Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

Building Agentic AI systems beyond connecting APIs or LLMs is complicated, but not impossible.

This architecture lays the foundation for how AI agents think, communicate, and improve, covering everything from testing and observability to deployment and memory management.

Here’s a breakdown of the key layers and components that make up a scalable Agentic AI Architecture :

1.🔸Decomposition
Break down complex systems by domain (e.g., Coding Agent, Data Agent), by cognitive capability (Reasoning, Planning, Execution), or by agent role (Planner, Executor, Memory Manager, Communicator).

2.🔸Communication
Enable message passing between agents using inter-agent protocols or A2A (Agent-to-Agent) orchestration.
Support both single-agent and multi-agent setups for small or distributed workflows.

3.🔸Deployment
Deploy agents in containerized or serverless environments using Docker or Modal.
Support orchestrators like CrewAI or AutoGen for collective intelligence in multi-agent workflows.

4.🔸Data & Discovery
Integrate knowledge bases (like vector databases for RAG), memory stores (FAISS, Redis, Pinecone), and APIs for dynamic data access.
Context is passed using Model Context Protocol (MCP) for structured and real-time reasoning.

5.🔸Testing & Observability
Validate workflows end-to-end, test reasoning logic, and evaluate performance under real conditions.
Monitor using Weights & Biases, LangFuse, and track metrics like latency and task success rate.

6.🔸UI & Style
Provide intuitive feedback loops through visualization layers, dashboards, and self-reflective modes.
Enable collaborative, proactive, and goal-driven reasoning among multiple agents.

7.🔸Security
Protect access with token-based authorization and data encryption.
Include Trust Layers for human-in-the-loop validation and Policy Enforcement for safe execution.

8.🔸Cross-Cutting Concerns
Handle configuration, secrets, and environment management.
Support flexible frameworks like LangChain, AutoGen, or CrewAI for runtime execution and modular design.

Agentic AI is the future of automation - where AI doesn’t just assist but collaborates and learns.

Save this post to understand the architecture that powers the next generation of AI systems

#AgenticAI

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AI agents are great at working alone. However, they can also be effective when operating within networks of collaboration.

The real power of AI lies in how these agents communicate, coordinate, and complement each other’s abilities.

Orchestration patterns define this collaboration which turns isolated agents into intelligent, goal-driven systems capable of solving complex tasks together.

Here are 6 orchestration patterns that power the next generation of AI systems :

1.🔸Sequential Orchestration
Agents work one after another - each passing its result to the next.
Perfect for structured, step-by-step tasks like data pipelines or analysis chains.

2.🔸Concurrent Orchestration
Multiple agents work in parallel, handling different parts of a problem simultaneously.
This boosts speed, scalability, and overall task efficiency.

3.🔸Group Chat Orchestration
Agents communicate in a shared conversation, refining ideas together.
Useful for brainstorming, collaborative decision-making, or generating consensus.

4.🔸Maker–Checker Loops
One agent creates or proposes; another verifies, corrects, or approves.
Ideal for maintaining accuracy, compliance, and quality assurance in workflows.

5.🔸Handoff Orchestration
Agents transfer control dynamically based on context or expertise.
Best suited for adaptive workflows like support systems or multi-step automation.

6.🔸Magnet Orchestration
A central “manager” agent dynamically assigns tasks to the right specialists.
Enables fluid, context-aware collaboration across multiple intelligent agents.

AI Agents are evolving fast and orchestration is what makes them powerful.

Learning how these patterns can help you build smarter, more collaborative AI systems can be a huge advantage.

#AIAgent

AI Agent Orchestration Patterns

📚LLMs in the Enterprise are finally getting the playbook they deserve… LLMs in Enterprise by Ahmed Menshawy and Mahmoud Fahmy provides practical guides for building real AI systems that operate at scale.

Most teams talk about LLMs in theory. This book focuses on execution. It bridges foundational concepts with the hands-on design patterns that matter when you are integrating models into production environments.

Here are the insights that stood out👇

1.🔸Enterprise LLM integration is a data architecture problem
The book breaks down how to design pipelines, tune retrieval, and structure data so models operate with consistency and low latency in real workloads.

2.🔸Scaling LLMs requires pattern-level thinking
They go deep on architectural patterns that reduce complexity, improve efficiency, and streamline deployment. This includes RAG frameworks, fine-tuning strategies, segmentation techniques, and evaluation patterns that teams often overlook.

