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.

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Beginners guide on CI/CD Pipeline with Python LangGraph AI Agent and Docker!

📋 What's covered:
• Introduction to CI/CD concepts and GitLab
• Building Python LangGraph AI agents with state management
• Docker containerization best practices
• Complete GitLab CI/CD pipeline configuration
• Testing, deployment, and troubleshooting tips

🔧 Key technologies featured:
• Python with LangGraph for stateful AI agents
• Docker for containerization
• GitLab for CI/CD automation
• VS Code style code examples throughout

Which CI/CD pipeline are you using for your AI Agent projects?

CI CD Pipeline for AI Agent

🚀 Generative AI For AI Agent Interview Prep: Key Questions and Insights for 2025 🤖

Generative AI is shaping the future—and it’s time to get ahead of the game. Whether you're prepping for interviews, upskilling, or diving deep into AI, this FREE guide has everything you need!

🔥 𝗜𝗻𝘀𝗶𝗱𝗲:
🧠 Generative vs. Discriminative Models: Understand the core differences.
⚡ Why Transformers Rule: The tech behind groundbreaking generative models.
💻 Scaling Challenges: Learn how top experts tackle them.
🔒 Ethics & Safety: How to responsibly use AI and prevent biases.

I’ve attached a PDF with 25 expert-level interview questions and answers to help you crush your next AI interview.

👇 Grab the full guide NOW from the attachment and stay ahead of the curve!

🔥 What excites you most about the future of Generative AI? Drop your thoughts in the comments!

AI Agent GEN AI Interview Guide

📈 300% Boost in AI Agent Scalability with Ease! 🚀

AI agents are revolutionizing business operations, but scaling and deploying them effectively is a challenge for many companies. 

This AI Agent in Action Second Edition by @guide reveals how to design and deploy AI agents, delivering personalized customer experiences 3x faster!

📚 What's Inside:
✅ The Rise of Agents – Autonomous Decision-Making, Perception-Decision-Action Framework, Proactive Personalization
✅ Mastering Large Language Models – OpenAI API Integration, Prompt Engineering, Choosing the Right LLM
✅ Engaging GPT Assistants – Customizing GPTs, Adding Custom Actions, Extending Knowledge with File Uploads
✅ Building Multi-Agent Systems – Multi-Agent Orchestration, Agent Critiques, Collaborative Agents
✅ Empowering Agents with Actions – Function Calling, Semantic Kernel, Building Interactive Agents
✅ Building Autonomous Assistants – Behavior Trees, Autonomous Execution, Managing Behavior Trees
✅ Using an Agent Platform – Nexus Platform, Streamlit for Chat, Profiles & Personas
✅ Understanding Agent Memory – RAG, Vector Databases, LangChain Integration
✅ Mastering Agent Prompts – Prompt Flow Design, Evaluating Responses, Personalizing with Profiles
✅ Agent Reasoning and Evaluation – Chain-of-Thought Prompting, Zero-Shot Reasoning, Self-Consistency Evaluation
✅ Agent Planning and Feedback – Sequential Planning, Feedback Loops, Performance Tracking

This book is your ultimate guide to building scalable, efficient AI systems that can automate processes and create personalized experiences.

🔗 Ready to dive deeper?

My journey with AI began in 2011, diving into CVPR conference papers and exploring the cutting-edge research in computer vision. By 2012, I was deep i

Open article: 3x Boost in Customer Personalization with AI Agent on Kubernetes — The Key to Scaling AI Agent Systems by Ashish Patel 🇮🇳 • 9 min read

3x Boost in Customer Personalization with AI Agent on Kubernetes — The Key to Scaling AI Agent Systems

🚀 Speculative Decoding: Small Models Guess, Big Models 3× Faster — Without Losing Accuracy

LLMs generate tokens one by one — great quality, but slow and expensive.
𝗦𝗽𝗲𝗰𝘂𝗹𝗮𝘁𝗶𝘃𝗲 𝗗𝗲𝗰𝗼𝗱𝗶𝗻𝗴 speeds this up using a small draft model + a large target model.

➜ Draft model quickly proposes a sequence of tokens
➜ Target model checks them 𝘪𝘯 𝘱𝘢𝘳𝘢𝘭𝘭𝘦𝘭 for validity
➜ If they match its predictions → ✅ accept
➜ If not → 🔁 regenerate from the mismatch point (aka 𝘥𝘪𝘷𝘦𝘳𝘨𝘦𝘯𝘤𝘦)

🔍 Why it works:
➜ Draft = fast + cheap proposals
➜ Target = accurate validation
➜ Most proposals align → less recompute needed

⚡ 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀:
✅ 2–3× faster inference
✅ Lower compute cost
✅ Enables real-time use in copilots, chatbots, AI search, etc.

