Decoding Data Science

I am passionate about helping businesses and professionals upskill in Artificial intelligence and advance their careers in the field of data science. 

▶️ With a 17+ years background in Data Science, I have extensive experience in applicable skills and technologies and have a track record of success in 100+ job placements of my mentees.

▶️ At Decoding Data Science, we offer a range of courses and programs designed to provide learners with the knowledge and skills they need to succeed in the competitive field of data science. Our team of experienced instructors and mentors is dedicated to helping our students achieve their goals and reach their full potential.

▶️ I am constantly seeking new opportunities to expand and improve our offerings and am always looking for talented individuals to join our team. If you are interested in learning more about the Data Science Academy, or if you have any questions about our programs, please don't hesitate to reach out
at connect@decodingdatascience.com

Founder

Harvard Business Review

I actively contribute to the research community, offering insights that shape content development and deepen our understanding of contemporary perspectives in management and leadership. My involvement in this space allows me to stay at the forefront of emerging trends and to influence the dialogue around key topics from Harvard Business Review (HBR) and beyond.

Advisory Council

Majid Al Futtaim

Heading Advanced Analytics in figuring out the right customer profile, and different selling touchpoints to grow an omnichannel retail business. 

▶️ During my tenure as a Data Science Manager at Majid Al Futtaim (Carrefour), I achieved a remarkable uplift in sales of 15 million AED in the last quarter across the UAE. 

▶️ My key accomplishments included developing a powerful dashboard enabling stores to optimize prices and product assortment based on store attributes and customer profiles. I also conducted customer segmentation to drive personalized and targeted sales strategies.

Data Science Manager

Nakheel

Heading Data Science to deliver Advanced Analytics use cases to help retail business increase revenue.

During my time at Nakheel, Dubai, I developed accurate forecasting models for sales and footfall prediction across various properties. I integrated these models with interactive Tableau dashboards and automated C-level reporting to provide valuable business insights. 

Additionally, I utilized National Language Processing (NLP) techniques to categorize maintenance tickets and created a comprehensive dashboard for streamlined ticket management.

Lead Data Scientist

Accenture

Talent Supply Chain

Business Impact: Help our business leaders make insight-driven decision about our people to achieve our talent and business priorities. Drive key analytics driven initiatives aligned with the global HR priority as we shape the HR of the Future.

👉 Responsible for defining and implementing new innovations and systems to enable Accenture's Talent Supply Chain (TSC) HR and business processes. 

👉 Designing systems for accommodating our forecast for demand & supply planning, chargeability and Roll offs, enabling business use the dashboard for making decisions for using Talent optimally.


Attrition Dashboard

Business Impact: Was able to save million dollars for Global Accenture in acquisition costs.

👉 Attrition Dashboard gives you details of employees who are going to attrite with 6 months using demographics, training, compensation, awards and leaves data.

👉 Product uses various machine learning algorithms to learn from historical data and gives consistent and accurate output.

Tools Used: SAP HCM, Workday, Tableau , Qliksense, Rstudio

Major Highlights

• Assisting Data Engineering and Innovation(DEI) Leadership team in setting up a new vertical for Talent and Human Resource (THR) Analytics 
• Leading presales and POC initiatives for setting up THR analytics with existing client base 
• Developed solution and operationalizing data analytics projects and setting up teams to support delivery fulfillment activities 

Major Projects

Microsoft Analytics
Objective:  Go to Person for Analytics & Reporting - Microsoft Sourcing Team
• Developed a solution using machine learning algorithm for Talent Success Profile and scored resume which are probable to be hired.
• Managed the Account for all Analytics needs – Designing, planning and implementation for high end analytics to Microsoft Client
• Developed Tableau and PowerBI dashboard to showcase sourcing story to showcase Leadership

Accenture- Diversity Analytics

Objective:  Accenture wanted to understand the employee diversity characteristics and how to improve it.
• Developed a logistic regression model to Identify driver for Hiring female employees.
• Designed recruitment funnel to show top of the funnel view in Tableau

Best Buy- Repeat Caller

Objective: Retail Client wanted to understand reasons for Repeat calls at Helpdesk and how to reduce the same. 
• Used text mapping to get themes for reason for repeat call , analysis and actionable insights and recommendation 
Tools used: SAS Enterprise Guide 5.1, R Studio, Advanced Excel, QDA Miner

Senior Data Scientist

Dell Technologies

Social Lead Targeting Project

• Built several Propensity Models using logistic regression for  DELL
• Combining online and offline information and generating Social Lead targeting list across regions and segments Digital Analytics. 
• Quarterly scoring for different segments and regions. Measurement of ROI numbers of these reports.
• Market basket and sequential analysis for Client’s product using Enterprise miner
• Lead a team of 5 resources including project and capacity planning, Also involved in hiring.

