🚀 𝐈 𝐚𝐝𝐯𝐢𝐬𝐞 𝐅𝐨𝐮𝐧𝐝𝐞𝐫𝐬, 𝐂𝐄𝐎𝐬, & 𝐈𝐧𝐯𝐞𝐬𝐭𝐨𝐫𝐬 𝐨𝐧 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 & 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐭𝐡𝐞𝐢𝐫 𝐀𝐈 𝐆𝐫𝐨𝐰𝐭𝐡 𝐄𝐧𝐠𝐢𝐧𝐞𝐬 𝐭𝐨 $𝟏𝟎𝟎𝐌+ | 𝐂𝐡𝐢𝐞𝐟 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐎𝐟𝐟𝐢𝐜𝐞𝐫, 𝐂𝐡𝐢𝐞𝐟 𝐆𝐫𝐨𝐰𝐭𝐡 𝐎𝐟𝐟𝐢𝐜𝐞𝐫, & 𝐂𝐡𝐢𝐞𝐟 𝐂𝐨𝐦𝐦𝐞𝐫𝐜𝐢𝐚𝐥 𝐎𝐟𝐟𝐢𝐜𝐞𝐫 | 𝐊𝐞𝐲𝐧𝐨𝐭𝐞 𝐒𝐩𝐞𝐚𝐤𝐞𝐫 | 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐌𝐞𝐧𝐭𝐨𝐫

I am the Founder & CEO @ Aetheris Ventures: building AI-powered growth engines that turn early stage potential into market leaders. I've architected revenue strategies that have scaled companies from pre-seed to IPO, including one unicorn exit! 🦄 

🔗 𝐀𝐝𝐯𝐢𝐬𝐨𝐫𝐲: https://angeleymullins.org/advisory
🔗 𝐒𝐩𝐞𝐚𝐤𝐢𝐧𝐠 : https://angeleymullins.org/speaking
🔗 𝐌𝐞𝐧𝐭𝐨𝐫𝐢𝐧𝐠 : https://angeleymullins.org/mentoring

I have been a Chief Commercial Officer, Chief Marketing Officer, and Chief Growth Officer building 4X scale ups in addition to leading revenue-generating engines for some of the world's most successful technology brands, including Amazon, Intuit, and GoDaddy. I was recognised by Crunchbase, as one of the most influential women in sales, highlighting my ability to use an inbound demand generation engine to scale digital revenue growth. My revenue strategies are perfect for: 

✅ B2B SaaS and E-Commerce Founders & CEOs scaling GTM & Revenue from $0 to $50M ARR
✅ Companies looking to build a brand or grow brand awareness to generate revenue
✅ CEO's who want to increase pipeline conversion out of existing revenue engines to $100M+ ARR

I have a passion for Brand & Content to drive revenue and have been featured in Forbes, VentureBeat, The Drum, and other leading media and publications. I am also a frequent keynote speaker and podcaster with communities including: SaaStock, Turing Fest, SaaSiest, ARRtist, SheCanCode, CMO Alliance, Cognism, and many others.

I am passionate about scaling purpose- driven tech, using technology to give back to the community and mentoring the next generation of leaders. I hold multiple board and advisory roles including: World Bicycle Relief, The Female Factor, Coders Beyond Borders, and The Revenue Marketing Alliance among many others. I have a special interest for advancing the conversation around women in leadership and diversity at the executive level.

✅ Follow me for actionable insights on: 

➡️ GTM & AI Growth Strategy in both B2B SaaS & D2C
➡️ Commercial Leadership & Revenue Scaling
➡️ Marketing & Brand Positioning
➡️ AI in Marketing & Content Engines 
➡️ Women in Leadership & Women in Tech
➡️ Pitch Decks & Winning Investment

Angeley Mullins

What a great kickoff to 2026 with an amazing event in Paris 🇫🇷 🇫🇷 🇫🇷 with Founders and Operators from Weglot to support the "Go Global" program!!! We are helping some the leading startups and scale-ups expand internationally and the event was so much fun! 😀 . 

If you are an early stage tech company dreaming of going global, international expansion is possible. However I have watched too many brilliant founders stumble. During the event I had the chance to give some tips on International expansion. 

Here are the biggest mistakes I see Founders and CEOs make: 

🌍 𝐓𝐡𝐞𝐲 𝐟𝐚𝐢𝐥 𝐭𝐨 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐜𝐮𝐥𝐭𝐮𝐫𝐚𝐥 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬: What works in your country might fail in another market. This is one of biggest mistakes that companies make. It doesn't matter if you are a European company trying to expand to the US, or you are a US or UK company trying to expand into Europe, the understanding of cultural differences can make or break your success. 
🌍 𝐓𝐡𝐞𝐲 𝐚𝐬𝐬𝐮𝐦𝐞 𝐭𝐡𝐞𝐢𝐫 𝐩𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐰𝐢𝐥𝐥 𝐰𝐨𝐫𝐤 𝐞𝐯𝐞𝐫𝐲𝐰𝐡𝐞𝐫𝐞: Your growth hack that worked in San Francisco? It might fall completely flat in Singapore. Your brilliant GTM strategy that worked in London? It might crash and burn in Berlin. Playbooks are best when they are customised. 
🌍 𝐓𝐡𝐞𝐲 𝐭𝐫𝐲 𝐭𝐨 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐇𝐐: Parachuting in strategies without local voices is a fast track to blind spots. Your first hire in a new market is not just an employee, they are your cultural translator, your reality check, your bridge to customers you have not met yet. Build with locals, not for them.
🌍 𝐓𝐡𝐞𝐲 𝐬𝐩𝐫𝐞𝐚𝐝 𝐭𝐨𝐨 𝐭𝐡𝐢𝐧, 𝐭𝐨𝐨 𝐟𝐚𝐬𝐭: Ten countries sounds impressive, but one market where you truly understand your customers beats ten where you are guessing. Start small, learn deeply, win there, then expand. Depth before breadth always wins.

