I build AI products from zero to one, and I teach others how to do the same.Over 12+ years, I've taken AI/ML products from proof-of-concept to launch at Amazon, Nike, T-Mobile, and Disney. The work I love most is early-stage: validating ideas, getting executive greenlight, and shipping MVPs that prove value before scaling. At Nike, that approach drove a $100M annual reduction in product returns.I also teach what I build. I co-created and lead the AI Product Management Certificate at the University of Washington, where experienced PMs ship real GenAI products complete with evaluation plans and GTM strategies. Previously, I trained 4 cohorts at Product School.What I bring: the ability to translate messy AI capabilities into clear product value, align technical and business stakeholders, and move fast without breaking trust. I write about AI PM craft at aipmguru.substack.com.I'm currently exploring opportunities to lead 0→1 AI product work at companies solving meaningful problems. I'm especially energized by AI in healthcare and education, though I'm open to any domain where the product can genuinely improve lives. 

Shaili Guru

This message made my morning ☀️

A reader just shared that they landed an AI Product Manager role, and mentioned my content helped along the way.

Here's the thing though: I'm glad the content helped, but they put in the work.

The learning. The interviewing. The career pivot. That's all them.

Moments like this remind me why I write. Not to have all the answers, but to make the path a little clearer for folks navigating the transition.

If you're in the middle of your own AI PM journey right now, keep going. It's working.

AI Product Leader | 0→1 Builder & Educator | Ex-Nike, Ex-Amazon, Ex-Disney

My kids got Harry Potter Guess Who for Christmas, and watching them play gave me an idea. What if I built a digital version with our 46 family members? They could learn their relatives the same way they're learning about Hogwarts. I opened Bolt and started building. 34 versions later, I had a working game. But the real lesson wasn't about AI coding tools.

It was seeing PM fundamentals play out in real-time, compressed into one afternoon. Clear requirements prevented wrong interpretations. Defining the data model upfront avoided rewrites to the architecture. Thinking through edge cases early meant fewer fixes later. AI doesn't ask clarifying questions or push back on ambiguity. It just builds exactly what you describe.

Vibe coding is a mirror for PM discipline. It validated what I've learned across teams: clear requirements, defined data models, explicit acceptance criteria. The 4 patterns that made this work 👇

If I were teaching someone about AI product lifecycles for the first time, I'd start with POCs.
Not because they're the first step in a framework diagram.

Because they're where most AI products quietly die.

It reminds me of my research days working with Zebrafish embryos. You don't scale up a staining protocol before you've proven it works on a handful of samples.

Same logic applies to AI.

But here's what most teams get wrong: they run AI POCs like traditional software spikes. Build it, demo it, ship it.

AI doesn't work that way.

Your POC might hit 85% accuracy. Looks great in a demo. But what happens to the 15% that fails? Who handles those cases? What's the cost of being wrong?

These are the questions traditional PMs aren't trained to ask.

In my latest post, I break down:
→ Why POCs are the critical gate (not just another phase)
→ Three types of AI products and how they change what you're validating
→ The question most teams forget to ask before calling a POC "successful."

Link in comments 👇

Building your personal brand is hard. On LinkedIn, off LinkedIn—doesn't matter. It's just hard.

I have been at it for about a year now, and honestly? Still figuring it out.

I'm part of PDMA Seattle's committee, and our next session is with Leslie on how to make LinkedIn work for your PM career.

📅 Jan 14, 12-1 PM PST 🔗 https://luma.com/wno7fylk

Our last session on PM hiring trends had a great turnout, and everyone walked away with something useful. Expecting this one to be just as good.

Come through if you're trying to figure out the brand thing too.

Product Development and Management Association Seattle team - Anna Bigornia Sako Eaton Harmony Crawford Prabina B. Matt Foster Sahit G.  Banu R.

#productmanagement #linkedin #personalbrand

Open article: Stand Out With Your Brand Out. How Product Managers can grow their career using LinkedIn. · Zoom · Luma by luma.com

Stand Out With Your Brand Out. How Product Managers can grow their career using LinkedIn. · Zoom · Luma

Applied AI | Building with LLMs, RAG & LangChain | From Data Analytics to AI Architecture | Currently working on real-world GenAI use cases. Let’s connect if you're building AI products, LLM infra, or tools for devs.