Tools Used: SAS, Teradata, SFDC, Omniture Sitecatalyst, Google Analytics, Advanced Excel, Lattice SalesPrism, eMiner

Data Scientist

Absolutdata Analytics-an Infogain company

Digital Analytics

• Developed attribution model to understand exact ROI spends of different digital media channels
• Created Omniture dashboard and custom reports with meaningful web metrics for a leading advertising agency
• Involved in proposals/business development for prospective clients (DA capabilities)
• Involved in Facebook Branding for Absolutdata and Optimizing the website using Google Analytics • Created and analyzed Social Listening and Online Reputation Management reports using Radian 

Tools Used: Omniture Sitecatalyst, Google Analytics, Radian6, Fusion Charts, Qlikview, Microsoft Office, Advanced Excel, VBA, Tableau, Omniture Discover

Marketing Mix Modelling

• Helped with business pain points like: Key drivers of sales, Volume Due to different drivers, Effectiveness and ROI of different media vehicles, Media planning, Effectiveness of Trade Promotions and Price Elasticity
 • Used techniques like linear regression, pathway analysis, multiple regression Hierarchical Bayesian regression etc. to do market mix modelling. 
• Worked with data at different granularities and from multiple data sources to develop models.
• Developed what if simulators to see impact of independent variables on Sales. 

Tools used: SAS, SPSS, Teradata, eMiner, R, Microsoft Office, AMOSS, Advanced Excel, VBA

YALE Innovations

• Analyzed Expedia Solution Specialists Ltd financial statements, product portfolio & marketing plan and provided recommendations to improve the cash flow and the firm profitability. 

• Defined Roles and provided roadmap for growth strategy.

• Led instructive training workshop on Marketing for 26 SME clients. Achieved 95% satisfaction rate.

• Contributed to the paper on ‘Management Control Process'

Strategy Consultant

• Drafted and implemented Digital Marketing ( adsense, social media and partners) Strategy

• Analyzed Customer funnel, worked on Google Analytics and managed Google Adwords.

• Email Marketing using Mail Chimp- End to end delivery.

• Market research for new products and features. MVT Testing. Business Development.

Business Analyst

Hewlett-Packard

• Built Propensity Models for products using logistic regression with online data
• Web Analytics, A/B testing, Dashboarding and Reporting using Omniture Site Catalyst and Brio
• Integrated reseller (amazon) partners on hp.com 
• Measure success of website and online marketing initiatives using data and key metrics

Tools Used: Omniture SiteCatalyst, Omniture Discover, Omniture Test & Target, SAS

* Developing and implementing sharepoint site (MOSS) according to client requirements .
* Creating Custom workflows excel services and automated reporting. End to End solutions for SharePoint site

Team Lead

Ezbuy and Banner Ops: Internet Marketing

Technological Competencies: Filezila , FTP client, Real media, Photoshop, Image builder , XML editor, Omniture, Brio, MS Acess, SQL

• Worked on implementing a partner on hp.com website Data Analytic of hp.com

• Web Analytics and Reporting using  Omniture Site Catalyst  ( Adobe Digital Marketing Suite) and Brio ( Hyperion - Oracle Enterprise Performance Management System )

• Integrated reseller partners on hp.com and designed tools for updating of product prices.

• Configuration of new ad campaigns cross regional/ across segments

• Dealing with day to day requests to ensure proper Pricing on HP.com as well as to share expertise on EZbuy

• Configuration changes of countries and Deploying new country configuration.

• e-commerece & data classification activities.

Senior Software Engineer

Customer Relationship Management (CRM) -Clarify, EWM and Amdocs

Client: KPMG, Syngenta, Hutchison ,Vodafone, etc

Technological Competencies:SQL, Clarify, Data Dictionary, User interface Editor, Data Dictionary Editor, Debugger, Query Analyze , Webmethods, IIS , enterprise Manager , Proxycap, putty, Data Exchange, dd comp and CB exchange.

My responsibility and work included

• Change request at form level and database level as per client request.

• Analyzing, Designing and Implementing clarify scripts

Software Engineer

CEO DecodingDataScience.com | 🌎 AI Community Builder | Data Scientist | Strategy & Solutions | Generative AI | 20 Years+ Exp | Ex- MAF, Accenture, HP, Dell | LEAP & GITEX Keynote Speaker & Mentor | LLM, AWS, Azure & GCP

I am a seasoned business problem solver with over 20 years of experience in diverse domains such as retail, digital, e-commerce, Talent & HR, marketing, real estate, and technology. I specialize in applied analytics, artificial intelligence, Generative AI, and deep learning, and have a proven track record of using these skills to solve complex business problems.

My experience includes using LLM, AI Product Development, predictive modeling, random forest, xgboost, SVM, marketing mix modeling, logistic regression, linear regression, segmentation, cluster analysis, digital analytics, blockchain, cryptocurrency, web3, and NLP to analyze data from various sources and provide strategic insights to clients. I am proficient in tools and software such as R, Python, SAS, VBA, Tableau, PowerBI, Qliksense, Snowflake, Pyspark, Collab, Azure Synapse Analytics, AWS, and big data.

As an effective manager, mentor, trainer, and problem solver, I have successfully led teams to deliver high-quality projects on time and within budget. My excellent communication and interpersonal skills have allowed me to build strong relationships with clients and colleagues, resulting in positive outcomes for all parties involved.

I am passionate about using data and analytics to drive business success and am always looking for new challenges and opportunities to apply my skills and experience

In a world where AI is moving faster than most people can keep up, clarity doesn’t come from more tools — it comes from the right conversations.

