Dare to Data

I founded Dare to Data to formalize and structure the relationships I have with some of the world's leading companies to consult, support and empower them in their data and AI journey.

Current and past clients include PepsiCo, PUMA, Whirlpool, Nitto, Amazon Web Services, AtScale, EM360, O'Reilly Media, nexxworks, Aporia, Colquímica, DATAcated Plus, The Ravit Show, Ontotext, Growth Tribe, IBM / Databand, CoRise, SingleStore, Equals, Einblick, Impetus, Nexla, Starburst, ClearML, Nvidia, Dubai Future Foundation, ThriveDX, Global DIY-Summit, Afera, Prophecy, Listly, Dremio, amongst others.

Founder

LinkedIn

Over 65,000 learners to date! Building and teaching content as a part of the data course library, currently focused on empowering others to take full control of their data through the use of SQL as well as how to tap into the power of analytical thinking.

LinkedIn Learning Instructor

IE Business School

Delivering keynotes for the US, Spain, Canada, Portugal, Russia, Armenia, Georgia, Turkey, Azerbaijan, Jordan, Belgium, Luxembourg, Norway, Switzerland, Chile, Greece, Japan, South Korea, Nigeria, Egypt, Lebanon, China, Indonesia, Saudi Arabia, France, Cyprus & more.

International Speaker

Graduate-level courses taught primarily within the MBA programs covering data and artificial intelligence topics, as well as personal branding workshops.

Adjunct Professor, Department of Information Systems and Technology

Leading the Corporate Masters program for Saudi Aramco employees.

Academic Director, Saudi Aramco Master in Business Analytics and Big Data

Leading the online Executive program which explores the range of possibilities offered by using new technologies and aligning digital strategy with general strategy in a company.

Academic Director, Digital Strategy and New Technologies Executive Program

Porto Business School

Graduate and executive level courses taught in business strategy for data and artificial intelligence.

Adjunct Professor

ISDI

Launched, continue to coordinate and teach the Data & AI module within one of the flagship programs at ISDI - the Master in Internet Business in Madrid, Barcelona and online.

Data & AI Module Program Manager

Regularly lecturing in advanced analytics topics at the Masters level and Executive programs in ISDI, an international institution focused on digital transformation.

Lecturer

VN Campaign Partners

Partner

Google

Developing insights and tools to empower our teams to work smarter and our clients to get the most out of Waze Ads. Leading analytical studies and strategy across North America for some of our largest advertising verticals (Fuel, Auto Parts & Services) as well as non-endemic verticals such as Entertainment. Creating scalable data solutions via daily use of SQL, BigQuery, Looker, CARTO, GSheets.

Analytical Lead, Waze

As a part-time facilitator, teaching engineers & analysts how to apply statistical methods to big data problems (Hypothesis Testing, T-Tests, Multiple Linear Regression, Logistic Regression, Centered Moving Average, Causal Impact, K-Means Clustering).

Master Faculty - Data Science I

Leading complex data projects, automation and optimization with our most advanced international clients cross-office.

Data Specialist

#IamRemarkable is an initiative empowering women & underrepresented groups to speak openly about their accomplishments in the workplace & beyond, thereby breaking modesty norms & glass ceilings. Workshops hosted for the UN World Tourism Organization, Amadeus, CARTO, IE Business School, LastMinute, Teleperformance, Google Campus - Digitalizadas.

#IamRemarkable Facilitator

Combining Google Search data with advanced analytics and Cloud tools to drive business impact. Working with a $xxx-million client portfolio.

Analytical Consultant

Nielsen

Maintaining data integrity and leading projects in data process optimization, as well as supporting a migration to Big Data technologies (Google Suite). Delivering both internal and external seminars and training courses around data topics.