I help businesses turn Al into real, measurable outcomes without unnecessary complexity. From solution strategies that scale to implementing practical Al solutions, my goal is to make Al actionable, human-centred and impactful.

With over 8+ years of experience in data analytics, AI product strategy, AI Automation, and customer success, I've helped companies across healthcare, SaaS, and enterprise optimise AI adoption, enhance decision-making, and deliver business results.

I specialise in:

➡️Al Product Strategy & Roadmaps: Designing and executing scalable Al solutions
➡️Customer Success Architecture: Enabling enterprise adoption and driving business impact
➡️Generative Al & Al Ops: Optimising Al systems for reliability and performance
➡️Data-Driven Insights: SQL, Python, cloud technologies (IBM, GCP, AWS) to build actionable solutions
➡️Bridging Tech & Business: Translating technical Al into measurable outcomes

Few Highlights:

➡️Led anomaly detection project with AI/ML integration at DecisionHUB
➡️Built automated SQL + Python pipelines for Al-driven insights
➡️Developed customer engagement dashboards that influenced product roadmaps
➡️Designed a Therapeutic Al voice agent

I also host the Lattency and Latte podcast, inviting guests to discuss Al, technology, and emerging trends, making complex topics practical and human-centred.

Renuka M.

We are hiring! Growing the team in stealth mode. 

From recent applicants, I’m reaching out to move forward with the next steps. 
Thanks for your patience!

We are looking for three roles and need the right people to help us build what we are working on.

The three roles are:

1. AI/ML Engineer

→ Build and deploy machine learning models in production
→ Work with LLMs, fine-tuning, and real-world AI systems
→ Ship features that users interact with

2. Software Engineer

→ Build scalable backend systems
→ Design APIs and infrastructure
→ Keep things running as we grow

3. Product Manager

→ Define what we build next
→ Talk to users and translate needs into product roadmaps
→ Work with engineers to ship AI features that solve real problems

If you specialize in:

→ Software Engineer - Machine Learning
→ Software Engineer - Infrastructure

We are also opening spots for an AI/ML intern and a few more intern roles we are finalizing soon.

We would love to talk too.

What we are looking for:

→ People who ship fast
→ Comfortable with ambiguity
→ Learn by doing, not just planning
→ Actually curious about building with AI

If this sounds like something you'd like to be part of or know someone who might fit, drop a comment below or send a quick note to get in touch!

Renuka Mujumdar

Helping businesses to leverage Al for real outcomes without making it complex | AI Product Strategist | Solution Strategist | Podcast @ Lattency and Latte | AI Automation

🚀 Building a Community for Data & AI Professionals (Limited Spots)

Over the past few weeks, I have had numerous conversations with data scientists, ML engineers, and AI builders at various stages of their careers.

One pattern keeps emerging: people want genuine opportunities and a space for genuine learning, without the distractions.

So I'm building something focused around that.

Why I'm starting this community: Primarily to connect talented people with real opportunities, starting with roles on my team and expanding to vetted positions across my network.

What you'll get:
→ Exclusive hiring updates – Be first to know about open roles (my team + network) 
→ Real opportunities, real feedback – Share your work, get noticed by hiring teams 
→ Curated resources – No fluff, just what actually matters for your growth 
→ Expert sessions – Live conversations with people who are hiring and building → Ask questions that matter – Get answers from practitioners doing the work

👉 Interested? Comment "INTERESTED" or DM me to get early access.
(Hiring updates drop first to community members)

〰️〰️〰️
Follow (Renuka) for practical Data and AI insights. Subscribe to my podcast Latency and Latte for in-depth technical conversations: https://lnkd.in/gvjuJuGp

📊 78% of AI initiatives fail to move from pilot to production due to strategy-execution gaps

I found the definitive guide to fix this + actionable frameworks you can implement immediately

📖 "The AI Optimization Playbook" by  Chun Schiros, PhD, Supreet Kaur, Rajdeep Arora, and Usha Jagannathan, PhD