That’s exactly what our AI community meetups are built for.

Every session brings together professionals who are trying to understand where AI is heading, how to apply it in real work, and what truly matters amidst all the noise.

 When you sit in a room with people asking honest questions, sharing use cases, and challenging the hype, something important happens: confusion turns into direction.

These meetups create a learning environment that goes beyond presentations.

 You learn by discussing.
 You learn by listening.
 You learn by seeing how others are solving the same challenges you’re facing.

This is the future of AI education — community-driven, conversation-based, and rooted in real-world application.

If you're serious about staying relevant in AI, join spaces where clarity is created, not assumed — where people grow together through open dialogue and shared experience.

Looking forward to seeing many of you at the next meetup today

Be part of the fastest-growing ecosystem in the region

Mohammad Arshad

𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐢𝐬𝐧’𝐭 𝐜𝐨𝐦𝐢𝐧𝐠 — 𝐢𝐭’𝐬 𝐛𝐞𝐢𝐧𝐠 𝐛𝐮𝐢𝐥𝐭 𝐢𝐧 𝐫𝐨𝐨𝐦𝐬 𝐥𝐢𝐤𝐞 𝐭𝐡𝐢𝐬.

This Thursday, we’re bringing together the region’s 𝐦𝐨𝐬𝐭 𝐚𝐦𝐛𝐢𝐭𝐢𝐨𝐮𝐬 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬 𝐚𝐧𝐝 𝐛𝐮𝐢𝐥𝐝𝐞𝐫𝐬 for an evening dedicated to 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 and 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠. If you want to create systems that automate workflows, solve complex tasks, and deliver measurable impact, this is the room you want to be in.

Decoding Data Science (DDS) is proud to support this hackathon as the 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐏𝐚𝐫𝐭𝐧𝐞𝐫 — because events like these strengthen the UAE’s builder ecosystem and open doors for talent across the region.

𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬
 This isn’t a showcase. It’s 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 with Airia’s AI team, where 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧, 𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧, and 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 come together to produce working autonomous agents.

𝐈𝐧𝐬𝐢𝐝𝐞 𝐭𝐡𝐞 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞
 • Build 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 using Airia’s platform
 • Learn directly from 𝐞𝐱𝐩𝐞𝐫𝐭𝐬
 • Team up with 𝐭𝐨𝐩 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬 𝐚𝐧𝐝 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐨𝐫𝐬
 • Compete for 𝟕,𝟎𝟎𝟎 𝐀𝐄𝐃 in prizes
 • Fireside chat with 𝐉𝐚𝐦𝐞𝐬 𝐕𝐨𝐰𝐥𝐞𝐬, Team Principal at Atlassian Williams Racing
 • Special appearance by 𝐀𝐥𝐞𝐱 𝐀𝐥𝐛𝐨𝐧, Williams Racing Driver

If you’re 𝐬𝐞𝐫𝐢𝐨𝐮𝐬 𝐚𝐛𝐨𝐮𝐭 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐢𝐧 𝐀𝐈, this is the place to be.
(𝐄𝐯𝐞𝐧𝐭 𝐥𝐢𝐧𝐤 𝐢𝐬 𝐩𝐨𝐬𝐭𝐞𝐝 𝐢𝐧 𝐭𝐡𝐞 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐛𝐞𝐭𝐭𝐞𝐫 𝐫𝐞𝐚𝐜𝐡)

If this aligns with your interests, drop a comment — would love to hear what you’re building and what you’d like to see in future hackathons.

 And if you know developers or founders in the UAE who should be in this room, share this post so more people can take part.

The global AI landscape is shifting faster than ever — and this week made that clear.

From cloud giants racing to build cheaper, more powerful compute, to governments shaping national AI strategies, to media companies pushing back on data use, the industry is entering a new phase: the AI industrial revolution.

This week’s developments highlight exactly that:
• AWS unveiled next-gen Trainium3 servers and adopted Nvidia NVLink, delivering 4× more compute at lower energy cost.
 • Meta acquired the AI-wearables startup Limitless, signaling a push toward personal AI assistants built into everyday devices.
 • SoftBank moved to acquire DigitalBridge, deepening its stake in the global AI infrastructure boom.
 • The New York Times sued Perplexity over alleged content misuse, intensifying the debate around data licensing and AI training.
 • The UAE reaffirmed its national AI strategy, with a strong focus on healthcare, education and public-service innovation.
 • Guggenheim announced plans to expand into Saudi Arabia to invest in AI, infrastructure and technology.

Together, these stories show a world scaling AI at every layer — hardware, devices, capital, regulation and national strategy.

If you’d like to read the full breakdown, let me know.

Decoding Data Science

This week marked a turning point in the global AI race — major cloud players doubled down on infrastructure, consumer tech giants pushed into AI-enabl

Open article: The Week AI Got Bigger: Infrastructure, Wearables, and Regulation by Mohammad Arshad • 4 min read

The Week AI Got Bigger: Infrastructure, Wearables, and Regulation

AI is no longer a tool. It is becoming the architecture of the global economy.