Expert Engineer in Project Development and Big Data

Graduate Intern: Associate Systems Engineer

Graduate Fellow

Data Whisperer ♦ Analytics Specialist & Tech Enthusiast ♦ Join My #bookaweekchallenge

📚 Follow & join my #bookaweekchallenge
📍 Current resident of Málaga, Spain & native of Charlotte, North Carolina

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Greek-American currently living in Spain, helping companies harness the power of data. Driven analytics professional & strong supporter of lifelong learning. I hope to bring advanced data technologies to the masses & exploit the potentials of emerging technologies. I am bilingual & passionate about international relations. Avid reader (bibliophile) & traveler. With trips to 60+ countries & first-hand experience working in a variety of cultures & languages, I enjoy & thrive when working in an international environment.

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Alongside my corporate work, I am a regular international speaker supporting data analytics, emerging technologies & women in STEM. Some of my latest involvements include:
● Guest appearances on more than 30 podcasts such as SuperDataScience, Ken's Nearest Neighbors, How to Get An Analytics Job, Driven by Data, Humans of Data Science, Present Beyond Measure, LightsOnData & more.
● Interview features, guest lecturer, panelist and event host!
● Keynote speeches delivered regularly since 2018 across 15+ countries in-person, virtually spanning the globe, with audiences surpassing 1,000+. Top involvements include:
→ → The WPO Annual Conference 2022 in Montreal CA, "The Information Age: Digitized & Datafied"
→ → Bloom Tech Forum 2021, "Harnessing the Power of Data for a Better Tomorrow"
→ → The WPO Annual Conference 2021 in Nashville TN, “Data Science: A Double-Edged Sword"
→ → The World Knowledge Forum 2021, “The Information Age: Digitized & Datafied"
→ → The Global DIY-Summit 2021, "The Future of Retail: Data as a Differentiator"
→ → Africa Communications Week 2021, “The Shift toward Data-Driven Marketing & Communications” 
→ → DATAcated Conference 2021, “From zero to hero: a simple explanation of ML"
→ → DATAcated Conference 2020, “SQL isn't going anywhere"
→ → Quantic EMBA Conference 2020, “The Truth behind the Buzz- Emerging Data Technologies for the 21st Century"
→ → The Smart Future World Expo 2019 in Istanbul Turkey, "Harnessing the Power of Big Data"
→ → TechIE 2019 in Madrid Spain, "Big Data & ML Intro Workshop"
→ →  IE-UCOM Big Data Day 2018 in Yerevan Armenia, "Harnessing the Power of Big Data"

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For further opportunities (podcasts, events, etc.) in my interested fields, feel free to contact me directly.

The opinions stated here are my own, not those of my employers.

*Note that I may earn a small commission for sales through my affiliate links shared here.

Christina Stathopoulos, MSc

MongoDB

Staff Developer Advocate (AI/ML)

Slalom Build

Machine Learning Architect

NVIDIA

As a Contributing Writer for NVIDIA, I write and creatte compelling content on Machine Learning, Data Science and AI topics for a data science audience. 

My role involves researching and staying up-to-date on the latest advancements in these fields and identifying and writing about topics that interest our readers.

Key Responsibilities:

- Researching and staying up-to-date on the latest advancements in Machine Learning, Data Science and AI
- Identifying and writing about topics that would be of interest to our readers
- Creating compelling and informative content for our data science audience
- Collaborating with other writers and editors to ensure that our content is of the highest quality
- Leveraging my expertise in these fields to create technical tutorials and explain complex concepts in an easy-to-understand manner

Contributing Writer

Emeritus

Providing educational services for a Professional Certificate in Machine learning and Artificial Intelligence for Imperial College Business School

- Creating and presenting lectures on Data Science and Statistics topics in relation to machine learning
- Informing students on the latest advanced trends within the field of deep learning and artificial intelligence
- Creating and presenting lecture content around machine learning and deep learning topics such as convolutional neural networks, backpropagation, neural networks etc
- Introducing and showcasing functionalities of tools utilised within the ML field to students, such as Keras, TensorFlow, SciKit Learn, Pandas etc
- Educating students on the development of machine learning models, from traditional Random Forest classifiers to more complex deep learning neural networks architecture.
- Informing students about real-world problems where machine learning solutions can be leveraged
- Marking and providing grades and feedback on weekly assignments and a final course capstone project.
- Weekly delivery of machine learning and deep learning course materials over 25 weeks.