A huge thanks to everyone on the Weglot team for their support w/ Elodie L., Pierre FERTOUT, Noemie R., Merve Alsan, Elizabeth P., Thomas Fanchin, Augustin P., Rayne A., and all the amazing members on the team! 😍 

Also thanks to the amazing Founders, CEOs, and GTM Leaders who building and scaling internationally: w/ Saber Naceur, Maeva Giani Marshall, Bill Stathopoulos, Fabian Hartmann, Cecilia T., Batiste Pasquet, Ruta Sudmantaite, Emma Davies, Jennifer Hoffman, Julian Philipp Nagel, Ishaan Pulast, Michal Lasman, ✨Ananya Chattaraj, Stefania Guglielmetto, Franziska Heyde, Cynthia Mensah-Neglokpe, Gina Neumann, Monika Vo, and so many other great leaders!! 
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👋 Hi, I'm Angeley! I work with Founders & CEOs to build & scale their AI Marketing and Revenue Engines. I am also a public speaker and love inspiring audiences around the world about the future of technology and women in leadership
🔗 You can learn more about me here: https://lnkd.in/dpq3Y-GV

Founder & CEO @ Aetheris Ventures: I help CEOs build and scale their growth engines to $100M+ | Keynote Speaker, AI GTM & Marketing Strategist, Board Member, Empowering Female Leaders, Crunchbase Most Influential Women

Looking to supercharge your marketing strategy in 2026? 🚀  Here are my pics on the top 10 essential AI tools every marketer should know about:

𝗗𝗥𝗘𝗔𝗠 𝗜𝗧:
✅ Jasper: Transform your boldest content ideas into reality
✅ AirOps: Turn scattered thoughts into streamlined content workflows
✅ Claude: Your AI partner that thinks alongside you, not for you

𝗕𝗨𝗜𝗟𝗗 𝗜𝗧:
✅ Surfer: Rank where you deserve to be, backed by data that works ( SEO/AEO/GEO) 
✅ Canva: Design like a pro, even if you never thought you could
✅ Synthesia: Create stunning videos without cameras, crews, or complexity

𝗦𝗖𝗔𝗟𝗘 𝗜𝗧:
✅ Zapier: Multiply your impact by connecting everything that matters
✅ AdCreative.ai: Launch campaigns that capture hearts and minds
✅ Windsor.ai:See the full picture of what's actually driving results
✅ Ocoya: Be everywhere your audience is, effortlessly

These tools don't replace your creativity, vision, or strategic mind. They amplify it. 🔥 🔥 🔥 They free you from the mundane so you can focus on the meaningful. They turn "What if?" into "Watch this."

The marketers thriving in 2026 are not the ones with the biggest budgets, they are the ones who learned how to balance their vision with the best AI tools in order to help themselves and their teams become more efficient! 

As always giving a shout out to some of the amazing marketing leaders out there paving the way: w/ Islam Gouda, Monika Vo, Julieta Varsano, Ulrike Grandi-Haferstroh, Jennifer Hoffman, Joy Corkery, Anastasia Albert 🇺🇦, Mayuri Reddy, Janina Schütz, Gina Neumann, Claire Connell, Johanna Kassing, Agrima Sharma, Emiliia Karpiuk, Megan Thudium, Elisheva Marcus, Stefania Guglielmetto, ✨Ananya Chattaraj, Cindy Fiorella Rojas, Alicia Smith, Cecilia T., and so many amazing others!!! 

Want more insights on Marketing & AI? Follow for regular updates on the latest tools and strategies! 

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👋 Hi, I'm Angeley! I work with Founders & CEOs to build & scale their AI Marketing and Revenue Engines. I am also a public speaker and love inspiring audiences around the world about the future of technology and women in leadership
🔗 You can learn more about me here: https://lnkd.in/dpq3Y-GV

Most of your marketing tools will be legacy tech by 2027!!! 😱 The half-life of marketing technology is shrinking fast. What felt innovative in 2023 are table stakes in 2026. And here's what's wild: lean teams are now outperforming entire departments. Solo founders are launching campaigns that used to require agencies. The shift is real...

In my latest article for Weglot, titled " 𝐓𝐡𝐞 𝐓𝐨𝐩 𝐀𝐈 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 & 𝐆𝐓𝐌 𝐓𝐨𝐨𝐥𝐬 𝐟𝐨𝐫 2026" I talk about the new GTM reality and which categories of tools you will need in this new era. 