This week’s headlines show how fast the shift is happening:

• Retailers are now optimizing content for AI chatbots, not search engines.
 • Music labels are licensing their catalogs to AI startups, defining a new model for generative training.
 • Amazon Web Services (AWS) and Google Cloud launched a joint multicloud service, prioritizing resilience over rivalry.
 • Saudi aramco Ventures is investing hundreds of millions into European AI startups.
 • Kazakhstan and the UAE announced new AI, HPC, and GovTech collaborations.
 • The UAE’s “AI in the Ring” benchmark ranked Gemini first for cultural intelligence.

These trends, taken together, highlight a clear pattern: AI is spreading across every layer of business, infrastructure, and global strategy. The winners will be those who understand how these signals connect.

If this roundup brings value, feel free to share so more people stay ahead of the curve. 

Follow Decoding Data Science not to miss any updates

#decodingdatascience #dds #ai

This week’s AI and tech developments show how rapidly the global landscape is shifting across industries. Retailers are redesigning their digital pres

Open article: From Generative Retail and Music Licensing to Multicloud Alliances and Gulf-Driven Investment by Mohammad Arshad • 7 min read

From Generative Retail and Music Licensing to Multicloud Alliances and Gulf-Driven Investment

In 2026, the biggest career risk isn’t “AI will replace you.”

It’s that you’ll sound confident in meetings… while secretly not understanding the terms driving the decisions.

That’s why I created an Essential GenAI + AI Glossary for Professionals (2026 edition) — a practical reference you can use while building, leading, buying, or implementing AI.

This isn’t definitions for the sake of definitions.
 It’s clarity you can apply immediately.
Inside the glossary you’ll find:

Core GenAI concepts: LLMs, embeddings, tokens, RAG, fine-tuning, agents, tool/function calling

Newer industry terms showing up everywhere: MCP, A2A, multimodal, evals, guardrails, synthetic data, alignment

Critical business + delivery language: data privacy, governance, model risk, latency, cost, reliability, compliance
What you get from it:
Faster understanding in discussions with engineers, vendors, and leadership
Better decisions because you know what’s hype vs what’s real
A shared language for teams building AI products end-to-end

I’m sharing the full glossary inside the Decoding Data Science community, and we’re adding many more terms and examples as the ecosystem evolves.

If you want it, join the community and grab it there.

Comment with one AI term you keep hearing but still find unclear — I’ll reply with a simple explanation (and a real-world example).

#GenerativeAI #AILeadership #AIResources #Innovation #Learning #decodingdatascience #dds

Gen AI Concepts 2025

𝐌𝐨𝐬𝐭 AI projects don’t fail because of the model—they fail from 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 and governance

Design your 𝐀𝐈 𝐬𝐭𝐚𝐜𝐤 with 𝐢𝐧𝐭𝐞𝐧𝐭, not guesses.

Here’s the 9-Layer AI Tools Matrix I use after shipping prototypes → MVPs → production:

𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐞 𝐟𝐚𝐬𝐭: start 𝐧𝐨-𝐜𝐨𝐝𝐞, then 𝐥𝐨𝐰-𝐜𝐨𝐝𝐞 for secure internal workflows.
 𝐁𝐮𝐢𝐥𝐝 𝐯𝐞𝐥𝐨𝐜𝐢𝐭𝐲: move to 𝐏𝐲𝐭𝐡𝐨𝐧 𝐔𝐈 (𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐭/𝐆𝐫𝐚𝐝𝐢𝐨).

 𝐀𝐧𝐜𝐡𝐨𝐫 𝐨𝐧 𝐀𝐏𝐈𝐬: go 𝐀𝐏𝐈-𝐟𝐢𝐫𝐬𝐭 (𝐎𝐩𝐞𝐧𝐀𝐈/𝐁𝐞𝐝𝐫𝐨𝐜𝐤), add 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 (𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧/𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡/𝐂𝐫𝐞𝐰𝐀𝐈).

 𝐒𝐭𝐚𝐲 𝐠𝐫𝐨𝐮𝐧𝐝𝐞𝐝: add 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 (𝐑𝐀𝐆) with 𝐏𝐢𝐧𝐞𝐜𝐨𝐧𝐞 / 𝐩𝐠𝐯𝐞𝐜𝐭𝐨𝐫 / 𝐂𝐡𝐫𝐨𝐦𝐚.
 𝐒𝐡𝐢𝐩 𝐟𝐨𝐫 𝐫𝐞𝐚𝐥: stand up a 𝐛𝐚𝐜𝐤𝐞𝐧𝐝 (𝐅𝐚𝐬𝐭𝐀𝐏𝐈/𝐄𝐱𝐩𝐫𝐞𝐬𝐬) with 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 & 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 (𝐋𝐚𝐧𝐠𝐒𝐦𝐢𝐭𝐡/𝐓𝐫𝐮𝐋𝐞𝐧𝐬).

 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞 𝐬𝐚𝐟𝐞𝐥𝐲: use 𝐀𝐈-𝐚𝐬𝐬𝐢𝐬𝐭𝐞𝐝 𝐝𝐞𝐯 (𝐑𝐞𝐩𝐥𝐢𝐭/𝐋𝐨𝐯𝐚𝐛𝐥𝐞/𝐂𝐮𝐫𝐬𝐨𝐫); never skip 𝐫𝐞𝐯𝐢𝐞𝐰𝐬, 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲, 𝐭𝐞𝐬𝐭𝐬.

𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞: 𝐬𝐭𝐚𝐫𝐭 𝐬𝐦𝐚𝐥𝐥, 𝐢𝐧𝐬𝐭𝐫𝐮𝐦𝐞𝐧𝐭 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 & 𝐥𝐚𝐭𝐞𝐧𝐜𝐲, keep components 𝐬𝐰𝐚𝐩𝐩𝐚𝐛𝐥𝐞 to avoid 𝐥𝐨𝐜𝐤-𝐢𝐧.

I’m sharing the 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤, 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐦𝐚𝐭𝐫𝐢𝐱, 𝐬𝐭𝐚𝐫𝐭𝐞𝐫 𝐬𝐭𝐚𝐜𝐤𝐬, 𝐑𝐀𝐆 𝐜𝐡𝐨𝐢𝐜𝐞𝐬, 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬, 𝐛𝐚𝐜𝐤𝐞𝐧𝐝 & 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐧𝐨𝐭𝐞𝐬, 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐜𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭𝐬.

Drop your 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞 + 𝐜𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭𝐬 in the comments—I’ll reply with a 𝐬𝐮𝐠𝐠𝐞𝐬𝐭𝐞𝐝 𝐬𝐭𝐚𝐜𝐤.

𝐃𝐞𝐜𝐤 + 𝐨𝐧𝐞-𝐩𝐚𝐠𝐞 𝐦𝐚𝐭𝐫𝐢𝐱: see first comment.

 𝐍𝐨𝐭 𝐰𝐫𝐢𝐭𝐭𝐞𝐧 𝐛𝐲 𝐀𝐈.



#decodingdatascience #dds #AI #LLM #RAG #MLOps

AI Tools Matrix

𝐒𝐭𝐨𝐩 chasing 𝐭𝐨𝐨𝐥𝐬. 𝐒𝐭𝐚𝐫𝐭 mastering the 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 that make them work.

Most AI resumes list 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬. Very few show understanding of how 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 behave in the wild. In our AI Residency, we teach the 𝐦𝐞𝐜𝐡𝐚𝐧𝐢𝐜𝐬 so tools become 𝐢𝐧𝐭𝐞𝐫𝐜𝐡𝐚𝐧𝐠𝐞𝐚𝐛𝐥𝐞 and results become 𝐫𝐞𝐩𝐞𝐚𝐭𝐚𝐛𝐥𝐞.

What you’ll master

Problem framing and 𝐝𝐚𝐭𝐚 𝐝𝐞𝐬𝐢𝐠𝐧: clear 𝐨𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬, 𝐜𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭𝐬, and 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 plans
𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 fundamentals: 𝐜𝐡𝐮𝐧𝐤𝐢𝐧𝐠, 𝐢𝐧𝐝𝐞𝐱𝐢𝐧𝐠, 𝐠𝐫𝐨𝐮𝐧𝐝𝐢𝐧𝐠, and 𝐟𝐚𝐢𝐥𝐮𝐫𝐞 analysis
𝐂𝐨𝐧𝐭𝐞𝐱𝐭 management: 𝐭𝐨𝐤𝐞𝐧𝐬, 𝐰𝐢𝐧𝐝𝐨𝐰𝐬, 𝐫𝐨𝐮𝐭𝐢𝐧𝐠, and 𝐜𝐨𝐬𝐭–𝐥𝐚𝐭𝐞𝐧𝐜𝐲–𝐪𝐮𝐚𝐥𝐢𝐭𝐲 trade-offs
𝐌𝐨𝐝𝐞𝐥 behavior: 𝐩𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 as interface design, 𝐠𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬, 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠, and 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 loops
𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 that matters: 𝐨𝐟𝐟𝐥𝐢𝐧𝐞 tests, 𝐑𝐀𝐆 𝐟𝐚𝐢𝐭𝐡𝐟𝐮𝐥𝐧𝐞𝐬𝐬, 𝐫𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 suites, and live 𝐀/𝐁𝐬
𝐒𝐡𝐢𝐩𝐩𝐢𝐧𝐠 discipline: 𝐀𝐏𝐈𝐬, 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲, and “𝐝𝐞𝐦𝐨-𝐟𝐢𝐫𝐬𝐭” iteration

What you’ll ship
𝐖𝐨𝐫𝐤𝐢𝐧𝐠 GenAI/LLM apps for 𝐫𝐞𝐚𝐥 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬
𝐑𝐀𝐆 pipelines you can 𝐭𝐞𝐬𝐭 and 𝐭𝐫𝐮𝐬𝐭
𝐃𝐞𝐩𝐥𝐨𝐲𝐞𝐝 𝐀𝐏𝐈𝐬 and minimal UIs (𝐅𝐚𝐬𝐭𝐀𝐏𝐈, 𝐆𝐫𝐚𝐝𝐢𝐨, 𝐇𝐅 𝐒𝐩𝐚𝐜𝐞𝐬)
A 𝐜𝐨𝐧𝐜𝐢𝐬𝐞, 𝐜𝐫𝐞𝐝𝐢𝐛𝐥𝐞 𝐩𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 that proves 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐲
Cohort 8 details