The complete roadmap for delivering measurable business value through strategic AI implementation

What you'll learn:

1️⃣ 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆
- Business-aligned AI roadmaps
- Executive sponsorship frameworks
- ROI-driven project prioritization
- Moving from innovation to impact

2️⃣ 𝗠𝗟𝗢𝗽𝘀 & 𝗟𝗟𝗠𝗢𝗽𝘀 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻
- Prototype-to-production pipelines
- Scalable ML system architecture
- Model lifecycle management
- Enterprise-grade deployment practices

3️⃣ 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 & 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀
- GenAI operations frameworks
- Autonomous decision-making systems
- Agent deployment at scale
- Real-world enterprise applications

4️⃣ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀
- Explainability and transparency
- Fairness metrics and compliance
- Trustworthy LLM implementation
- Ethical AI governance

5️⃣ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 & 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
- Beyond accuracy: adoption metrics
- Experimentation and causal inference
- Performance monitoring systems
- Continuous improvement frameworks

6️⃣ 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲
- Evolving AI regulations
- Legal framework navigation
- Risk mitigation strategies
- Future-ready compliance practices

✨ Bonus:
→ Real-world case studies across industries
→ ROI evaluation frameworks
→ DRM-free PDF version included
→ Complete implementation roadmaps

Perfect for: CTOs, CIOs, CDAOs, CAIOs, and AI/ML leaders driving enterprise transformation

👉 Get the book: https://lnkd.in/gmD5tX9G

⚡️━━━━━⚡️
🔄 Found this useful? Repost and share it with your network.
🎯 Follow me for practical Data & AI insights.
🎧 For deeper dives, listen to my podcast Latency and Latte: https://lnkd.in/gvjuJuGp

Understanding How Large Language Models Actually Work

Ever wondered what happens inside an LLM when you type a prompt? Let me break down the journey your text takes through these powerful systems.

✨ Tokenization: 
Breaking It Down. Your input gets split into smaller pieces, tokens. These could be full words, parts of words, or even individual characters. Think of it as creating a dictionary that the model can understand, with each token assigned a unique ID.

✨ Embedding: 
Adding Meaning Those token IDs now get transformed into mathematical vectors, essentially, numbers that capture meaning. Words with similar meanings end up close together in this numerical space. Position information is also incorporated here, so the model understands that word order matters.

✨ Attention Mechanism: 
The Magic Ingredient. This is where things get interesting. The model figures out which words should "pay attention" to which other words. It does this multiple times in parallel (multi-head attention), capturing different types of relationships simultaneously. Each word can look at every other word to understand context.

✨ Feed-Forward Networks: 
Deep Processing. Each token's representation gets processed independently through neural network layers. Non-linear functions help the model learn complex patterns that go beyond simple relationships.

✨ Normalization: 
Keeping Things Stable Layer normalization keeps values in check, while skip connections let information flow smoothly through the network. Dropout during training prevents the model from becoming too dependent on specific patterns.

✨ Prediction: 
Generating Output. Finally, the model converts all this processing into probabilities for what token should come next. Temperature settings control how creative or deterministic the output will be.

The key difference? 
LLMs generate text one token at a time, constantly feeding their own output back as input; that's what makes them "autoregressive."

Question for you: Which part of this architecture do you think contributes most to hallucinations in AI responses?

Based on insights shared by Manthan Patel 

Follow Renuka for more such updates on AI and technology.

If you’re planning for 2026 and want to get serious about Generative AI, this might help.

Across students, early professionals, and experienced engineers, the pattern I keep seeing is the same: plenty of interest, plenty of noise, and very little clarity on what to learn first.

This learning path cuts through that. It’s structured, practical, and meant to be taken one step at a time.

Go slow, build alongside it, and let the concepts settle before moving on.

This is a solid, well-structured learning path 👇

Take it step by step and focus on understanding, not speed.