Program Lead / Learning Facilitator 

Loveshark

Leading Computer Vision initiative and implementation for unique and immersive experience within iOS mobile game applications.

Responsible for the research and implementation of common Computer Vision techniques such as pose estimation, semantic segmentation, gesture recognition and face/facial feature detection.

Mainly working with the TensorFlow machine learning library (Python and JS) and Apple's CoreML models

Computer Vision Engineer

O'Reilly Media

Constructed, led and delivered a training course on Computer Vision in the O'Reilly Live training session.

https://bit.ly/3JoOjAi



- Led a team of students in learning the theory and practical components of computer vision and deep - 
learning
- Provided explanations of widely used terminologies and taught Python programming language
- Guided students in implementing techniques and solutions to a typical computer vision image - classification problem using Python, TensorFlow, NumPy, and pandas
- Used Jupyter notebooks for hands-on learning and practice
- Taught the application of computer vision in practical environments
- Explained the fundamental components of deep learning as a machine learning field
- Demonstrated how to leverage deep learning to solve computer vision tasks
- Taught the structural components of deep neural networks and convolutional neural networks
- Provided guidance for students to implement solutions to common computer vision tasks
- Demonstrated the use of machine learning libraries to implement deep learning solutions
- Helped students to build a deep neural network that classifies images
- Helped students to build a convolutional neural network (AlexNet) that classifies images
- Demonstrated the use of TensorBoard to visualize the neural network training process

Course Instructor

Eventcide

Founder and Developer

Allianz

Front End developer utilizing the Angular 1.x and Angular framework

Contract Web Developer

Front End Developer focused on the Angular Framework and JavaScript Web applications

TeacherHQ

Developing and maintaining the application Frontend with AngularJS
Developing and maintaining the application Backend with NodeJS and Express
Developing and maintaining the application database (PostgreSQL)(KnexJS)
Deploying and maintaining application and database server on AWS
Developing functional testing and maintaining Front End browser testing with Nightwatch
Implementing and handling application error logging with Rollbar
Building RESTful APIs endpoints
Managing ongoing project plans and timelines.
Training junior developers.
Turning ideas from the team into product features
Oversight of core platform development (Front and Back end)

Full Stack JavaScript Developer

Richmond Alake is a highly experienced Machine Learning Architect and Engineer with over five years of expertise in the field. He specializes in Computer Vision and Deep Learning and has a proven track record of successfully developing and integrating deep learning models to solve a wide range of problems, such as motion detection, object detection, and pose estimation. Throughout his career, he has worked with a diverse range of clients, including large conglomerates, financial institutions, and small startups. In addition to his professional work, Richmond also serves as an AI advisor to a number of startups in the UK and the US.

With a background in building websites and mobile applications, Richmond is a firm believer in using technology to solve everyday problems. He has extensive knowledge of Machine Learning and has written over 200 articles on the subject, gaining over a million views. He was recognized as one of Medium's top AI writers in 2020/2021 and has collaborated with companies such as O'Reilly, BuiltIn and Nvidia to develop effective educational and informative learning materials on AI.

Currently, Richmond Alake is a Machine Learning Architect at Slalom Build UK. As the first hire of the machine learning practice in the UK division, he is responsible for helping organizations move from machine learning research to productionization and assisting maturing organizations in promoting AI models into existing infrastructure to drive commercial and business value. His main role as an ML Architect is to assist organizations in developing and maintaining machine learning pipelines by implementing MLOps principles, techniques, and tooling. He is well-versed in Feature Stores and has conducted internal training for Data Engineers, Data Scientists, and ML Engineers.