𝐒𝐭𝐚𝐫𝐭𝐬 Friday 𝟖 𝐍𝐨𝐯, 𝟏𝟐 𝐏𝐌 𝐆𝐒𝐓
𝟐𝟎-𝐰𝐞𝐞𝐤, 𝐦𝐞𝐧𝐭𝐨𝐫-𝐥𝐞𝐝, 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 format
For 𝐛𝐮𝐢𝐥𝐝𝐞𝐫𝐬, 𝐟𝐨𝐮𝐧𝐝𝐞𝐫𝐬, 𝐝𝐚𝐭𝐚/𝐌𝐋 pros, and 𝐜𝐚𝐫𝐞𝐞𝐫 𝐬𝐰𝐢𝐭𝐜𝐡𝐞𝐫𝐬

Outcomes our residents see
Moving from 𝐭𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬 to 𝐝𝐞𝐩𝐥𝐨𝐲𝐞𝐝 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬
𝐂𝐥𝐚𝐫𝐢𝐭𝐲 on 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 choices and 𝐭𝐫𝐚𝐝𝐞-𝐨𝐟𝐟𝐬
𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 pieces that stand up to 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 and stakeholder 𝐬𝐜𝐫𝐮𝐭𝐢𝐧𝐲

If you’re done collecting 𝐛𝐚𝐝𝐠𝐞𝐬 and ready to understand how 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 actually work, comment or DM “𝐑𝐞𝐬𝐢𝐝𝐞𝐧𝐜𝐲” and I’ll share the 𝐬𝐲𝐥𝐥𝐚𝐛𝐮𝐬 and 𝐬𝐚𝐦𝐩𝐥𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬.



#AIResidency #LLM #GenAI #RAG #AIforCareer #AIforFounders #AIforExecutives #decodingdatascience #dds

AI Residency  Best way to learn AI 

𝐓𝐡𝐞 𝐒𝐩𝐫𝐚𝐲 𝐚𝐧𝐝 𝐏𝐫𝐚𝐲 𝐦𝐞𝐭𝐡𝐨𝐝 𝐢𝐬 𝐝𝐞𝐚𝐝.

In 2025, if your resume isn’t at least an 𝟖𝟓% 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐦𝐚𝐭𝐜𝐡 to the Job Description, you aren’t at the bottom of the pile — you’re not in the pile at all.

𝐓𝐡𝐞 𝐧𝐞𝐰 𝐫𝐞𝐚𝐥𝐢𝐭𝐲:
• 𝐀𝐈 𝐆𝐚𝐭𝐞𝐤𝐞𝐞𝐩𝐞𝐫𝐬: ATS systems now rely on semantic matching, not keyword counting. If the JD says “Python for Distributed Systems” and your resume only signals general Python, you’re filtered out instantly.
 • 𝐓𝐡𝐞 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐆𝐚𝐩: Manually rewriting your resume for every role is not realistic.
 • 𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: You must become a “𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫” 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐨𝐰𝐧 𝐜𝐚𝐫𝐞𝐞𝐫.

The goal is not to fabricate — it’s to translate your real experience into the exact language used by employers and their AI filters.

𝐔𝐬𝐞 𝐭𝐡𝐢𝐬 𝐛𝐞𝐟𝐨𝐫𝐞 𝐚𝐩𝐩𝐥𝐲𝐢𝐧𝐠:

 “𝐀𝐜𝐭 𝐚𝐬 𝐚 𝐬𝐤𝐞𝐩𝐭𝐢𝐜𝐚𝐥 𝐒𝐞𝐧𝐢𝐨𝐫 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐑𝐞𝐜𝐫𝐮𝐢𝐭𝐞𝐫 𝐚𝐭 𝐚 𝐅𝐀𝐀𝐍𝐆 𝐜𝐨𝐦𝐩𝐚𝐧𝐲. 𝐑𝐞𝐯𝐢𝐞𝐰 𝐦𝐲 𝐫𝐞𝐬𝐮𝐦𝐞 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐭𝐡𝐢𝐬 𝐉𝐃. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝟑 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐫𝐞𝐝 𝐟𝐥𝐚𝐠𝐬 𝐰𝐡𝐞𝐫𝐞 𝐦𝐲 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐦𝐚𝐭𝐜𝐡. 𝐁𝐞 𝐫𝐮𝐭𝐡𝐥𝐞𝐬𝐬.”

𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐰𝐨𝐫𝐤𝐬:

 It forces clarity, removes vague statements, and aligns your achievements with the true hiring signals employers optimize for.

𝐈𝐟 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐭𝐡𝐞 𝟐𝟎𝟐𝟓 𝐑𝐞𝐬𝐮𝐦𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐆𝐮𝐢𝐝𝐞, 𝐟𝐢𝐧𝐝 𝐢𝐭 𝐛𝐞𝐥𝐨𝐰 — 𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲-𝐟𝐢𝐫𝐬𝐭 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨 𝐥𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐞𝐜𝐡 𝐫𝐨𝐥𝐞𝐬 𝐢𝐧 𝐚𝐧 𝐀𝐈-𝐟𝐢𝐥𝐭𝐞𝐫𝐞𝐝 𝐰𝐨𝐫𝐥𝐝.