1️⃣ Generative AI for Everyone
https://lnkd.in/gmqrkxbV

2️⃣ Introduction to Generative AI
https://lnkd.in/gkiYM29e

3️⃣ Introduction to Large Language Models
https://lnkd.in/g2KrkBkJ

4️⃣ How Transformer LLMs Work
https://lnkd.in/g_zC9pms

5️⃣ ChatGPT Prompt Engineering for Developers
https://lnkd.in/gHgaArth

6️⃣ LLM Embeddings – Visual Primer
https://lnkd.in/gxWRv_3z

7️⃣ Retrieval Augmented Generation (RAG)
https://lnkd.in/gDUCrECi

8️⃣ Fine-tuning Large Language Models
https://lnkd.in/gT3RDjuR

9️⃣ LLM Inference Optimization (cost, scale, latency)
https://lnkd.in/gU2dyN5k

🔟 Introduction to Responsible AI
https://lnkd.in/gT6twDH3

One practical suggestion:

Take it slow. Focus on one area, build alongside it, and move on only when it makes sense. That’s what leads to real learning

Credit to Aishwarya Srinivasan for curating this learning path.

Follow me (Renuka) for more AI insight and subscribe to my Podcast (link in the comments) to find more in-depth tech conversations.

𝟐𝟎𝟐𝟔 𝐡𝐚𝐬 𝐤𝐢𝐜𝐤𝐞𝐝 𝐨𝐟𝐟 𝐰𝐢𝐭𝐡 𝐢𝐧𝐬𝐩𝐢𝐫𝐢𝐧𝐠 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐭𝐡𝐞 𝐠𝐥𝐨𝐛𝐞, 𝐚𝐧𝐝 𝐈 ha𝐯𝐞 𝐛𝐞𝐞𝐧 𝐡𝐨𝐧𝐨𝐫𝐞𝐝 𝐭𝐨 𝐛𝐞 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐭𝐡𝐞𝐦. 

The organizations winning with AI are not the ones automating fastest, they are the ones amplifying smartest.

Appreciate Bayo George and The TechHub Centric the CareerByteCode  team for the invitation and for hosting such a meaningful discussion. 

This morning's LinkedIn Live with TechHub Centric on "The GenAI Roadmap: How Forward-Looking Organizations Use LLMs for Viable Growth" was thoughtful, engaging, and energizing.

𝐓𝐡𝐞 𝐜𝐨𝐫𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭: 𝐀𝐈 𝐢𝐬𝐧'𝐭 𝐡𝐞𝐫𝐞 𝐭𝐨 𝐫𝐞𝐩𝐥𝐚𝐜𝐞 𝐩𝐞𝐨𝐩𝐥𝐞. 𝐈𝐭'𝐬 𝐡𝐞𝐫𝐞 𝐭𝐨 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧 𝐚𝐧𝐝 𝐚𝐦𝐩𝐥𝐢𝐟𝐲 𝐰𝐡𝐚𝐭 𝐰𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐝𝐨 𝐛𝐞𝐬𝐭.

Here's what we explored:
💡 Transformation Over Automation
Using large language models and generative AI to reimagine core business models and workflows not just speed up existing tasks. This is about enterprise AI implementation that drives real digital transformation.

🤝 Human + AI Amplification
Moving past replacement anxiety to focus on human-AI collaboration, how artificial intelligence enhance human judgment, creativity, and strategic thinking in the future of work.

🛡️ Responsible AI & Regulatory Frameworks
A deep dive into NDPR and GDPR compliance, data privacy, ethical AI deployment, and why responsible AI adoption matters as organizations scale AI strategy globally. 

The African tech community's energy was inspiring. Let's keep building responsibly. 🚀

Interested in the session slides? Drop a comment below and I'll share the Gamma link.

Data & AI Community Update: AMAs, Webinars & Meetups Coming Soon

Quick update on what's launching for data professionals and AI practitioners:

Coming soon:
* AMA sessions with industry leaders
* Members-only technical webinars on ML, data engineering & AI
* In-person meetups for local networking

Early access includes:
* Curated data science and AI job opportunities
* Live learning events 
* Priority invites to all community events

I'm onboarding intentionally to keep quality high. Join the waitlist here:  https://lnkd.in/gTFNi7JD

Original post below 👇

Why qualified data professionals get rejected (and it has nothing to do with skills) 🚫

I just finished my 3rd resume review call this week, and I'm honestly frustrated 😤

Not at the candidates, but at how broken the ATS system really is for data and AI roles.