3.🔸Performance is not just about bigger models
The authors show how to optimize model behavior with advanced inferencing engines, contextual model customization, and monitoring systems that keep applications predictable.

4.🔸Real enterprise value comes from operational rigor
Security, fairness, transparency, and accountability are not afterthoughts. They are part of the design process, especially when LLMs touch business workflows and customer data.

5.🔸AI teams win by mastering both concepts and impact
The flow of the book reflects the real enterprise lifecycle: Concept → Customization → Impact
A clear, structured way to think about turning LLM capabilities into business outcomes.

If you are building production AI systems, leading an LLM program, or preparing for the next wave of enterprise adoption, you should definitely get a copy of this book.

Enterprise AI is evolving fast. Understanding these design patterns early puts at a career advantage which allows you to shape the next generation of intelligent applications.

#LLM

Agentic AI vs Traditional AI: The Shift from Supervision to Autonomy

For years, Traditional AI has been like a skilled worker. It has been efficient, but dependent on human supervision.

Agentic AI, however, takes a different approach. It is a decision-maker that thinks, plans, and adapts without constant direction.

Let’s dive into what truly separates them 👇

Agentic AI: Smarter. Adaptive. Independent.

- Dynamic Learning: Perceives its environment, plans actions, and evaluates outcomes continuously.

🔸Goal-Oriented Thinking: Sets goals, creates sub-goals, and adjusts strategies like a human problem solver.

🔸Reasoning & Reflection: Learns from mistakes, refines decisions, and improves performance over time.

🔸Collaborative Agents: Multiple agents share context, delegate tasks, and synchronize for collective intelligence.

Traditional AI: Powerful but Limited.

🔹Static Learning Models: Train once, deploy, and manually retrain, with no real-time adaptability.

🔹Human-Dependent Execution: Waits for prompts or tasks, performs inference, and stops.

🔹Narrow Focus: Optimized for specific use cases without flexibility.

🔹No Self-Improvement: Requires human supervision for updates and tuning.

In Short:

- Traditional AI executes.

- Agentic AI thinks, plans, and evolves.

It’s the difference between automation and autonomy, a shift that’s redefining how AI systems operate, learn, and collaborate in the real world.

What other diffenreces have you noticed?

#AI

Ashish Patel 🇮🇳

Sr.Data Scientist & AI Consultant

Oracle

▪️ Led the AI strategy, architecture, and design for Artificial Intelligence, Machine Learning, and Data Science products, services, and solutions, aligning them with Oracle Cloud Applications, OCI, and Oracle Enterprise.
▪️ Provided thought leadership and technology oversight for AIML Development teams, directly influencing business and technology decisions.
▪️ Spearheaded the development and deployment of AI Agents for scalable, resilient enterprise applications, driving AI-powered automation across business functions.
▪️ Architected and implemented MCP server enterprise architecture, ensuring seamless integration with cloud-native environments and maximizing the scalability and performance of enterprise applications.
▪️ Collaborated with senior executives across multiple business lines to translate business requirements into technology solutions, aligning them with the overall AI strategy and next-gen tech stack.
▪️ Directed the development of machine learning models, integrating them with full-stack software ecosystems, and contributed to the strategic design of data engineering.
▪️ Worked with product managers and development teams to define project scopes and software requirements, ensuring the timely delivery of projects meeting quality standards.
▪️ Authored and maintained technical documentation, including architecture diagrams, design specifications, and system diagrams.
▪️ Led the adoption of cutting-edge technologies and frameworks (e.g., PyTorch, TensorFlow, Scikit-learn), driving innovation and best practices within the team.
▪️ Mentored junior developers, fostering skill development and encouraging a culture of creativity and ownership within the AIML domain.
▪️ Stayed at the forefront of industry trends, ensuring the team remained competitive by recommending new technologies and platforms.