Richmond Alake

Is it safe to say I coined the term "Memory Engineering"
At least within the context of AI/Agentic systems.
Someone let me know who beat me to it 😉

Either way I'm glad to see Agent Memory taking centre stage in a number of conversations in Agent Engineering. And equally glad to see some startups raising funds to solve what argualbly is one of the last problems to solve in AI.

Want to hear more about Memory Engineering,
Then you can catch my talk at the AI Native Dev Conference in NY next week.

And an advance warning: Nothing in my talk will be “new”.
Vectors are not new.
Automated workflows are not new.
And BTW AI existed long before ChatGPT.

Those who get to listen in on this talk will understand the evolution of the job AI Developers are doing to make AI reliable, believable and capable. 
I'll touch on the following:
1️⃣ Prompt Engineering as Maximisation (pack more tokens into the context window)
2️⃣ Context Engineering as Optimisation (select and structure the right tokens)
3️⃣ Memory Engineering as Continuation (persist, retrieve, adapt, and improve over time)
4️⃣ My developing understanding of Continual Learning
5️⃣ The work AI Developers building AI Agents will be doing the most in 2026 

If you’re in New York next week, say hi.
If you’re not — I’ll share the key insights here as always.

The AI Stack Engineer | AI Strategist and Educator

I must be one of the worst "LinkedIn Influencers"
I didn't even notice I passed 10,000 followers.
Apparently it's something to be celebrated.
So....let's celebrate....

Now that's out of the way 😉
Follow me if you are interested in hearing about
- AI Agents and Applications development (technical stuff)
- Agent Memory and Memory Engineering (mostly propaganda lol)
- Occasional pics/vids of my travel when I decide to leave my home office
- And summary pancake discussion with my friends and folks in the AI space
- #100daysofAgentMemory and more

When is the next milestone to be celebrated?

How far would you go to prove a point?
For me? 
Pretty far.
Like UK → New York far. ✈️.

🧠 Day 59 / 100 of Agent Memory

Today I found myself in front of dozens of AI developers at the AI Native Dev Conference in NYC(a moment that’s been over a year in the making).

I’ve spent the last couple of years advocating for a clear, practical way for AI developers to implement Agent Memory — something that’s intuitive, production-ready, and doesn’t require a PhD in retrieval pipelines.

Not a lot of people understood what I meant when I said the words "Agent Memory" or "Memoru Engineering"... it's obvious now... back then, not so much.

So I built MemoRizz. 
1/3 serious mission. 1/3 wild experiment. 
1/3 running joke that somehow escaped.

But very quickly — talking to real engineers working on real production agent workloads — I realised something: Their biggest bottlenecks weren’t model hallucinations. It was retrieval, latency, chunking, indexing, search optimization, and hybrid retrieval. All the “deep plumbing” work that makes agents reliable.

So today, I delivered a 2-hour workshop showing developers not just what Agent Memory is, but how to build it using a fully integrated Agent Stack:
🔹Oracle AI Database — Memory Provider (Memory Core)
🔹Tavily — Web search/internet access
🔹OpenAI — LLM (Reasoning Core)/Embedding Model provider

Code is here: https://lnkd.in/e9WEZUdZ.

We even took a crack at implementing Deep Research: https://lnkd.in/e_Yhv-ZJ

And after the workshop, one developer came up to me and said:
“Wow Richmond… I didn’t know you were going into that much detail.”
Look — same. That makes two of us.

But the more I teach this stuff, the clearer the whole Agent Stack becomes:
And right in the middle — quietly powering all the continuity, personalization, and intelligence — is the Memory Core.

More on Memory Core later.

Side note:
I love talking about Agent Memory…
But man, I think I’ve been to the US like five times this year 😅
What conferences should I be speaking at in Singapore and Tokyo next? I think it’s time I spread this Agent Memory gospel to the other side of the world.