Yesterday, I reviewed a resume from a Data Scientist with:
→ 2 years of Python and SQL experience
→ Built an end-to-end ML pipeline that reduced processing time by 40%
→ Created Tableau dashboards used by 50+ team members daily
→ Strong experience in Pandas, ETL, and cloud computing

Their resume? Rejected by 30+ companies, including Amazon, Google, and top tech startups.

Zero interviews. Not even a screening call.

The problem wasn't their technical skills. It was how they presented them.

Here's what we fixed in our 45-minute resume review session:

✅ Restructured for ATS compatibility (90% of data analyst resumes never reach human recruiters)
✅ Added industry-specific keywords: ETL pipelines, data modeling, ML deployment, Python automation
✅ Quantified every project with measurable business impact
✅ Removed outdated elements: DOB, photos, generic objective statements
✅ Condensed to ONE page with clean, recruiter-friendly formatting

The result? Interview callbacks started within 10 days.

This is exactly why I built our Data & AI mentoring community.

After conducting 100+ resume reviews for data analysts, data scientists, ML engineers, and AI professionals, I've identified the patterns that kill applications:

❌ Listing Python, SQL, ML skills they can't defend in technical interviews
❌ Multi-page resumes for entry-level data roles (instant ATS rejection)
❌ Missing critical keywords recruiters search for: data pipelines, A/B testing, predictive modeling
❌ Impressive projects buried under poor formatting and vague descriptions

What actually gets you past ATS and into interviews:

🔹 Technical skills aligned with job descriptions: Python, SQL, Tableau, Power BI, Spark, AWS, Azure
🔹 Project descriptions using the CAR method: Context → Action → Result with metrics
🔹 Strategic keyword optimization for data roles without stuffing
🔹 Clean, single-page format that ATS systems can parse and recruiters can scan in 6 seconds

If you're a data professional applying to 50+ jobs without responses, your resume is probably the bottleneck, not your Python skills or project experience.

Comment "TEMPLATE" below, and I'll share the exact ATS-friendly resume format. 

Your data career breakthrough might be just one resume revision away 🚀


⚡️━━━━━⚡️
🔄 Found this useful? Repost and share it with your network.
🎯 Follow me for practical Data & AI insights.
🎧 For deeper dives, listen to my podcast Latency and Latte: https://lnkd.in/gvjuJuGp

𝐈 𝐡𝐚𝐯𝐞 𝐛𝐞𝐞𝐧 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐭𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 𝐥𝐚𝐭𝐞𝐥𝐲, 𝐚𝐧𝐝 𝐈 𝐤𝐞𝐞𝐩 𝐬𝐞𝐞𝐢𝐧𝐠 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐩𝐨𝐩 𝐮𝐩: 𝐒𝐡𝐨𝐮𝐥𝐝 𝐈 𝐥𝐞𝐚𝐫𝐧 𝐒𝐐𝐋 𝐨𝐫 𝐏𝐚𝐧𝐝𝐚𝐬 𝐟𝐢𝐫𝐬𝐭?

Wrong question.

Here's what I wish someone told me when I started in analytics:

Your data source decides your first move.

I spent my first month trying to use Pandas for everything. Loading massive tables from our database into memory, waiting 10 minutes for a simple aggregation, crashing my laptop twice.

Then a senior analyst showed me one SQL query that did the same thing in 8 seconds.

That's when it clicked.

Here's what actually matters:

The data doesn't care about your tool preference. If it's in a database with proper indexes, SQL will outperform Pandas every time on large operations. If it's a messy CSV that needs custom cleaning logic, Pandas gives you the flexibility SQL can't match.

My actual workflow now:

I start every project asking: Where does this data live, and what do I need to do with it?

Database + aggregations/filters/joins → SQL does the heavy lifting 
Local files + exploration + weird transformations → Pandas handles it Production pipeline → SQL extracts, Pandas cleans and enriches

The turning point for me was realizing both tools solve different problems.

Stop learning tools in isolation. Start learning which problems they're built to solve.