Senior Principal Architect - AIML & Data Science

▪️Design and/or delivery of solutions, assets, and content at sustainable AI.
▪️Apply methodology and governance around project development.
▪️Document and articulate clear understanding of customer/partner success criteria.
▪️Apply critical thinking, technical knowledge, customer/partner insights to solve problems with industry methodologies, and frameworks.
▪️Collaborate with IBM and AWS specialists to design and build complex enterprise solutions with foundation of Red Hat Open Shift, AWS Infrastructure, and IBM Software technology.
▪️Standardize solutions in accordance with the AWS Well-Architected Framework across all pillars, including operational excellence, security, performance efficiency, cost optimization, and reliability.
▪️Define, lead, and execute architecture design workshops and white-boarding sessions to build implementation plans.
▪️Develop deep relationships with key customers through solution integration to drive long-term IBM and AWS adoption.
▪️Team well with our key IBM & Red Hat business units, and strategic partners to support our partnership.
▪️Consult as a subject matter expert on our IBM/AWS technology and services portfolio.
▪️Author use cases, reference architectures, blog posts, and other field and external content.
▪️Work in sandbox environments to demonstrate IBM Hybrid Cloud Capabilities with AWS.
▪️Led the design and automation of tasks in Oracle HCM using Generative AI and LLMs, streamlining HR processes and enhancing efficiency.
▪️Mentored junior and senior scientists, guiding their development and ensuring successful project execution.
▪️Developed production code and championed best coding and engineering practices in AI and machine learning projects.
▪️Identified and mitigated risks in AI project execution, ensuring successful business and technical outcomes.
▪️Collaborated with product managers and technical leaders to ensure timely and successful delivery of AI models and services.

Sr. AWS AI/ML Solution Architect

Cygnet Infotech

• Interact with customer and come up with use-case’s on ML/AI and use the same in
solution integration on existing business. 
• Selecting features, building, and optimizing classifiers using machine learning techniques.
• Develop and test Computer vision algorithms to edge devices and advanced hardware systems created.
• Develop and evaluate state-of-the-art computer vision algorithms for real-time edge devices
and other hardware devices.
• Perform technical performance benchmarking and analyses to support engineering decisions
• Data mining using state-of-the-art methods 
• Extending company’s data with third party sources of information when needed 
• Enhancing data collection procedures to include information that is relevant for building
analytic systems 
• Processing, cleansing, and verifying the integrity of data used for analysis.
• Participate in design reviews. 
• Doing ad-hoc analysis and presenting results in a clear manner 
• Creating automated anomaly detection systems and constant tracking of its performance 
• Research and devise innovative statistical models for data analysis 
• Work as the lead data strategist, identifying and integrating new datasets that can be 
leveraged through our product capabilities and work closely with the engineering team to
strategize and execute the development of data products. 
• Collaborate with product design and engineering to develop an understanding of needs
and Quality. 
• Analyse data for trends and patterns and Interpret data with a clear objective in mind. 
• Provide deep learning and machine learning base solutions to Solve varies Real world
problem.

Projects : Semantic Segmentation, Image classification,  3d Sementic Segmentation, Few-shot Image Classification, Object detection, Image Quality Assessment, Pose Estimation, Key-point Detection,  Image Augmentation, Super Resolution

Senior Data Scientist

SYMBIOSIS INTERNATIONAL UNIVERSITY

- Teaching Computer vision to M.tech Students.

Topics I teach: 

𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗜𝗺𝗮𝗴𝗶𝗻𝗴
- Image Acquisition, Sampling, Quantization, Difference in Monochrome and Multichrome imaging, the concept of color spaces, point processing techniques, mask processing methods, image filtering, shape in images, edge detection, gradient operators- Roberts, Sobel, Prewitt, Canny, Slope magnitude method, morphological image processing, erosion, dilation, opening, closing, hit-n-miss transform, thinning, Top Hat transformation, Bottom hat transformation

𝗜𝗺𝗮𝗴𝗲 𝗥𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗥𝗲𝗴𝗶𝗼𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
 - Shape Descriptors-contour based, region-based, Boundary based; Thresholding based segmentation, Watershed based Segmentation, Gray level Co-occurrence Matrix-energy, entropy, maximum probability, contrast, correlation; wavelets, wavelet Pyramids, Image matching, 
- Hough Transform, RANSAC, Large-scale retrieval: Spatial Verification, TF-IDF, Query Expansion, feature encoding, Crowdsourcing, and Human Computation, Corners - Harris and Hessian Affine, Orientation Histogram, SIFT, SURF, HOG, GLOH, Scale-Space Analysis- Image Pyramids and Gaussian derivative filters, Gabor Filters and DWT.

Industry Associate Professor

Packt

TensorFlow Developer Certificate Guide : 

https://www.amazon.in/TensorFlow-Developer-Certificate-Comprehensive-Guide-ebook/dp/B0BVMQ1TZS

Technical Book Reviewer

Apress

Book Author

Manning Publications Co.

