Administration agent
• Software developer to Coding Agents

Two New Job Directions

• Agent creator jobs
• Agent Supervisors

Happy Agentic Solutions.

Course Launch: Generative AI and Cybersecurity – Frameworks and Best Practices 2024

• This blog is close to 15 years old. Every small learning/perspectives build on top of it.
• Consulting / Product Development / Teaching provides our own perspectives.
• Summing up AI Adventures + GenAI Lessons we have our course

Keep Learning!!!

Knowledge vs Perspective vs Learning

• Focus on understanding over copy-pasting
• Code can be replicated, but understanding cannot
• Embrace experimentation and learning from failures
• Build deep knowledge of system behavior:
• Identify potential failure points
• Understand edge cases
• Document limitations
• Quality is everyone's responsibility

Keep Experimenting!!!

The Human Animal is just a Machine - Warren McCulloch

 

Keep Exploring!!

Estimates vs Projects vs Big picture

Sometimes I also have a mixed bag of tasks, Estimates depends on many factors, Predictable estimate vs just estimates 

• Vision papers / Experiment colab codes
• API creation
• Dockerize
• AWS / Azure learn sassify
• DB Tasks
• Catching up with GenAI 
• Mapping products vs models vs MVP
• Sell what you already have built
• POV / Proposals / PPT / Architecture

If I were a freelancer I would focus on building on some products and be a seller / reseller. Being an Expert of all takes time. Coding vs Full Stack vs ML vs DL vs Ideas .. Learning whatever is possible but need to pick and choose

Keep Exploring!!!

 

Algos vs Features

You will see a lot of #posts on #Datascience #alogrithms but very few posts on #features #data. 80% of core learning is #features and #data. Hope #features are also #shared more openly for benefit of the community and #learning from #experiements. 

Pay deep attention to features. For example, For an average shopper what all I can track about his purchase?

• How many average views to buy a product
• How many competitive products comparison
• How many average price comparisons
• How many discount comparisons
• Time of purchase (Weekday / Weekend)
• Festive Shopper
• Bought together supplementary items
• Weekend shopper
• Late night shopper
• Salary Day Shopper
• Discount Shopper

Many times it is a tradeoff between 10000 feet view vs 1000 feet ground view. Balance both #perspective vs #bigpicture. Nth level personalization / real-time personalization across browsers/devices like we see how google recommends in the next browser session. #perspectives #datascience #persppectives #learningmoments 

Keep Exploring!!!

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!!!!

You are your 'brand'

• You have to train your mind like a mental athlete
• Cold-blooded killer of ideas / solutions / practice / learn
• Be clear about skills to learn / code/skills to be aware of
• Work ethic is more important, Genuine to support. You are your 'brand'
• Domain / Ideas / Constant learning is the key. 'No' is a part of the journey with wins/failures
• I don't connect for need / Get work done, When we work for a while it becomes natural to connect but connecting to get work done is not my way of work :)

Pause / Break / Relook / Unlearn / Relearn 

Keep Going!!!