🔗 🔗 🔗 You can read the article here: https://lnkd.in/d9pmt7uA

I have broken down the six essential categories every modern GTM stack needs: 
1️⃣ Content Intelligence that maintains your brand voice at scale 
2️⃣ Customer Data platforms that predict buying behavior 
3️⃣ Sales Enablement that personalises outreach without the manual work 
4️⃣ Personalisation engines that deliver tailored experiences
5️⃣ Analytics that finally connect activities to revenue 
6️⃣ GTM Orchestration that coordinates everything seamlessly

In the article I outline the standout platforms for 2026: from Clay's game changing data enrichment to Jasper's brand consistent content creation, from Warmly,'s real time visitor identification to HockeyStack multi-touch attribution that actually makes sense. I also talk about Perplexity, AirOps, 6sense, Outreach, HockeyStack, and other great tools! 

The future of GTM isn't about doing more. It's about doing what matters, with tools that multiply your impact. 🚀 

Thanks to the wonderful team at Weglot for supporting founders and companies to not just expand but to "Go Global" 🌎  w/ Elizabeth P., Elodie L., Thomas Fanchin, Eugene Ernoult, Merve Alsan, Noemie R., Rayne A., Augustin P., Pierre FERTOUT, Thomas Fanchin, and everyone else on the team! 

Here's to all the great GTM leaders out there who are paving the way through the balance of AI tools and GTM excellence: w/ Bill Stathopoulos, Macklin Buckler, Steven Lewandowski, Fernanda Bosmediano, Batiste Pasquet, Ekaterina Parashina, Busayomi Omotosho, Magdolin Harmina, Mayuri Reddy, Franziska Heyde, Haley Glass, Isabel Delgado, Fabian Hartmann, Ruta Donovan, Stefania Guglielmetto, ✨Ananya Chattaraj, Cindy Fiorella Rojas, Adriana Carmona Beltran, Tatjana Pohl, Agrima Sharma, and so many other fabulous leaders!!

The best brands don't win by shouting louder, they win by telling stories that matter. 📢 ⭐  That's why i'm so excited to be joining the The Customer Conference in London 🇬🇧 this January to speak about what I believe will be one of the most important GTM topics this year: 𝐖𝐡𝐲 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐭𝐡𝐞 𝐖𝐢𝐧𝐧𝐢𝐧𝐠 𝐆𝐓𝐌 & 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐌𝐨𝐭𝐢𝐨𝐧 𝐨𝐟 2026.  I will be doing this incredible fireside chat alongside the amazing Steven Lewandowski of addressable value. 

📍 The Customer Conference | London 🇬🇧 
📅 January 28-29, 2026
🎟️ Register here:  https://lnkd.in/d_JTtFSS and use the discount code: TCC4SPEAKERS

Here is what we are already seeing with so many AI tools...a world drowning in features, specs, and noise. The companies that will thrive are those that understand something profound: 𝑷𝒆𝒐𝒑𝒍𝒆 𝒅𝒐𝒏'𝒕 𝒃𝒖𝒚 𝒑𝒓𝒐𝒅𝒖𝒄𝒕𝒔, 𝒕𝒉𝒆𝒚 𝒃𝒖𝒚 𝒊𝒏𝒕𝒐 𝒔𝒕𝒐𝒓𝒊𝒆𝒔. 𝑻𝒉𝒆𝒚 𝒊𝒏𝒗𝒆𝒔𝒕 𝒊𝒏 𝒗𝒊𝒔𝒊𝒐𝒏𝒔. 𝑻𝒉𝒆𝒚 𝒄𝒐𝒎𝒎𝒊𝒕 𝒕𝒐 𝒋𝒐𝒖𝒓𝒏𝒆𝒚𝒔 𝒕𝒉𝒆𝒚 𝒄𝒂𝒏 𝒔𝒆𝒆 𝒕𝒉𝒆𝒎𝒔𝒆𝒍𝒗𝒆𝒔 𝒊𝒏. 🔥 

We are at an inflection point where authenticity is not just nice to have, it's the competitive advantage. Where emotional resonance drives revenue. Where the narrative you craft can be the difference between a logo on a slide and a customer who becomes your evangelist. This is not just a "nice to have" or a theory...This is the future unfolding right now.

Join us as we explore: 
✅ How to break through the noise with narratives that captivate
✅ How to transform sales conversations from transactional to transformational 
✅ How to turn every customer interaction into a chapter in an ongoing story of mutual success

Whether you are building a company, leading a team, or shaping a go-to-market strategy (GTM), this conversation is for anyone who believes that connection beats competition, and stories beats specs.