⚠️ 𝗟𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀:
➜ Weak draft = high rejection rate
➜ Strong draft = less speed gain
➜ Batching strategy impacts efficiency
➜ Limited gains for long contexts or low-quality drafts

📌 TL;DR: Propose with a small model, verify with a big one — get speed 𝘢𝘯𝘥 accuracy.

✨ Are you passionate about 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆?

Get ready for Gujarat’s 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗳𝗲𝘀𝘁𝗶𝘃𝗮𝗹 – 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗗𝗮𝘆 𝟮𝟬𝟮𝟱 – a celebration that promises to be 𝗯𝗶𝗴𝗴𝗲𝗿, 𝗯𝗼𝗹𝗱𝗲𝗿, 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹 than ever! 🌍✨

🎟️ 𝗠𝗶𝘀𝘀𝗲𝗱 𝘁𝗵𝗲 𝗘𝗮𝗿𝗹𝘆 𝗕𝗶𝗿𝗱 𝗧𝗶𝗰𝗸𝗲𝘁?

We’ve got you covered! As a token of gratitude to our incredible community, here’s another chance to secure your spot.

👉 Use my exclusive code “𝗢𝗦𝗗𝗔𝗦𝗛𝗜𝗦𝗛𝗚𝗨𝗘𝗦𝗧𝟮𝟱” to get a 𝗳𝗹𝗮𝘁 𝟲𝟬% OFF on ticket prices!
 Don’t wait—secure your pass before it’s gone.

📅 𝗗𝗮𝘁𝗲: 5th October 2025 | ⏰ 09:00 AM Onwards
 📍 𝗩𝗲𝗻𝘂𝗲: Silver Oak University, Ahmedabad, Gujarat
 🔗 𝗚𝗿𝗮𝗯 𝗬𝗼𝘂𝗿 𝗧𝗶𝗰𝗸𝗲𝘁 𝗡𝗼𝘄: https://lnkd.in/ddbHDjjf

🎤 𝗦𝗽𝗲𝗮𝗸𝗲𝗿 𝗟𝗶𝗻𝗲-𝘂𝗽 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀
 🔹 Parth Pandya: The Future of Open Source: Trends, Opportunities & Emerging Technologies
 🔹 Kiran Trivedi, PhD: Open Source Hardware for Tiny ML
 🔹 Varun Pandya: Open Source & AI
 🔹 Chetan Soni: Building Secure Foundations: An Open-Source Approach to Kubernetes Secrets
 🔹 Someshwaran Mohan Kumar: From Guesswork to Flamegraphs: eBPF Profiling with DevFiler
 🔹 Ansh Arora: Significance of Open Source Projects & Sustainability
 🔹 Jay Thakkar: Gemini CLI: The All-Round Developer Friend in the Terminal

⚡ 𝗖𝗫𝗢 𝗣𝗮𝗻𝗲𝗹 – 𝗧𝗵𝗲 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗶𝗻 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 & 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻
 ✔️ Nirav Gohil (CEO, Tecmantras Solution)
 ✔️ Chetan Sheladiya (Founder & CEO, Metizsoft Solutions Pvt Ltd & Destinova AI Labs Pvt Ltd)
 ✔️ Bhavika Patel (Co-Founder, HK Infosoft)
 ✔️ Chinmay Shah(Director, Crescent Electronics Pvt. Ltd.)

🌐 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗣𝗮𝗻𝗲𝗹 – 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗦𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆: 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 & 𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗟𝗲𝗮𝗿𝗻𝗲𝗱
 ✔️ Pratik Bhatt CSM CSM (WordPress Ahmedabad)
 ✔️ Vijay Golani (Magento Ahmedabad)
 ✔️ Dhaval Gajjar (Java User Group Gujarat)
 ✔️ Ruchit Patel (Laravel Ahmedabad)
 … and many more inspiring speakers!

🎯 By securing your Ticket, you gain access to:
 🔹 500+ brilliant minds from diverse domains
 🔹 50+ visionary partners leading open-source innovation
 🔹 Knowledge-packed sessions across AI, Web3, Data, Design, and more

✨ Why join as an attendee?
 ✅ Be recognized as part of the OSD 2025 journey
 ✅ Network with industry leaders & changemakers
 ✅ Gain exposure to cutting-edge technologies
 ✅ Contribute to shaping the future of open-source innovation

Rinkesh Bansal, PhD Nirav S. Santosh Yadav Mehul Patel Ashish Tiwari Prathamesh Chavan Harsh Manvar Neel Shah Bhargav Pithva, PMP® SAFe Agilist® CSM® Falgun Bhalsod Bharat Makwana Prashant Bhavsar Drijesh Ppatel Ram Iyengar  Dr. Satvik Khara Vishal Patel Shreyan Mehta Kartik Dhokaai

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