Prompt Softech

•	Collaborated with business teams to gather and analyze requirements, ensuring 100% alignment with organizational objectives and delivering solutions that met strategic goals.
•	Enhanced training datasets using advanced image augmentation techniques, improving model robustness and achieving a 15% increase in classification accuracy.
•	Leveraged Transfer Learning methodologies, repurposing pre-trained models to deliver 25% faster development cycles while achieving notable accuracy improvements across multiple tasks.
•	Implemented state-of-the-art deep learning models for image classification, extracting meaningful insights from complex datasets and enabling 98% accuracy in production systems.
•	Validated and fine-tuned models on extensive datasets containing thousands of images, ensuring 99% reliability and seamless deployment in real-world applications.

Softweb Solutions Inc.

•	Data mining using state-of-the-art methods. 
•	Hands-on experience in selecting features, building, and optimizing classifiers using machine-learning techniques. 
•	Enhancing data collection procedures to include information that is relevant for building analytic systems 
•	Processing, cleansing, and verifying the integrity of data used for analysis.
•	Doing ad-hoc analysis and clearly presenting results.
•	Creating anomaly detection systems and constant tracking of its performance.
•	Having strong knowledge in Statistics, Feature Engineering, Model Selection, Model Evaluation, and Feature Scaling to build accurate machine learning.
•	Experience in tuning and optimize ML/DL model for performance improvement.
•	Hands-on experience working with live projects on different machine learning techniques.
•	Having strong problem-solving capabilities.
•	Working with most demand industrial technology Framework like pandas, numpy, matplotlib, nltk(NLP), Keras(Deep Learning, tensor flow scikit learn(Machine learning), open cv

Projects: Image Classifications, Facial Expression Recognition, Recommendation system, Predictive Modeling, Regression Analysis

Solaris Publication

●	Research raw data and disseminate easily understood insights to provide organizational direction.
●	Produced a company guidelines and best practices document based on consumer insight analysis.
●	Performed regular web scraping to synthesize data.

Project: Image Classification, Seasonality Trends Analysis, Recommendation System

Jr. Data Scientist

🔥 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |

I have over 12+ years, Author, Data Scientist and Researcher with 8+ Years of Experience of Data Science technology and Research experience in wide functions including predictive modelling, data preprocessing, feature engineering, machine learning and deep learning. Currently, I work as Sr.Aws AI ML Solution Architect(Chief Data Scientist) at IBM India Pvt Ltd. Helps to Collaborate with IBM and AWS specialists to design and build complex enterprise solutions with the foundation of Red Hat Open Shift, AWS Infrastructure, and IBM Software technology. Standardize solutions in accordance with the AWS Well-Architected Framework across all pillars, including operational excellence, security, performance efficiency, cost optimisation, and reliability.

If you catch me outside of work, I'm an adventurous traveller and avid cricket lover!

𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝗶𝗲𝘀: Data science, Machine learning, Deep learning, Audio Processing, Python, Computer Vision, SQL, Keras, TensorFlow, Librosa, Pyaudio, Mlflow, tube flow, mlops, DVC, MLOps on AWS, Kubeflow on AWS, 

Enterprise LLMs Services: Amazon Bedrock, Amazon Titan, IBM WatsonX.ai, Azure OpenAI, NvidiaNemo, Google PaLM

LLMs Knowledge: GPT-4, Claude, ChatGPT, OpenChat, UltraLM, OpenCoderPlus, WizardLM, Nous Hermes, Vicuna, Baize-v2, Guanaco, Minotaur, Davinci003, Falcon, Alpaca Farm PPO Sim (GPT-4), Pythia, Cohere, Alpaca Farm PPO Human, Cohere Chat, Alpaca, Falcon 7B Instruct, Davinci001.

MLOps on AWS Services: Amazon SageMaker, AWS CodePipeline, AWS CodeBuild, AWS CodeCommit, AWS CodeDeploy, AWS Batch, AWS Glue, AWS Data Pipeline, Amazon Elastic Container Service, AWS Farget, AWS Lambda, AWS EKS(Elastic Kubernetes Service), AWS ECS(Elastic Container Service), AWS APP Runner, AWS Cloud Formation, AWS ECR(Elastic Container Registry), AWS CloudWatch, AWS Auto Scaling, AWS CloudTrail, AWS EC2, AWS EBS(Elastic Block Store), AWS S3, AWS Route53

I also like meeting new people over a cup of coffee. Feel free to reach out at ashishpatel.ce.2011@gmail.com 