Thanks to Sai for posting about this topic, got me reflecting on my own learning journey.

What's one thing you wish you knew earlier about working with data?

♻️ Repost if this resonates with your experience

Follow Renuka for more such updates.

#DataAnalysis #SQL #Python #DataScience #TechHubCentric #TechHubCentricAcademy

MLOps in 2026: The Career Everyone's Talking About 🚀

You trained an ML model. It hit 95% accuracy. Your team celebrated. 🎉
Then someone asked: "When can we deploy this to production?"

Welcome to MLOps. The bridge between "it works on my laptop" and "it works for a million users."

🔥 WHY MLOPS IS EXPLODING
Think of it like baking cookies at home. MLOps is figuring out how to:

→ Bake 10,000 cookies daily (scaling)
→ Keep every cookie tasting the same (consistency)
→ Know instantly when the oven breaks (monitoring)
→ Update recipes without chaos (deployment)

The numbers: MLOps market growing from $4.38B (2026) to $89B (2035), that's 40% yearly growth.

💰 WHAT THIS MEANS FOR YOU

→ 41% of U.S. tech jobs now require AI skills
→ Senior MLOps engineers are the hardest roles to fill
→ Workers with AI skills earn significantly more

Translation: If you know MLOps, you're a unicorn 🦄

🛠️ WHAT MLOPS ENGINEERS DO

Three jobs in one:
1. The Plumber 🔧 Build automated data pipelines
2. The Guardian 🛡️ Monitor models 24/7
3. The Translator 🌉 Bridge data scientists and engineers

📚 HOW TO LEARN (FREE RESOURCES)

MLOps Zoomcamp by DataTalksClub.Club
https://lnkd.in/g7SejEDK

Made With ML by Goku Mohandas
Project-based, comprehensive

Chip Huyen's MLOps Blog
huyenchip.com/mlops

Skills needed: Python + AWS/Azure + TensorFlow/PyTorch + Docker/Kubernetes

⚡ YOUR 30-DAY JUMPSTART
Week 1: Complete one course module
Week 2: Build your first ML pipeline
Week 3: Deploy a model to the cloud
Week 4: Document on GitHub

Remember: MLOps isn't about being a genius. It's about being organized, systematic, and curious.

💯 THE REALITY CHECK

45% of ML projects fail to reach production due to poor monitoring.
Companies need people who can ship models, not just build them.

That's where YOU come in.

Drop a comment:

👍 Already in MLOps
🚀 Planning to learn
❓ Have questions
Let's navigate 2026's hottest tech career together.

〰️〰️〰️
 If this resonated, repost it for others. Follow (Renuka) for practical Data and AI content. For deeper dives, listen to Latency and Latte: https://lnkd.in/gvjuJuGp

🚀 Early Access: AI Interview Accelerator

I’m partnering with a senior AI leader (ex-Meta/Google) who’s helped hire 1,000+ Data Scientists & ML Engineers.

We’re opening limited early access to an AI interview simulator built around real company interview processes.

What you get:
✅ Company-specific interview simulations
✅ Level-aligned questions
✅ Safe space to identify gaps before real interviews
✅ Chance to shape the product
✅ Free access before it becomes paid

Opening this first to a small group of:
• Data Scientists
• ML Engineers
• Candidates interviewing in the next 30 days

I’m also building a focused Data & AI community, and a few slots are still open. Form link is in the comments if you’d like to join.

💬 Comment INTERESTED or DM me for access.

⚡️━━━━━⚡️
🔄 Found this useful? Repost and share it with your network.
🎯 Follow me for practical Data & AI insights.
🎧 For deeper dives, listen to my podcast Latency and Latte: https://lnkd.in/gvjuJuGp

The Data Scientist role is changing fast.

The bottleneck is no longer writing code. It's how quickly teams can turn ideas into usable systems.

This morning, I stress-tested a workflow for rapid AI adoption. What usually takes teams ~2 weeks for a simple dashboard took ~20 minutes using a low-code, high-intelligence setup:

– Cursor (Composer mode) to scaffold the Python backend and RAG logic
– Lovable to turn that logic into a clean, usable UI almost instantly

The real shift isn't tooling. It's a mindset.