Let's reimagine what's possible when we put human connection at the center of how we grow. 🚀 

👋 See you in London! 🇬🇧 
W/ Olha Semeikin, Sara Babila, Shay Hakimi, Nisan Bartov, Adrian Beck, Dora K., Chris Singh, Dave J., Sandy Yu, Bob London, Adi Janowitz (she/her), Alejandra Otero, Eleanor O'Neill, Orly Gerassi Ganor, Richard McGuinness and so many others! Also a shout out to the wonderful leaders who are driving GTM growth and focusing on real value w/ Jennifer Hoffman, Rahma Hassan, Ishaan Pulast, Nadia McKay, Anastasia Albert 🇺🇦, Franziska Heyde, Gina Neumann, Ruta Sudmantaite, Emma Davies, Haley Glass, Joy Corkery, Vera Matisovits, Vessela Krissel, Ekaterina Parashina, Mythili M., Louise Alsheimer-Niklasson, Elnura Ashimova, Katie Payne, Ale Wiecek Rojo, Adriana Carmona Beltran, Alexandra Matthews, and so many other wonderful leaders! ✨

2026 is not just another year... It's a reset in the marketing community. The rules of marketing are being rewritten in real time, and the winners are not the ones with the biggest budgets, they are the ones moving fastest. 🔥 

I just had the chance to read the new 2026 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐀𝐝𝐯𝐞𝐫𝐭𝐢𝐬𝐢𝐧𝐠 𝐓𝐫𝐞𝐧𝐝𝐬 𝐑𝐞𝐩𝐨𝐫𝐭 by Smartly, and they collected data from over 450 Marketing Leaders to give insights on some of the top trends we will see this year: 

1️⃣ 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐋𝐞𝐚𝐝𝐞𝐫𝐬 𝐰𝐢𝐥𝐥 𝐩𝐫𝐞𝐝𝐢𝐜𝐭 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐫𝐞𝐚𝐜𝐭: 1 in 3 marketers already use AI for predictive modelling and creative validation. They know what will work before spending. This one is a huge leap from the traditional " step by step iteration" into "predictive modelling". 

2️⃣ 𝐓𝐡𝐞𝐲 𝐰𝐢𝐥𝐥 𝐭𝐡𝐢𝐧𝐤 𝐢𝐧 𝐟𝐥𝐨𝐰𝐬, 𝐧𝐨𝐭 𝐟𝐮𝐧𝐧𝐞𝐥𝐬: The customer journey is not linear anymore. It's fluid, unpredictable, and personalised from the very first moment. 92% of marketers agree that AI has fundamentally changed how we think about reaching customers. We will still have our standard funnels however it is no longer about inbound vs outbound or channel X vs Channel Y. It is an "all-bound and all-channel" approach. 

3️⃣ 𝐕𝐢𝐝𝐞𝐨 𝐢𝐬 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧: Video and animation are now becoming the connective tissue for engagement and the universal language of modern advertising

While some marketers debate whether to adopt AI, others are already three steps ahead, turning intelligence into advantage and complexity into clarity.

The best Marketing Leaders in 2026 are "video first", cross channel fluent, and AI intensive. 

This is your moment to be one of them and rock 2026!! 🚀 

Would you like to see the report for yourself? Download the report using the link in the comments. 👇 👇 👇 

Of course giving a shout out to some of the fabulous marketing leaders out there building the future of how we tell our stories and attract clients: Islam Gouda, Michal Lasman, Monika Vo, Mayuri Reddy, Joy Corkery, Cindy Fiorella Rojas, Stefania Guglielmetto, ✨Ananya Chattaraj, Jennifer Hoffman, Julia Goelles, Janina Schütz, Gina Neumann, Megan Thudium, Claire Connell, Alicia Smith, Anastasia Albert 🇺🇦, Emiliia Karpiuk, Richard King, Laura Jenkins, Cecilia T., Julieta Varsano, Ulrike Grandi-Haferstroh, Emma Davies, Ruta Sudmantaite, and so many more!!

My missions
→ Lead & ship production-grade AI/ML systems - including AI Agents & RAG that actually move business metrics.
→ Educate AI/ML practitioners on how real-world production AI/ML systems are designed, deployed, and maintained (the parts most people skip).

My portfolio
→ Senior Data Scientist / AI Engineer (8+ years)
→ Built an AI Agent–powered FAQ chatbot (LangChain/LangGraph, AWS) used by thousands; significantly reduced customer support load.
→ Built end-to-end RAG: ingestion, semantic/recursive chunking, retrieval strategies, GDPR-first data flows
→ Built CLV forecasting, recommendation systems, marketing optimization, and scalable MLOps systems with significant business impact

My newsletter
→ JAM with AI - read by 26,000+ AI/ML builders
→ jamwithai.substack.com

Work with me
→ Collaborations, talks, and mentoring.

Shirin Jam

I've read 25+ AI/ML books in 8 years. 

Only 11 would actually move the needle in production.