I am a passionate blogger, Researcher like to write about Machine learning and deep learning related Articles. you can visit my blog 

𝗟𝗶𝗻𝗸: https://medium.com/ml-research-lab.
book: https://www.amazon.in/B-V-Vishwas-ebook/dp/B08GLG46PQ/
Orcid Profile: https://orcid.org/0000-0002-5385-3897
Google Scholar : https://scholar.google.com/citations?hl=en&authuser=1&user=MfHiCbkAAAAJ

P.S. If you're passionate about creating tech-focused products to help people reach their potential, I am happy to connect at https://www.linkedin.com/in/ashishpatel2604

Sr. AWS AI/ML Solution Architect(Chief Data Scientist)

▪️Design and/or delivery of solutions, assets, and content at sustainable AI
▪️Apply methodology and governance around project development.
▪️Document and articulate clear understanding of customer/partner success criteria.
▪️Apply critical thinking, technical knowledge, customer/partner insights to solve problems with industry methodologies, and frameworks.
▪️Collaborate with IBM and AWS specialists to design and build complex enterprise solutions with foundation of Red Hat Open Shift, AWS Infrastructure, and IBM Software technology.
▪️Standardize solutions in accordance with the AWS Well-Architected Framework across all pillars, including operational excellence, security, performance efficiency, cost optimization, and reliability.
▪️Define, lead, and execute architecture design workshops and white-boarding sessions to build implementation plans.
▪️Develop deep relationships with key customers through solution integration to drive long-term IBM and AWS adoption.
▪️Team well with our key IBM & Red Hat business units, and strategic partners to support our partnership.
▪️Consult as a subject matter expert on our IBM/AWS technology and services portfolio.
▪️Author use cases, reference architectures, blog posts, and other field and external content.
▪️Work in sandbox environments to demonstrate IBM Hybrid Cloud Capabilities with AWS.

If you want to truly understand LLMs…
try calculating them by hand first.

That’s the magic of My friend Prof. Tom Yeh  Deep Learning Math Workbook.

It forces you to slow down.
Do the math.
See the mechanics.
Understand what’s happening behind every token prediction.

Here’s what each chapter teaches you — and why it matters for LLMs:

1. Dot Product

When you calculate it manually, you see how “similarity” is measured.
In LLMs → this becomes attention scores.

2. Matrix Multiplication

Doing it by hand shows how information combines and transforms.
In LLMs → this powers every forward pass through every layer.

3. Linear Layer

Manually computing W·X + b shows how inputs turn into features.
In LLMs → this is how Q/K/V projections and MLPs work.

4. Activation

Calculating ReLU, Sigmoid, Tanh makes you feel the effect of non-linearity.
In LLMs → this gives the model its reasoning ability (GELU, gates, etc.).

5. Artificial Neuron

Working through a single neuron reveals the logic inside every unit.
In LLMs → millions of these form the model’s internal understanding.

6. Batch

Manually applying operations across multiple examples shows training efficiency.
In LLMs → batching is the secret to stable, scalable learning.

7. Connection

Seeing residuals and wiring by hand makes network flow click.
In LLMs → this prevents memory loss and exploding gradients.

8. Hidden Layer

When you compute hidden transformations, you see abstraction forming.
In LLMs → this is where meaning and representation live.

9. Deep

Stacking operations manually shows how depth compounds understanding.
In LLMs → deeper stacks = richer reasoning and context retention.

10. Wide

Calculating wide layers reveals how models store more information.
In LLMs → width boosts knowledge capacity and expressiveness.

11. Softmax

Manually normalizing logits shows how models “choose” the next token.
In LLMs → this becomes the probability distribution we decode from.

12. Gradient

Computing gradients by hand shows how learning actually happens.
In LLMs → gradients tune billions of weights during training.

When you calculate these steps manually, you stop memorizing formulas…
and start understanding the machine.

That’s the real gift of Prof. Tom Yeh’s workbook — it makes deep learning visible, tangible, and uncomplicated.

♻️ Repost this if you want more people to build real intuition — not just run code.

P.S. Which chapter should I break down next with a simple example?

Everyone is yelling about “AGI” right now, but the people closest to the frontier are still giving wide, uncertain timelines.

Ilya just said 5–10 years on the Dwarkesh Patel podcast.
Demis Hassabis has said similar numbers.
Meanwhile, expert surveys still place the median expectation closer to the 2040s–2050s, and a good chunk of researchers think it could be much later — or never.

there is no consensus, just speculation.