Special thanks to the Tessl team for making this happen and Sofia Guzowski for the pics.

#100DaysOfAgentMemory #AgentMemory #MemoryEngineering #AIEngineering #AgenticSystems #OracleAIDatabase

Google is onto something here.
And yes. This is more Agent Memory propaganda.
Buckle up 🙂

🧠 Day 58 / 100 of Agent Memory

If you are interested in agentic systems, context management, memory architectures, multi-agent coordination, or the future of AI engineering, then bookmark this:

“Context Engineering: Sessions, Memory” [1]
 Authors: Kimberly Milam, Antonio Gulli, and a full research team.

This is a 70-page deep dive into: Sessions. Memory managers, Retrieval strategy, Compaction, Multi-agent interoperability, Procedural memory, Memory provenance, Memory-as-a-tool, and Production architectures

That's a lot.

But if you've been following the #100daysOfAgentMemory series, then you will be able to follow what most of the whitepaper introduces and explains.

I’m not going to break it down here (that deserves its own day).

But I'm glad, like I said
2026: The year of AI/Agent Memory
How many Agent Memory Talks can I do in a year?

Whitepaper: https://lnkd.in/eGP6amkq

#100DaysOfAgentMemory

Are the Cursor team secretly following the Agent Memory series? 👀

🧠 Day 57/100 of Agent Memory

Because back on Day 53[1] of the Agent Memory series, I speculated that Cursor’s internal architecture probably used a hybrid retrieval approach — blending pre-computed (RAG-style) retrieval with Just-in-Time (JIT) context loading to make their coding agent more efficient.

And last week, they published “Improving Agents with Semantic Search”[2] — confirming almost exactly that. They revealed how semantic search, paired with traditional grep-style keyword search, dramatically boosts coding agent performance:

✅ Up to 12.5% higher accuracy
✅ More retained code changes
✅ Fewer iterations to reach a correct solution

💡 The Not-so Hidden Shift: From Search to Continual Learning
It looks like Cursor’s team didn’t just optimize retrieval — they turned it into a learning process. By training their custom embedding model on agent session traces, they’ve essentially built a retrieval feedback loop.

Each interaction becomes an example of what the agent should have retrieved earlier, helping the system refine its sense of semantic relevance over time.

That’s a subtle but major step forward — retrieval as experience accumulation.

In traditional systems, retrieval is a stateless function: you query, you fetch, you forget. But in Cursor’s approach, retrieval now evolves — using history to inform future behavior. That’s the foundation of memory engineering.

And what’s especially important here:  even those “small” percentage gains (like 0.3%, 1%, or 2%) aren’t trivial.  In large, production-scale engineering environments, they compound into huge outcomes — fewer bugs, higher code reliability, smoother merges, and stronger developer trust.

🧠 Rethinking Continual Learning

For the longest time, I viewed continual learning as something that applied only to large language models — complex, expensive, and risky to run in production. But this approach opened my eyes: embedding models might be the low-hanging fruit for continual learning. 

They can learn from retrieval logs, feedback loops, or developer traces — improving contextual accuracy without retraining the full LLM.

They’re smaller, faster, and much more adaptable.

That said, there’s a cost curve:
1️⃣ Reindexing overhead rises as embeddings drift, demanding more frequent vector rebuilds.
2️⃣ Forgetting risk increases if updates aren’t properly regularized.

So while embedding-level continual learning gives agility, it has to happen at a measured cadence — perhaps monthly or quarterly, balancing freshness and stability. 

[1] https://lnkd.in/esxqrGWp
[2] https://lnkd.in/eDniUfHT
[3] OpenSpeech Notes: https://lnkd.in/eT4mAVKA

#100DaysOfAgentMemory #MemoryEngineering #ContextEngineering #ContinualLearning

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Christina Stathopoulos, MSc

Richmond Alake

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