(I've shipped Data Science, MLOps, RAG systems, and AI agents)

Here's what actually matters:
📌 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀
→ Mathematics of Machine Learning - Tivadar Danka
Understand the foundations with great intuitions!
🔗 Link - https://lnkd.in/d3g494VJ

→ ML with PyTorch and Scikit-Learn - Sebastian Raschka, PhD
Bridge theory to practice with both classical ML and deep learning.
🔗 Link -  https://lnkd.in/dkBwkjBc

📌 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 & 𝗦𝘁𝗮𝘁𝘀
→ Fluent Python - Luciano Ramalho
Write production-grade Python code.
🔗 Link - https://lnkd.in/dPzSFYmr
(skip it if you think you can learn better through courses)

→ Practical Statistics for Data Scientists - Bruce & Bruce
A/B test models and detect data drift.
🔗 Link - https://lnkd.in/dwtNNU4R

📌 𝗡𝗟𝗣 
→ Python NLP Cookbook - Antić & Chakravarty
Classical NLP for when RAG retrieves garbage.
🔗 Link -  https://lnkd.in/dVPGPew6

→ NLP with Transformers - Tunstall, von Werra, Wolf
Understand what's under the hood of LLMs.
🔗 Link - https://lnkd.in/dqk62AQT

📌 𝗠𝗟 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
→ Designing Machine Learning Systems - Chip Huyen
Be in the 13% that reach production.
🔗 Link - https://lnkd.in/dPXxKNgk

→ ML System Design Interview - Aminian & Xu
Think in patterns: scale from 1K to 100M users.
🔗 Link - https://lnkd.in/d_V98BW9

📌 𝗟𝗟𝗠𝘀 & 𝗔𝗴𝗲𝗻𝘁𝘀
→ AI Agents in Practice - Valentina Alto
Build agents that work in production.
🔗 Link -https://lnkd.in/d92bycbU

→ AI Engineering - Chip Huyen
LLMs in production: RAG, prompts, infrastructure.
🔗 Link - https://lnkd.in/dw_F-PtH

📌 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲
→ Designing Data-Intensive Applications - Martin Kleppmann
Your model is only as good as your infrastructure.
🔗 Link - https://lnkd.in/duWW2tD6

𝗠𝘆 𝗵𝗼𝗻𝗲𝘀𝘁 𝘁𝗮𝗸𝗲:
Pick 1-2 books. 
Build something small. 
Come back when you hit problems.

Detailed post on WHEN each saved me → Link in comments 👇

𝗪𝗵𝗮𝘁 𝗯𝗼𝗼𝗸𝘀 𝘀𝗵𝗮𝗽𝗲𝗱 𝘆𝗼𝘂𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆?

PS: There are many books out there, but these are the list of must read from my end. For learning LLMs as well there are multiple resources, so didnt include. 

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♻ Repost if to help others!
➕ Join 36.500+ AI/ML builders here: https://lnkd.in/ds_SzEUH

Shirin Khosravi Jam

Sr. Data Scientist/ AI Engineer | Data Science, RAG, AI Agents, & MLOps | Germany’s Top Female Voice in AI (Favikon) 🇩🇪

Stop using just Kaggle for ML data

7 real-world data sources for your Machine Learning projects:

𝟭. 𝗦𝘁𝗿𝗮𝘁𝗮𝗦𝗰𝗿𝗮𝘁𝗰𝗵
→ 49+ real business datasets
→ Actual interview-style problems
→ https://lnkd.in/dkscDjvQ

𝟮. 𝗨𝗖𝗜 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆
→ 653+ datasets, battle-tested
→ What researchers actually use
→ https://lnkd.in/dzCUAFpH

𝟯. 𝗔𝗪𝗦 𝗣𝘂𝗯𝗹𝗶𝗰 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀
→ 494+ production-scale datasets
→ Learn cloud integration from day 1
→ https://lnkd.in/dAqnJHjv

𝟰. 𝗪𝗼𝗿𝗹𝗱 𝗕𝗮𝗻𝗸 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮
→ Global economic/social data
→ Perfect for real-world impact projects
→ https://lnkd.in/dYu-m2PK

𝟱. 𝗗𝗮𝘁𝗮.𝗴𝗼𝘃
→ 283K+ government datasets
→ 50+ formats, serious scale
→ https://data.gov/

𝟲. 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗦𝗲𝗮𝗿𝗰𝗵
→ Academic + research data
→ Find what others can't
→ https://lnkd.in/d-ziT_3z

𝟳. 𝗢𝗽𝗲𝗻𝗠𝗟
→ 5,400+ datasets with ML benchmarks
→ Compare your work to SOTA
→ https://www.openml.org/

NOTE:
One useful source of data would be:
Making use of LLM to build synthetic data

You definitely can practice on the synthetic data 
In fact it could be much closer to the real-world scenario!

Bookmark this to come back to it the next time.

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Three things remain consistent in 2026 
Python . PyTorch . GenAI

But GenAI ≠ just LLMs.

𝗧𝗵𝗶𝘀 𝗯𝗼𝗼𝗸 𝗰𝗼𝘃𝗲𝗿𝘀 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝘀𝗽𝗲𝗰𝘁𝗿𝘂𝗺.

Recently I reviewed the book:
Generative AI with Python and PyTorch (2nd Ed)
By Raghav Bali & Joseph Babcock (practitioners with AI patents & PhDs)

I particularly liked this book because:
Everyone talks about LLMs.
Few master image generation.
This teaches both at production scale.