#ResumeTips #TechCareers #JobSearch2025 #Engineering #AI #CareerAdvice #decodingdatascience

Prompt  Engineering for Resume Guide

The future of 𝐀𝐈 belongs to those who understand 𝐝𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠—

If you want to build cutting-edge AI, mastering 𝐝𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 isn’t just a skill — it’s the 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧.

From research labs to real-world applications, this combination is powering the 𝐧𝐞𝐱𝐭 𝐰𝐚𝐯𝐞 𝐨𝐟 𝐀𝐈 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡𝐬.

𝐖𝐡𝐲 𝐢𝐬 𝐏𝐲𝐭𝐡𝐨𝐧 𝐭𝐡𝐞 𝐠𝐨-𝐭𝐨 𝐟𝐨𝐫 𝐝𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠?

• 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 – PyTorch, TensorFlow, and Keras simplify development and deployment.
 • 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 & 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 – Seamlessly manage large datasets and train complex neural networks.
 • 𝐓𝐡𝐫𝐢𝐯𝐢𝐧𝐠 𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦 – Join a global community accelerating AI innovation.

Whether you're just starting out or scaling production-grade models, deep learning in Python gives you the 𝐟𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲, 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞, and 𝐦𝐨𝐦𝐞𝐧𝐭𝐮𝐦 to lead in the AI era.

🔍 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐜𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐨𝐧 𝐝𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧?
 Share your experiences or challenges in the comments — let’s learn from each other.

If you found this valuable, 𝐩𝐥𝐞𝐚𝐬𝐞 𝐫𝐞𝐬𝐡𝐚𝐫𝐞 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐨𝐭𝐡𝐞𝐫𝐬 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐧𝐞𝐭𝐰𝐨𝐫𝐤 𝐬𝐭𝐚𝐲 𝐚𝐡𝐞𝐚𝐝.



#DecodingDataScience #NeuralNetworks #AIInnovation #dds

Deep Learning with Python

𝗗𝗼𝗻'𝘁 𝗕𝗲 𝗜𝗻𝘁𝗶𝗺𝗶𝗱𝗮𝘁𝗲𝗱 𝗯𝘆 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀! 𝗟𝗲𝘁'𝘀 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗦𝗶𝗺𝗽𝗹𝗲 𝗔𝗻𝗮𝗹𝗼𝗴𝘆

For beginners, think of 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 as a powerful extension of something you might already know: 𝗹𝗶𝗻𝗲𝗮𝗿 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻!

🔍 𝗔𝗻𝗮𝗹𝗼𝗴𝘆 𝗶𝗻 𝗔𝗰𝘁𝗶𝗼𝗻:

In 𝗹𝗶𝗻𝗲𝗮𝗿 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻, we take data and try to find the "𝗯𝗲𝘀𝘁 𝗳𝗶𝘁" 𝗹𝗶𝗻𝗲 that predicts future outcomes. Simple and effective! 𝗡𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 take this idea further, 𝘀𝘁𝗮𝗰𝗸𝗶𝗻𝗴 𝗹𝗮𝘆𝗲𝗿𝘀 of these "best fit" predictions together. Instead of just one line, imagine a 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗼𝗳 𝗹𝗶𝗻𝗲𝘀 𝗮𝗻𝗱 𝗰𝘂𝗿𝘃𝗲𝘀, each layer refining the last. This allows neural networks to tackle 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀—like 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘇𝗶𝗻𝗴 𝗶𝗺𝗮𝗴𝗲𝘀 𝗼𝗿 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀.

So, if you understand 𝗹𝗶𝗻𝗲𝗮𝗿 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻, you're already on your way to grasping 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀! Check out the attached document for a more 𝗶𝗻-𝗱𝗲𝗽𝘁𝗵 𝗹𝗼𝗼𝗸 and some 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀.

💬 𝗟𝗲𝘁’𝘀 𝗗𝗶𝘀𝗰𝘂𝘀𝘀: Have you used 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 or 𝗹𝗶𝗻𝗲𝗮𝗿 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 in your work? Drop your thoughts or questions below!

🔄 𝗦𝗵𝗮𝗿𝗲 𝘁𝗵𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲: Know someone who's curious about 𝗔𝗜 but might be intimidated by the jargon? Feel free to 𝘀𝗵𝗮𝗿𝗲 this post with them!

Let’s keep learning together—𝗮𝗻𝘆𝗼𝗻𝗲 𝗰𝗮𝗻 𝗱𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗔𝗜 with the right resources and mindset!


Our New Cohort in Starting Nov 28, Get access to $5,500 Bonus Details in comments and content in AI Guild




#AI #NeuralNetworks #llm #data #MachineLearning #decodingDataScience

Neural Network

𝐅𝐨𝐜𝐮𝐬𝐢𝐧𝐠 𝐨𝐧 𝐀𝐈 𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬 — 𝐍𝐨𝐭 𝐀𝐈 𝐓𝐨𝐨𝐥𝐬.

Last week at 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐂𝐚𝐦𝐩 𝐃𝐮𝐛𝐚𝐢, I had the opportunity to speak about a message I deeply believe in:

 𝐀𝐈 𝐨𝐧𝐥𝐲 𝐜𝐫𝐞𝐚𝐭𝐞𝐬 𝐯𝐚𝐥𝐮𝐞 𝐰𝐡𝐞𝐧 𝐢𝐭 𝐝𝐫𝐢𝐯𝐞𝐬 𝐫𝐞𝐚𝐥 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬 — not when we chase the next tool, model, or feature.

