Product Data Scientist vs AI Consultant

 

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AI in Vision Marketing

My post last year

AI generated Ad

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Optimization solver / Anamoly Detection

Hexaly is the world’s fastest optimization solver for Routing, Scheduling, Packing, and more.

Use Cases

• Modeling and Solving the Aircraft Landing Problem
• How Hexaly solves Renault Group’s workspace allocation problem in seconds
• Hexaly establishes new records for the Inventory Routing Problem (IRP)

Anamoly 

• Anomaly Detection
• Anomalies in Log Patterns

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Weekly News for Learning - LLM,GenAI

Weekly News for Learning - LLM,GenAI

Sequoia Capital AI Ascent Summary

• Idea #1: LLMs as Agents - LLMs have the potential to be powerful agents, defined as (1) choosing a sequence of actions to take - through reasoning/planning or hard-coded chains – and (2) executing that sequence of actions
• Idea #2: Planning & Reasoning - Planning & reasoning was a major emphasis at our event and a close cousin to the “agents” topic
• Idea #3: Practical AI Use in Production - Smaller/cheaper/but still “pretty smart” models were a consistent theme in our event
• In addition, we discussed speed/latency, expanding context windows/RAG, AI safety, interpretability, and the CIO as “on the rise” as the key buyer for AI that makes enterprises more efficient internally.
• Idea #4: What to Expect from the Foundation Model Companies - Bigger smarter models, More developer platform capabilities

Apollo's AI email-writing assistant (Example of Idea #1)

• Automatic email opener-personalization
• One-click sequence generation
• One-click sales playbook generation
• Email response assistance

The Gong team has been quietly working on LLMs and Generative AI for over a year now. I have started to use the new Call Highlights internally and it's a huge time saver: no need to listen to calls anymore!

Agents on the Brain

To reach their full potential, the next generation will need to be:

• Compute aware: minimizing resource usage as an objective function
• Data awareness: finding and connecting to the right model or data source for the task
• Agent aware: finding, reusing and communicating with ecosystems of agents
• Safety aware: checking outputs and sandboxing code is the first step, plus more serious controls will be needed to prevent abuse
• User aware: learning from user behavior and preferences to optimize performance

Ref - Link1, Link2, Link3, Link4, Link5

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AI Skills

 Keep Learning!!!

Ten reasons why you don't need AI / ML Platform

• Data Disparity: Your datasets are dispersed across multiple silos without a unified view, hindering effective data analysis for AI/ML.
• Unclear Business Objectives: Without well-defined business problems and corresponding data mapping, your organization cannot identify valuable AI/ML use cases.
• Cross-Functional Misalignment: Lacking a collaborative ecosystem among product management, domain experts, and AI/ML specialists can prevent meaningful integration of AI/ML into business processes.
• Limited Data Operations: Your data volume is insufficient for significant AI/ML insights, and without preliminary model testing, the utility of AI/ML is questionable.
• Technology Stack Assessment Gap: Your data science team has not yet evaluated major cloud AI/ML and MLOps offerings, which is essential before committing to an AI/ML platform.
• Model Deployment Inexperience: The absence of experience with deploying machine learning models at scale on cloud platforms indicates that your organization might not yet be ready for an AI/ML platform.
• Cloud Integration Deficiency: Running on a major cloud provider without having experience deploying models integrated with cloud-based databases or CDPs suggests a lack of technical preparedness.
• Business-Tech Disconnect: Missing alignment and understanding between your business goals and technology capabilities, coupled with uncertainty about data privacy and compliance, poses significant risks.
• Strategic Incongruence: If AI/ML initiatives do not align with your company's product roadmap, then investing in an AI/ML platform may not support your business strategy.
• Adoption Ambiguity: Not having a defined path for how AI/ML will be leveraged for text, video, recommendations, forecasting, etc., leads to uncertainty in the adoption of an AI/ML platform.

In many companies, I observed these challenges. 

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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AI skills at work

• Selling AI is a skill
• Building (Billing) with AI is a skill
• Keeping the end goal a moving target is a skill
• Build vs Buy vs Manage cost is a skill
• Hiring someone who can Build (Bill) effectively is a skill
• Differentiating AI demos vs AI reality is a skill

Choose wisely!!!!

AI Skills <> AI Experience

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

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How to get correct in the First Attempt with AI

Experience in AI = Ability to ask the right questions even if you don't have answers and provide AI awareness, complexity, ROI, and helping them manage costs vs Selling vision + charging $$$$ hefty for all types of costs build/buy/explore/expand. 

Build targeted products :)

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Failures in AI/ML/GenAI Adoption

Success in #AI/ML/GenAI projects has a lot of challenges. Some projects' data availability / some projects handling bias / Some projects balance features vs bugs / Knowing 80% features vs 20% future releases. This needs a lot of iteration and team mix to make it work. Success goes in LinkedIn posts. Failures end up haunting us searching for the next success.

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GenAI + Vision

Some moments to cherish :) 

Ref - Link

Hellmann’s collaborates with Google on AI tool that tackles food waste

Hellmann’s Launches Innovative Campaign to Clear the Galaxy of Food Waste

SANDWICH HELLMANNS 

Happy Learning!!!

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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AI - Applied use case - Vision in Action

 

Spot the right use case, solve with the balance of data / strategy to meet the market on time

More read - Link

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Klara Chatbot - Devil in Details

• It recites exact docs and passes me on to human support fast.
• Good job on the team for making hallucination not possible - because it seems to spit out the same responses however I ask it, and refuses to go “out of bounds.”
• As soon as I ask or instruct anything that is not a doc, I’m *boom* talking with a human agent.
• Also, almost all questions I ask about payment terms or problems the chatbot tells me - in various ways - to talk to the merchant if I have a problem, not to Klarna
• These assistants turn docs into chat text, that people read!
• Klarna is a middleman. The customer buys from the merchant and Klarna sells defaulted payments to collections agencies!!
• Klarna wants potential investors to believe they are buying into an “AI edge” company

Ref - Link

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Dense to Sparse - AI World

• What we do in CNN - Convert Dense to Sparse with convolution and activations 
• What we do in NLP - Text Preprocessing: Stemming / Lemmatization / Stop-word removal - Vectorization 
• Topic Modelling - Words - Documents - Non-Negative Matrix Factorization 
• ML Feature Engineering / Recommendations - PCA / SVD 

Everywhere we attempt to retain key features/vectors aligned to vision/text/features/topic modeling tasks. Converting Dense to Sparse is the way to get the signal from the noise :)

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Custom Chatbot vs OpenAI Chatbot

Build vs Develop on LLM

Custom Chatbot

Time- Data collection, labeling, classification, NER models

Build

• Preprocessing
• Lower case
• Stemming, Lemmatization with POS Tags
• Entities, NER

Inference

• Intent
• Topic Classification
• Frame a response

Context - Limited to corpus

LLM Chatbot

Time - Prompts / Responses / Store / Retrieve

OpenAI

• Prompt
• Inference answers

Challenge

• Air Canada hallucinations use cases
• Context vs Hallucination

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