𝗟𝗟𝗠 𝗠𝗮𝘀𝘁𝗲𝗿𝘆:
✅ RAG systems (LangChain, vector DBs)
✅ Fine-tuning without burning cash (LoRA, PEFT)
✅ Open-source deployment (Llama, Mistral)
✅ Prompt engineering that works

𝗜𝗺𝗮𝗴𝗲 𝗚𝗲𝗻 𝗠𝗮𝘀𝘁𝗲𝗿𝘆:
✅ Diffusion models (Stable Diffusion, DALL-E techniques)
✅ GANs for style transfer & deepfakes
✅ VAEs for optimization
✅ CLIP for vision-language models

Perfect if you're building GenAI systems 
that go beyond chat interfaces.

Book link - https://lnkd.in/dvjsfGbq
Github code - https://lnkd.in/dtpQ6MnH

Definitely great book for data scientists & ML engineers building GenAI who wants to learn building GenAI systems across Text as well as Image!

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This Open-source 122K GitHub stars app solves the hardest part of building AI apps.

And most AI engineers still don't know about it.

Dify gives you:

RAG Pipeline
→ Document ingestion to retrieval
→ PDF, PPT extraction out-of-box

Agent Framework
→ 50+ built-in tools
→ Google Search, DALL·E, Stable Diffusion

Visual Workflows
→ Build AI pipelines on a canvas
→ No more spaghetti orchestration code

LLMOps
→ Logs, monitoring, analytics
→ Improve prompts from production data

Must check it out.
122K developers can't be wrong.

Link in comments.

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Stop building RAG & AI Agents in Jupyter notebooks

Use this production-ready FastAPI base repo as your starting point.

This is Infrastructure-first approach!
🔹 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼 (builder-first):

1️⃣ FastAPI app 
→ clean src/ with routers / services / repositories / schemas
↳ Pydantic models, dependency injection, structured logging, config in .env

2️⃣ Database ready 
→ PostgreSQL + SQLAlchemy + Alembic
↳ migrations, seeds, environment-driven settings

3️⃣ Search & vectors 
→ OpenSearch (BM25 + vector) wired in
↳ ready for RAG retrieval out of the box

4️⃣ LLM hookup 
→ Ollama (local) endpoints you can swap later
↳ notebook included that explains setup end-to-end

5️⃣ DevX & ops 
→ pytest, ruff, uv, Docker/compose, Airflow (optional)
↳ reproducible installs, linting, pipelines, and a sane pyproject.toml

🔹 𝗤𝘂𝗶𝗰𝗸 𝘀𝘁𝗮𝗿𝘁
1️⃣ Clone & copy .env.example → .env
2️⃣ uv sync to install deps
3️⃣ docker compose up -d (FastAPI, Postgres, OpenSearch, Ollama)

🔹 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁
CI/CD + cloud infra in the next phases.

Link: https://lnkd.in/dCYbsY3c
(Also in the pinned comment if you need it later.)

Save this for your next project!

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If you are starting with AI engineering,
here are basics of RAG you should know.

Most people overcomplicate RAG.
Here's what actually matters:

𝗧𝗵𝗲 𝘁𝗿𝘂𝘁𝗵:
95% of production RAG systems
use the same basic components.

You don't need fancy architectures.
You need solid fundamentals.

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘀𝗶𝗺𝗽𝗹𝗲 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻:
What RAG actually is:
↳ Retrieval = grounding in real data
↳ Generation = flexible, natural responses

That's it. Not magic. Just smart information retrieval.

𝗧𝗵𝗲 𝟱 𝗰𝗼𝗿𝗲 𝗽𝗶𝗲𝗰𝗲𝘀 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱:
1️⃣ Embedder (OpenAI works fine)
2️⃣ Retriever (vector search)
3️⃣ Reranker (optional but improves quality)
4️⃣ Prompt Constructor (assembles context)
5️⃣ Generator (your LLM)

𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝘁𝘁𝗲𝗿:
- Chunking: 512-1000 tokens with 20-100 token overlaps
- Hybrid Search: BM25 + vectors (better than vector alone)
- Metadata Filtering: source, date, type tags
- Prompt Engineering: "Use only provided context"
- Caching: speeds up + reduces cost

𝗦𝗸𝗶𝗽 𝘁𝗵𝗲𝘀𝗲 𝘂𝗻𝘁𝗶𝗹 𝗯𝗮𝘀𝗶𝗰𝘀 𝘄𝗼𝗿𝗸:
❌ Complex multi-agent systems
❌ Graph RAG architectures
❌ Custom fine-tuned embeddings
❌ Advanced routing logic

𝗧𝗼𝗼𝗹𝘀 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵:
↳ LangChain 
↳ Qdrant or Weaviate (vector DB)
↳ OpenAI embeddings (they work)
↳ RAGAS for evaluation

𝗛𝗼𝘄 𝘁𝗼 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲:
- Precision@k, Recall@k (retrieval quality)
- Faithfulness (uses provided context)
- Helpfulness (actually answers question)

That's the playbook.

Keep it simple.
Build on fundamentals.
Add complexity only when needed.

Most "advanced" RAG systems fail
because they skipped the basics.

Don't be that engineer.
Also remember the biggest component of many RAG systems is the pre-processing techniques e.g. OCR/multi-modal extractors.

What's your biggest RAG challenge?