As 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐩𝐚𝐫𝐭𝐧𝐞𝐫𝐬 for MeasureCamp Dubai, it was inspiring to see the region’s 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬, 𝐝𝐚𝐭𝐚, 𝐚𝐧𝐝 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 come together with one shared goal:

 𝐭𝐮𝐫𝐧 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐨 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬, 𝐚𝐧𝐝 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐢𝐧𝐭𝐨 𝐢𝐦𝐩𝐚𝐜𝐭.

During my session, we explored:
• 𝐖𝐡𝐲 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 𝐬𝐡𝐨𝐮𝐥𝐝 𝐬𝐡𝐢𝐟𝐭 𝐟𝐫𝐨𝐦 “𝐖𝐡𝐢𝐜𝐡 𝐭𝐨𝐨𝐥?” 𝐭𝐨 “𝐖𝐡𝐢𝐜𝐡 𝐨𝐮𝐭𝐜𝐨𝐦𝐞?”
 • 𝐇𝐨𝐰 𝐭𝐨 𝐫𝐞𝐭𝐡𝐢𝐧𝐤 𝐀𝐈 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐦𝐞𝐚𝐬𝐮𝐫𝐚𝐛𝐥𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐯𝐚𝐥𝐮𝐞
 • 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬 of outcome-driven AI across industries in the UAE
 • The mindset shift from 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 to 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐢𝐦𝐩𝐚𝐜𝐭 𝐩𝐚𝐭𝐡𝐰𝐚𝐲𝐬

Grateful to the organizers for bringing such an exceptional community together, and proud to represent Decoding Data Science as a community partner for this edition of MeasureCamp.

Sharing a few moments from the talk and the 𝐞𝐧𝐞𝐫𝐠𝐲 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐨𝐨𝐦.
When we stay 𝐨𝐮𝐭𝐜𝐨𝐦𝐞-𝐝𝐫𝐢𝐯𝐞𝐧, AI stops being hype and becomes a 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐢𝐞𝐫.

Nidhal Firoze Anosh Ahmed Tomáš Havlík

#decodingdatascience #dds #MeasureCampDubai #AI #DataScience #Analytics #DubaiTech #BusinessValue #AIMindset

Agentic AI 101: MCP, Tools, and Multi-Agent Systems

It is a practical session under AI Guild / AI Residency where we break down how modern AI agents work end-to-end. We’ll cover how agents use tools and function calling, why vector stores matter for reliable retrieval, what MCP enables for connecting models to external systems, and the core patterns behind multi-agent workflows (planner/executor, researcher/writer, critic loops). Expect an open, builder-focused discussion with clear examples and time for Q&A.

The future of technical careers isn’t about learning more tools

— it’s about learning to solve harder problems.

Today at 12 PM, I’m hosting a Career Talk on a theme that keeps coming up in AI, data, and engineering circles: the ladder is broken. The linear path we were all taught no longer reflects how careers grow in an AI-driven world.

To make this real, I’ll be using a story from our recent energy plant training:
A simple ML model worked perfectly in early tests… until real plant conditions changed. Drift appeared. Sensor behaviour shifted. Suddenly, accuracy collapsed.
That moment captures the new career reality. Static skills stop working in dynamic environments.

The future of technical careers isn’t about learning more tools

Most multi-agent demos fail for one reason: no evaluation layer.

If you’re building agentic AI, you’re not just shipping prompts—you’re shipping a system: tools, vector stores, memory, orchestration, and handoffs between agents. Without a clear way to test reliability, accuracy, and regressions, the “wow” disappears the moment users try real workflows.

In our next DDS session, we’ll break down:

Multi-Agentic Systems: roles (planner/executor/critic), coordination, tool use, memory patterns

MCP (Model Context Protocol): a clean way to standardize tool + data access across agent workflows

Evaluations: how to measure groundedness, consistency, tool-call correctness, and RAG quality using repeatable tests

If you’re building agents, this is the missing layer that turns experiments into production-ready systems.

Comment “AGENTS” and I’ll share the joining details.

Share with morepeople so it reaches more people

#decodingdatascience #dds #ai

Most multi-agent demos fail for one reason

Everyone is talking about AI Agents. Very few can explain how Agentic AI actually works end-to-end.

We’re hosting an open community discussion on Agentic AI: From LLM to Multi-Agent Systems—practical, builder-focused, and clear enough to apply immediately.

What we’ll cover (with real examples):
Tools + function calling: how agents take actions safely
Vector stores: why retrieval is the backbone of reliable agents
MCP (Model Context Protocol): connecting models to external systems cleanly

Multi-agent patterns: planner/executor, researcher/writer, critic loops, routing

Where agents fail: reliability, evaluation, observability, cost
To help more builders join this discussion:
Comment AGENTS if you’re joining
Tag one builder friend who’s exploring agents or RAG
Repost so more people discover it

Decoding Data Science

I’ll share the full joining details and registration link in the comments.

#decodingdatascience #dds

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