Daniel Svonava

Vector Compute @ Superlinked | xYouTube

➡️ Building a compute framework for turning data into vector embeddings.
🤗 Connect your data infrastructure to your vector database with Superlinked.

Join our product preview:
📋 Try it out in a Python notebook with no dependencies.
↗️ Design the perfect vectors for your retrieval use-case.
🔧 Mix and match streaming, batch, structured and unstructured data.
🚀 Launch to production with your favorite Vector Database.

➡️ Let's sort out information retrieval across your organization!
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Daniel is a Senior Software Engineer at Google, where he tech-leads, designs and builds forecasting & pricing systems for ads worth billions of dollars. These are distributed systems that run on thousands of machines, process terabytes of data per query and combine Machine Learning modeling with game-like simulation of tens of thousands ad campaigns - all happening in real-time to enable thousands of concurrent users of buying tools like AdWords and DBM.

Daniel wields a rare combination of deep technical skills, a user-centric drive and an ability to align multiple teams towards a common goal - launching and landing high quality cutting edge products.

Daniel co-founded a tech startup that brought machine vision to the consumer photo space 10 years ago, he was an invited speaker at an ML dev conference in Zurich and speaks at startup events regularly. He holds multiple patents with Google, is published in a peer-reviewed ML journal from his time at IBM Research and is a Google Code Jam on-site finalist.

On weekends he flies, climbs, skis and bikes in the Alps, see: http://instagram.com/mountain.supo

Co-Founder & CEO

superlinked.com makes it easier to turn data into vector embeddings

I build what matters.

I’ve spent the last two decades at the intersection of cloud computing, AI, and human potential; not to push technology for its own sake, but to shape systems that serve people with dignity and purpose.

Raised on values of service, curiosity, and faith, I carry a lifelong fascination with how seemingly small actions compound. Whether building platforms at AWS, leading AI initiatives at Microsoft and Redis, or mentoring young founders; I’ve always been drawn to the question: How can our ambitions and pursuits in life help us become more human, not less?

I lead AWS's Generative AI Innovation & Delivery at AWS, helping individuals and organizations unlock real-world value from emerging technologies like AI while keeping people and purpose at the center.  We are a premier organization that forward deploys organizations into the era of cognitive abundance comprising of Generative and Agentic AI.

Taimur Rashid

Ramadan begins in today.

For many, it is a sacred month of fasting and prayer. But beyond the religious observance, Ramadan carries a deeper invitation; to pause.

In a world that optimizes for speed, output, and constant reaction, Ramadan asks us to slow down. To reflect. To introspect. And it all started with a simple word:

“Read.”

Not just read text, but read yourself.
Read your intentions.
Read the room.
Read the moment we are living in.

As individuals, it is a chance to recalibrate.
As organizations, it is a reminder to reflect on purpose beyond performance.
As communities, it is an opportunity to examine whether progress is aligned with principle.

Fasting is not merely abstention from food; it is restraint, discipline, and clarity. It is creating space so that light can enter.

In a time of rapid technological shifts, geopolitical uncertainty, and economic acceleration, perhaps what we need most is the courage to pause long enough to truly “read” what is unfolding around us.

To my friends, colleagues, and community observing this month, I wish you Ramadan Mubarak. May it be a month of clarity, humility, and renewed intention.

And for all of us, regardless of faith, may it be a reminder that reflection is not weakness; it is leadership.

#Ramadan #Reflecfion #InnovationWithPurpose

Head of Generative AI Innovation & Delivery | Driving Breakthrough AI Innovations at AWS | Investing & Fueling the Growth of Cutting-Edge Data & AI Startups

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More and more, I'm speaking with executives across the C-Suite who are rapidly working to understand how AI operates and how to maintain strategic alignment with its evolution. Inference represents a critical building block 🧱 of AI infrastructure, and understanding it is essential for informed decision-making.
 
Consider a straightforward scenario: A business traveler in San Diego 🏖️ queries their AI assistant, "Where can I get great sushi 🍣 right now?" Within two seconds, they receive three highly rated options with available reservations. This seemingly simple interaction exemplifies inference; the process through which trained AI models generate responses to new inputs.
 
⚡ According to MIT Technology Review, inference accounts for 80-90% of AI computing power. Not model training, but model execution at scale.
 
The economic distinction is significant 💰 : Training an AI model represents a substantial but one-time investment, analogous to professional education. Inference represents the ongoing operational cost of deploying that capability thousands or millions of times daily. For enterprise applications, inference costs quickly eclipse training investments.
 
This two-second response involves four distinct computational stages occurring in milliseconds: tokenization (parsing input into processable units), prefill (contextual analysis across billions of parameters), decode (sequential response generation), and detokenization (conversion to human-readable output).
 
🤖 AI agents introduce exponential complexity. Rather than single predictions, agents orchestrate multiple models to execute multi-step workflows—calendar coordination, research synthesis, comparative analysis, recommendation generation. Each workflow represents interconnected inference chains. At enterprise scale, computational demands and associated costs multiply rapidly. 

Infrastructure architecture becomes strategically critical. AWS has developed custom Trainium chips delivering up to 40% cost reduction, Project Mantle for intelligent workload prioritization, and unified networking architecture for optimized data flow.
 
The market dynamics are noteworthy: inference infrastructure investment surpassed training infrastructure spending by end of 2025, while per-inference costs continue declining. This creates expanding economic viability for new applications.
 
👩‍💼 🧑‍💼 For C-Suite leaders, understanding inference enables more strategic questions: What is our inference cost structure? How should we balance latency requirements against cost optimization? What infrastructure decisions position us effectively for agentic AI deployment?
 
🚀 The competitive advantage lies not merely in the capability, but in the infrastructure enabling it at scale.
 
🔗 https://lnkd.in/g8YWN6Ty
 
#AWS #GenerativeAI #AIInference #EnterpriseAI #AIStrategy

Open article: Inside Gen AI's split-second sprint from question to answer by aboutamazon.com

Inside Gen AI's split-second sprint from question to answer

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Daniel Svonava

Taimur Rashid

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The Battle

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Taimur Rashid

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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);}Taimur Rashid's LinkedIn Performance

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