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𝐌𝐨𝐬𝐭 𝐀𝐈/𝐌𝐋 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐥𝐞𝐚𝐫𝐧 𝐭𝐡𝐞𝐨𝐫𝐲.
4 end-to-end projects that will
actually make you production-ready:

1️⃣ 𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐌𝐋 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞
↳ Build churn prediction for fintech/telecommunication platform
↳ Clean data with Pandas/Polars
↳ Train with LightGBM or CatBoost
↳ Track experiments with MLflow
↳ Serve with FastAPI + Docker on AWS
👉 𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗠𝗟 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗶𝗻 𝗼𝗻𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁.

2️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐑𝐀𝐆 𝐂𝐡𝐚𝐭𝐛𝐨𝐭
↳ Ingest product support docs
↳ Chunk & embed the data
↳ Store in OpenSearch or pgvector
↳ Use Ollama or Jina LLMs (free)
↳ UI in Streamlit, monitor in Langfuse
👉 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗽𝗼𝘄𝗲𝗿𝘀 𝗿𝗲𝗮𝗹 𝗚𝗲𝗻𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀.
You can build this TODAY with our
free RAG course: https://lnkd.in/dXjBwX6Z

3️⃣ 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐞 𝐚𝐧 𝐋𝐋𝐌 𝐨𝐧 𝐍𝐢𝐜𝐡𝐞 𝐃𝐚𝐭𝐚
↳ Collect domain docs (medical, legal, etc.)
↳ Fine-tune with LoRA/DPO
↳ Evaluate for hallucination & accuracy
↳ Deploy with HuggingFace Inference
👉 𝗔𝗱𝗮𝗽𝘁 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗱𝗼𝗺𝗮𝗶𝗻.

4️⃣ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐟𝐨𝐫 𝐌𝐨𝐝𝐞𝐥 + 𝐃𝐚𝐭𝐚 𝐃𝐫𝐢𝐟𝐭
↳ Detect drift in CLV model post-deployment
↳ Track with Evidently or Arize
↳ Trigger retraining via Airflow
↳ Visualize metrics in Grafana
👉 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗱𝗼𝗻'𝘁 𝗲𝗻𝗱 𝗮𝘁 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁, 𝘁𝗵𝗲𝘆 𝘀𝘁𝗮𝗿𝘁.

Theory gets you interviews.
Production systems get you hired.

Which project are you building first?

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8 terms every AI/ML engineer should know

(and how they work together)

1️⃣ 𝗘𝗻𝗰𝗼𝗱𝗲𝗿
The foundation model that converts
text into vector embeddings

Think: Text → Numbers

"Machine learning" → [0.23, -0.45, 0.89...]
Similar concepts get similar numbers.

2️⃣ 𝗕𝗶-𝗘𝗻𝗰𝗼𝗱𝗲𝗿 (𝗗𝘂𝗮𝗹 𝗘𝗻𝗰𝗼𝗱𝗲𝗿)
Encodes query and documents separately

Encode 1M documents once.
Store them.
When query comes, encode only the query.
Compare against pre-computed embeddings.

𝗪𝗵𝘆 𝗶𝘁'𝘀 𝗳𝗮𝘀𝘁:
↳ Pre-compute doc embeddings once
↳ Only encode query at runtime
↳ Simple similarity comparison

𝗨𝘀𝗲 𝗳𝗼𝗿: Initial retrieval (Stage 1)

3️⃣ 𝗖𝗿𝗼𝘀𝘀-𝗘𝗻𝗰𝗼𝗱𝗲𝗿
Processes query + document together

Reads both texts simultaneously.
Creates rich connections between every word.

Like having a conversation vs reading notes.

𝗪𝗵𝘆 𝗶𝘁'𝘀 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲:
↳ Full attention between texts
↳ Captures deep semantic relationships
↳ Learns exact relevance

Can't pre-compute. Must run per query-doc pair.
𝗨𝘀𝗲 𝗳𝗼𝗿: Final re-ranking (Stage 2)

4️⃣ 𝗥𝗲-𝗿𝗮𝗻𝗸𝗲𝗿
Any model that improves initial ranking

Retrieved 100 docs. Which 10 are truly best?
Re-ranker looks deeper at top candidates.

Think: First pass vs final review.

𝗧𝘆𝗽𝗲𝘀:
↳ Cross-encoder (most common)
↳ ColBERT (token-level matching)
↳ Cohere re-rank API
↳ Custom scoring models

5️⃣ 𝗗𝗲𝗻𝘀𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹
Vector-based search using embeddings

Search by meaning, not exact words.

Query: "affordable housing"
Finds: "cheap apartments", "budget homes"
Vectors capture semantic similarity.

𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:
↳ Neural encoders create embeddings
↳ Semantic similarity matching
↳ Finds meaning, not just keywords

𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Sentence Transformers, OpenAI embeddings

6️⃣ 𝗦𝗽𝗮𝗿𝘀𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹
Traditional keyword-based search

Query: "machine learning tutorial"
Finds docs with those exact terms.

Counts word frequency. No neural networks.

𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:
↳ BM25, TF-IDF algorithms
↳ Exact term matching
↳ Fast and interpretable

𝗨𝘀𝗲 𝗳𝗼𝗿: Hybrid search baseline

7️⃣ 𝗛𝘆𝗯𝗿𝗶𝗱 𝗦𝗲𝗮𝗿𝗰𝗵
Combining dense + sparse retrieval

Sparse catches exact matches.
Dense catches semantic matches.
Merge results intelligently.

𝗧𝗵𝗲 𝗺𝗮𝗴𝗶𝗰 𝗰𝗼𝗺𝗯𝗼:
↳ Sparse catches exact matches
↳ Dense catches semantic matches
↳ Weighted fusion of both scores

𝗥𝗲𝘀𝘂𝗹𝘁: Better than either alone

8️⃣ 𝗥𝗲𝗰𝗶𝗽𝗿𝗼𝗰𝗮𝗹 𝗥𝗮𝗻𝗸 𝗙𝘂𝘀𝗶𝗼𝗻 (𝗥𝗥𝗙)
Algorithm to merge multiple rankings

2 ranked lists. How to combine fairly?

RRF gives higher scores to docs
that rank high in both lists.

𝗨𝘀𝗲 𝗰𝗮𝘀𝗲:
Combining sparse + dense results into
one unified ranking

𝗙𝗼𝗿𝗺𝘂𝗹𝗮: Simple rank-based fusion
(no score normalization needed)

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Most people obsess over OpenAI, Anthropic.

Meanwhile, NVIDIA's resources are quietly dominating
both the hardware AND software sides of AI.

Here's what you're NVIDIA has done till now:

𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗕𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝘀
↳ DGX Spark: Run 100B param models on your desk
↳ DGX Station: 1T param models with 775GB memory
↳ Grace Blackwell: Petaflop-level AI performance locally

𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀
↳ NVFP4 format: 70% model compression, zero accuracy loss
↳ llama.cpp integration: 35% performance boost
↳ 8x faster than MacBook Pro M4 Max for video gen

𝗢𝗽𝗲𝗻 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺
↳ Free playbooks for RAG, robotics, genomics
↳ Pre-configured CUDA-X libraries
↳ vLLM, SGLang, llama.cpp ready out-of-box

The amazing truth?
You can now run frontier AI models
on a deskside system that fits under your desk.

No cloud. 
No data center. 
No monthly bills.

Complete Sovereign! 
That's the future of AI!
Just local inference with production-grade performance.
Still not convinced?

Check out the free notebooks and setup guides:
https://lnkd.in/dViv8nUE

This is what AI infrastructure looks like when
hardware and software teams actually collaborate.
What's your take on local vs cloud AI workloads?

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This is unlocking people like me in another way.

I just built a complete new Jam With AI website in 20 minutes.

Walk with me and understand how the AI is evolving.
I used completely different tool: Atoms

As most AI tools: Pretty UI, no backend
Atoms: Frontend + Backend + Supabase + Stripe

𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝘁:
Website for JAM with AI:
→ Dark/light mode toggle
→ User authentication
→ Database (Supabase)
→ Payment processing (Stripe)

All from THREE prompts.

One thing I loved is the Race mode (check the video)
Instead of one AI/LLM building your app...
Atoms runs multiple AI teams in parallel.
→ Claude Sonnet
→ DeepSeek
→ Gemini Pro
→ Gemini Flash

They compete.
You pick the winner.
It settles the doubt in 20 mins!

The biggest win is 𝗧𝗵𝗲 𝗦𝘁𝗿𝗶𝗽𝗲 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻:

Most AI tools stop at the frontend:
"Here's your pretty website!"

But you can't make money with just UI.

Atoms Backend gives you:
→ Database for user data
→ Authentication for accounts
→ Stripe payments to accept money

𝗧𝗵𝗶𝘀 𝗶𝘀𝗻'𝘁 𝗮 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹.
𝗜𝘁'𝘀 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘁𝗲𝗮𝗺.

Try it: https://atoms.dev/

Who is building their website with me?
What would be your inspiration to build?

#VibeBusinessEra #BuiltwithAtoms

As a Data Scientist I used to obsess over models.

But the first step to production that matters? Containerization.

In the last 7 years, every single AI/ML project my team and I shipped to production had one thing common. 
➡️ It was Docker. 
Every. Single. Time.

From simple pipelines to complex AI agents, containerization made it possible to ship with confidence.

And it’s not going away anytime soon.
So if you’re a data scientist, ML engineer, or just getting started - I highly recommend learning Docker.

Here are 𝟯 𝗙𝗥𝗘𝗘 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 I recommend (weekend-friendly):
✅ Docker Containers for Beginners - https://lnkd.in/dyVmTsZa
✅ Docker Fundamentals - Cantrill - https://lnkd.in/dvGMpr3w
✅ YouTube Docker Course - https://lnkd.in/dnKai3jV

Master this, and you’ll be ahead of 80% of AI practitioners struggling with deploys 🚀

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The Battle

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Shirin Jam

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Performance ScoreAngeley Mullins's LinkedIn Performance

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The Battle

Angeley Mullins

Shirin Jam

Followers

Posts

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Angeley Mullins's LinkedIn Performance

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