That’s why this meme fits the current moment: companies barking at each other about AGI while no one has crossed the threshold.

The real progress isn’t in “AGI claims.”
It’s in the unglamorous fundamentals:

• reliability under real workloads
• reasoning that actually generalizes
• efficient multimodal integration
• scalable data + compute strategies
• products that deliver value today, not in hypothetical AGI futures

The noise is about AGI timelines.
The work is about making current AI systems genuinely useful.

Last week didn’t just feel big.I had the honour of inaugurating the upGrad Ahmedabad Learning Centre…

…and as an Amdavadi, this moment hit differently.
(It felt like the city is finally stepping into the future.)

But the real spark?

The conversations with the students.

The excitement.
The curiosity.
The obsession with AI.

It was absolutely electric.

And the questions they asked?

Not basic. Not surface-level.
But sharp, forward-thinking, “this generation is built different” kind of questions:

• “How do we build a career in AI if we’re starting from scratch?”
• “What skills in AI will matter the most in the next 3–5 years?”
• “Will AI replace jobs — or create even bigger opportunities?”
• “How do we stay relevant when AI tools evolve so fast?”
• “What does the industry actually expect from an AI fresher?”

These weren’t just questions.
They were signals.

Signals that Gujarat isn’t just catching up —
Gujarat is ready to lead.

And that’s exactly why this centre matters so much.

Here’s what made the day unforgettable:

1️⃣ Meeting Shubham Singh  & Ravneet Arora  
Two leaders who are fearlessly pushing to bring future-ready learning to Gujarat.

2️⃣ Feeling the energy in the room with 
Students. Working professionals. 
Tech enthusiasts.
Everyone driven by one thing — growth.

3️⃣ What this centre unlocks
• Data Science & AI/ML
• Full Stack Development
• Data Analytics
• Digital Marketing

These aren’t just courses.
They’re career accelerators.
Gateways to opportunities that didn’t exist a decade ago.

#Gujarat’s learners are ambitious.
They’re curious.
They’re hungry.

All they needed was a launchpad.

And now — they have one.

If you’re in #Ahmedabad and want to build a future-proof tech or AI career, this is your moment to step in.

♻️ Reshare this to inspire someone who needs to see it.

I learned something wild listening to an O’Reilly podcast: we’re only 5% of the way to real LLMOps maturity.

The guest was Abi Aryan ☯︎  of LLMOps.
Here are the biggest lessons I walked away with👇

⸻

1️⃣ LLMOps isn’t MLOps 2.0

It’s a different world.

• From deterministic → nondeterministic
• From “deploy the model” → manage distributed inference
• From accuracy → latency + throughput + memory + cost

And honestly?
Software engineers seem to transition faster than MLOps engineers —
because LLMOps feels more like distributed systems than classic ML.

⸻

2️⃣ Agent systems = distributed systems (not magic)

Modern setups usually have:

• A planner
• Specialized workers
• A reassembly layer

But this creates real problems:

• GPU load balancing
• Consensus when an agent fails
• Errors multiplying across the chain

Also… most “agents” today are just fancy if-this-then-that workflows.

⸻

3️⃣ Observability needs a new mindset

It’s not about watching dashboards.
It’s about anomaly detection.

• What changed across 50+ signals?
• What’s unusual before an error appears?
• What’s drifting?

It’s more like monitoring fraud detection than monitoring ML.

⸻

4️⃣ FinOps is the silent pain point

We’re maybe 5% of the way to good tooling.

Real cost buckets:

1️⃣ Data engineering
2️⃣ Compute-bound work
3️⃣ Communication-bound bottlenecks (GPU↔GPU)

Most teams overspend because they:

• Use reasoning models unnecessarily
• Overfill KV cache
• Optimize compute when the real issue is memory

⸻

5️⃣ RL has a fatal flaw

LLM RL environments are static…
but agents need dynamic learning.

Once a model “finishes” the environment — it plateaus.
It’s the same freshness problem we see in RAG.

Curriculum learning exists in research,
but no practical implementation yet.

Huge gap.

⸻

6️⃣ The future (and how I’m thinking about my own skills)

Areas that feel massively underserved:

• System-level profiling
• Dynamic RL environments
• Data engineering (big practical wins)

And the skill order that makes sense:

• Learn distributed systems
• Learn profiling (memory, compute, comms)
• Then go deeper into model internals

LLMs aren’t “another ML model.”
They’re Software 3.0 — end-to-end systems where the model is the product.