For leaders in Data & AI, the question is no longer "How many engineers do we need?" 

It's "How much insight can one engineer realistically ship?"

We're moving from builders to orchestrators.

⚡️━━━━━⚡️
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Why Your RAG Pipeline Needs Better Error Handling 🔧

Building a RAG system that works in demos is easy. Building one that survives production? That's where most teams struggle.

The Problem
Your pipeline will fail. APIs timeout, documents get corrupted, embedding models hit rate limits, and vector databases hiccup. Without proper handling, one bad document can crash your entire ingestion pipeline.

Three Things That Made a Difference:
𝟏. 𝐒𝐦𝐚𝐫𝐭 𝐑𝐞𝐭𝐫𝐢𝐞𝐬: Implement exponential backoff for transient failures. Use circuit breakers when downstream services are down. Send permanently failed items to a dead letter queue instead of blocking everything else.

𝟐. 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 Ditch linear scripts. Use tools like Airflow, Prefect, or Temporal to manage complex workflows. They handle state management, retries, and monitoring out of the box.

𝟑. 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐥𝐞 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 You can't fix what you can't see. Track:
➡️ Error rates by failure type
➡️ Processing latency and throughput
➡️ Queue depths and backpressure signals
➡️ Document processing success rates

Quick Wins:

➡️ Make operations idempotent using content hashes
➡️ Validate inputs before expensive embedding calls
➡️ Cache embeddings to avoid reprocessing
➡️ Set up alerts before things completely break

The difference between a fragile prototype and a production system is how gracefully it handles failures.

What's been your biggest RAG deployment challenge?

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Why Your AI Search System (RAG) Fails Before It Even Answers

Most AI-powered document search systems (RAG) don't break at the answer stage.
They break way earlier, and you're fixing the wrong part.

RAG = Retrieval-Augmented Generation 
(Think ChatGPT searching your company docs, then answering questions)

It has 3 steps. Each breaks differently:

🔴 STEP 1: Processing Documents (INGESTION)

What breaks: 
 → PDFs lose tables and formatting 
 → Documents split mid-sentence
 → Critical context disappears (dates, sources, sections)

Example: AI says "Revenue is $10M" but misses it's from a 2019 report, not 2024.
The damage is permanent. There are no quick fixes. Documents destroyed here.

🔴 STEP 2: Finding Information (RETRIEVAL)

What breaks: 
→ System retrieves 20 chunks—18 are noise 
→ Vector search finds "similar" content, not correct content 
→ AI confidently answers based on wrong information

Example: "What's our refund policy for damaged items?" 
→ System grabs general returns, warranties, shipping info 
→ AI mixes everything into a confident wrong answer

Fix: Hybrid search (semantic + keyword) + metadata filtering + reranking
Not just "find similar", actually find relevant.

🔴 STEP 3: Generating Answers (GENERATION)

If steps 1-2 work, this is easy.

What breaks: 
→ You can't prompt-engineer around bad retrieval 
→ AI can only work with what it receives

✓ Force source citations 
✓ Order context by relevance 
✓ Accept that broken retrieval = broken answers

How to Fix It:

Stop tuning prompts first.

1️⃣ Check what the system retrieved 
→ Could a human answer correctly from these chunks? 
→ If no, retrieval is broken

2️⃣ Audit document quality 
→ Are chunks coherent? 
→ Is context preserved?

3️⃣ Measure retrieval accuracy 
→ For test questions, what % of results are actually relevant? 
→ Under 70%? Fix retrieval first

✅ Systems that work: 
→ Clean document processing 
→ Hybrid search + reranking 
→ Simple prompts

❌ Systems that fail: 
→ Obsessing over AI models 
→ Ignoring data quality 
→ Prompt engineering to fix retrieval

Building RAG in production?

Fix ingestion → Then retrieval → Generation is the easy part.

Most bugs never reach the AI; they are in your data pipeline.

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How Retrieval Failures Cause AI "Hallucinations" and What to Do About It

The secret about RAG systems? They fail more often than you think.