If this breakdown helped, consider resharing ♻️

P.S. What part of LLMOps do you think is most underserved right now?

Catastrophic Forgetting’s 𝗦𝗰𝗮𝗹𝗲 𝗟𝗮𝘄: Why Bigger LLMs Forget Faster 🚨

Ever fine-tuned a model and watched it forget everything? 🤯 The bigger the model, the faster it forgets. 🧠💨
A few months ago, we fine-tuned a 70B-parameter LLaMA model. It didn’t just forget the new task—it lost the skills and safety mechanisms we worked so hard to build. 😱

Why? That’s when I discovered 𝗖𝗮𝘁𝗮𝘀𝘁𝗿𝗼𝗽𝗵𝗶𝗰 𝗙𝗼𝗿𝗴𝗲𝘁𝘁𝗶𝗻𝗴 and 𝘁𝗵𝗲 𝗦𝗰𝗮𝗹𝗲 𝗟𝗮𝘄.

𝗪𝗵𝗮𝘁’𝘀 𝗛𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴? 🤔
Fine-tuning large models often causes catastrophic forgetting. Larger models (70B+) forget faster than smaller ones (7B).

Why?
 • High-Dimensional Loss Landscape 🌄: Bigger models have more catastrophic basins. Even small shifts can push them into these basins, causing knowledge loss.
 • Ripple Effects of Fine-Tuning 🌊: Tuning one layer can trigger a chain reaction, destroying unrelated skills.

𝗧𝗵𝗲 𝗦𝗰𝗮𝗹𝗲 𝗟𝗮𝘄 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 📊
A 13B model lost double the capabilities of a 7B on unrelated tasks after fine-tuning.
Bigger models = Bigger forgetfulness risks ⚠️

𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲 (𝗪𝗵𝗲𝗻 𝗶𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱)

 • 1989: "Catastrophic Forgetting" coined by McCloskey & Cohen.
 • 2016: French et al. confirm neural networks suffer from catastrophic forgetting.
 • 2017: Kirkpatrick et al. propose Elastic Weight Consolidation (EWC) to mitigate forgetting.
 • 2023: Luo et al. find larger LLMs (1B-7B) forget more severely.
 • 2023: Lermen et al. show fine-tuning impacts LLM adaptability.
 • 2024: Kalajdzievski et al. identify an inverse relationship between fine-tuning and forgetting with LoRA.
 • 2025: Google Research introduces Nested Learning to address catastrophic forgetting in AI.

𝗧𝗵𝗲 𝗟𝗼𝘀𝘀 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲: 𝗔 𝗛𝗶𝗱𝗱𝗲𝗻 𝗧𝗿𝗮𝗽 🕳️
Imagine a model’s knowledge as a wide canyon. Fine-tune in the wrong direction, and it’s like walking it off a cliff. Even a small update can push it into a "catastrophic basin"—performance collapses. 💣

𝗗𝗼𝗲𝘀 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿-𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗧𝘂𝗻𝗶𝗻𝗴 𝗛𝗲𝗹𝗽? 🚫
LoRA freezes most parameters, but even minimal updates can lead to massive skill loss. Fine-tuning GPT-3.5 on just 10 adversarial examples caused an 88.8% increase in harmful responses. 💀

𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻? 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝘆 🔄
Forget keeping weights close—preserve the geometry of the loss landscape.
 Methods like Sharpness-Aware Minimization (SAM) and Functionally Invariant Paths (FIP) reshape the landscape for stability. 🧭

𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 🔑

 • Bigger models = Bigger forgetfulness risks ⚠️
 • Small updates can lead to catastrophic forgetting 💥
 • Flat, wide loss landscapes are your best defense 🌍

The real challenge is optimizing the loss landscape geometry to prevent disastrous forgetting. 🌟

♻ Repost if you’re building or fine-tuning LLMs—your team needs to know! 🚀
P.S. Ever destroyed a model with just one fine-tune? Share your story in the comments! 🗣️👇

The Turing Test isn’t broken—it’s just irrelevant now.

AI can sound human without understanding a thing.

Mimicry is easy; reasoning isn’t.

Old benchmarks can’t measure modern capabilities.

We need tests that track reliability and real problem-solving.

Impressing humans for a few minutes isn’t intelligence.

This meme captures the gap perfectly.

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