While Retrieval-Augmented Generation has become the go-to solution for grounding AI responses in real data, retrieval failures remain a major source of hallucinations in production systems.

The 5 Critical Failure Modes

Recent research identifies five patterns that break RAG systems:

𝟏. 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐂𝐨𝐧𝐭𝐞𝐧𝐭: The answer isn't in your knowledge base, so the system fabricates one instead of saying "I don't know."

𝟐. 𝐋𝐨𝐰-𝐑𝐚𝐧𝐤𝐞𝐝 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞: The right document exists but ranks too low (position 15 when you only check top 10)

𝟑. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐍𝐨𝐢𝐬𝐞: Contradictory sources confuse the model. Old policies mix with new ones, creating dangerous inconsistencies.

𝟒. 𝐐𝐮𝐞𝐫𝐲 𝐀𝐦𝐛𝐢𝐠𝐮𝐢𝐭𝐲: "Renewal policy" could mean contracts, insurance, or software licenses. Wrong interpretation = wrong retrieval.

𝟓. 𝐅𝐫𝐚𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: Critical information splits across chunks. A "$10M threshold" loses its qualifying conditions during chunking.

What Actually Works

✅ Clean your data first: No pipeline fixes garbage inputs
✅ Add confidence thresholds: Return "I don't know" when retrieval confidence is low
✅ Use hybrid retrieval: Combine semantic search with metadata filtering
✅ Verify claims: Check each generated statement against retrieved evidence

The Bottom Line
A 2025 review in the Mathematics journal confirms that retrieval failures stem from four core issues: data source quality, query processing, retriever limitations, and retrieval strategy problems.

The fix isn't chasing zero hallucinations; it's managing uncertainty systematically. 
Fix your retrieval pipeline, verify outputs, and design for transparency.

Citations:

📊 Zhang, L., et al. (2025). "Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review." Mathematics, 13(5), 856.
📊 Barnett, S., et al. (2024). "Seven Failure Points When Engineering a Retrieval Augmented Generation System."

What retrieval failures have you seen in production? 👇

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Follow (Renuka) for practical Data and AI insights. Subscribe to my podcast Latency and Latte for in-depth technical conversations: https://lnkd.in/gvjuJuGp

Quick update on the Data & AI community.

The response has been much bigger than I expected. Thank you for your interest.

I've already shared the community link with many of you who reached out. To keep this space intentional and useful, I'm onboarding in a structured way instead of sharing the link publicly.

If you're interested in:
- meetups and small group sessions
- peer support and practical conversations around Data & AI
- hiring updates (my team + trusted network)

Please fill out this short form with your email. I'll share an invite privately in batches.

Google form link: https://lnkd.in/gTFNi7JD

This helps keep the community focused and respectful of everyone's time.

Appreciate the patience and interest.

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Your ML model works on your laptop. Now what? 🤔

11 resources to learn deployment, monitoring, and scaling.

📚 MLOps Learning & Practical Resources

1️⃣ Google Cloud MLOps Whitepaper
https://lnkd.in/guVhxfHe

2️⃣ Made With ML (hands-on production ML)
https://madewithml.com

3️⃣ Full Stack Deep Learning (industry course)
https://lnkd.in/geRvwgbY

4️⃣ Chip Huyen — ML Systems Blog
https://lnkd.in/gM52beee

5️⃣ MLflow Documentation (experiment tracking & lifecycle)
https://lnkd.in/ga9bZYS5

6️⃣ Kubeflow Documentation (ML pipelines on Kubernetes)
https://lnkd.in/grgSZr-g

7️⃣ AWS SageMaker MLOps Guide
https://lnkd.in/gYCCwCGn

8️⃣ Azure MLOps Documentation
https://lnkd.in/gFGQfuZ7

9️⃣ DataTalksClub MLOps Zoomcamp (Free & practical)
https://lnkd.in/g7SejEDK

🔟 Kubernetes Basics (infra knowledge matters)
https://lnkd.in/gsia2NYJ

1️⃣1️⃣ Machine Learning Engineering for Production (DeepLearning.AI)
https://lnkd.in/gw4E5auc

Start with one. Build something. Ship it.

Which one are you trying first? 👇

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