Yanir Seroussi – AI/ML Success Architect

Updating data’s hierarchy of needs with org foundations and automated decisions.

Climate tech is losing trendiness but not relevance.

An AI/ML Success Architect helps companies decide when to use AI, when not to, and how to design and build systems for real business impact.

Summary of my talk on the paper “Design Patterns for Securing LLM Agents against Prompt Injections”.

A LinkedIn post with little traction led to a surprising AI collaboration – proof that sharing online can spark unexpected opportunities.

Themes from AI Con 2024: data moats, stealthy AI use, Chatty’s UX revolution, and enduring fundamentals.

Building AI is hard and expensive.

On moving away from weekly blog posts in favour of deeper inconsistent articles and LinkedIn engagement.

Podcast chat on the reality of Data & AI and my consulting focus: Helping climate & nature tech startups ship data-intensive solutions.

Highlights and lessons from my solo expertise biz, including value pricing, fractional cash flow, and distractions from admin & politics.

My views on separating AI hype and bullshit from the real deal.

Exploring why universal advice on startup data stacks is challenging, and the importance of context-specific decisions in data infrastructure.

Podcast chat on my career journey from software engineering to data science and independent consulting.

Reflections on building a solo expertise business in Data & AI, focusing on climate tech startups.

The real world of AI/ML doesn’t fit into a neat diagram, so I created another diagram and a maturity heatmap to model the mess.

Video and key points from the second part of a webinar on a startup’s first data hire, covering tips for defining the role and running the process.

Video and key points from the first part of a webinar on a startup’s first data hire, covering data & AI definitions and high-level recommendations.

Asking identical questions about my MagicGrantMaker idea yielded near-identical responses from the top chatbot models.

Questions to assess the security posture of a startup, focusing on basic hygiene and handling of sensitive data.

Takeaways from an interview with Patty McCord on The Startup Podcast.

Questions to assess the quality of tech stacks and lifecycles, with a focus on artificial intelligence, machine learning, and analytics.

Quotes from Demetrios Brinkmann on the relationship between MLOps and DevOps, with MLOps allowing for managing changes that come from data.

Since we’re far from a utopia where data issues are fully handled by AI, this post presents six questions humans can use to assess data projects.

Summary of the main messages from the book The Passion Economy by Adam Davidson.

Expanding on the startup health check question of tracking Kukuyeva’s five business aspects as wide events.

Questions that prospective data specialists and engineers should ask about development processes before accepting a startup role.

Three essential questions to understand where an organisation stands when it comes to Data & AI (with zero hype).

Overview of the book The Economy of Algorithms by Marek Kowalkiewicz.

Eight questions that prospective data-to-AI employees should ask about a startup’s work and data culture.

Ten questions that prospective employees should ask about a startup’s team, especially for data-centric roles.

Fourteen questions that prospective employees should ask about a startup’s business model and product, especially for data-focused roles.

Reflections on what it takes to package expertise and deliver timely, actionable advice outside the context of employee relationships.

Reviewing the areas that should be assessed to determine a startup’s opportunities and challenges on the data/AI/ML front.

It’s easy to prototype with AI, but production-grade AI apps require even more thorough testing and observability than traditional software.

An high-level overview of things I learned from Justin Welsh’s LinkedIn Operating System course.

The story of how I joined Work on Climate as a volunteer and became its data tech lead, with lessons applied to consulting & fractional work.

My key takeaways from reading Fundamentals of Data Engineering by Joe Reis and Matt Housley.

Discussing the use of AI to automate underwater marine surveys as an example of the uneven distribution of technological advancement.

I put the book to use after the first listen, and will definitely revisit it in the future to form better habits.

Discussing considerations that arise when attempting to automate the extraction of structured data from PDFs and similar documents.

Classifying startups as ML-centric or non-ML is a helpful exercise to uncover the data challenges they’re likely to face.

Two stories of getting AI functionality to production, which demonstrate the risks inherent in custom development versus starting with a no-code approach.

First post in a series on building a minimum viable data stack for startups, introducing key definitions, components, and considerations.

Jonathan Stark makes a compelling argument why you should have the three Cs before quitting your job to go solo consulting.

Getting ChatGPT Plus to elaborate on possible book content and produce a PDF cheatsheet, with the goal of learning about its capabilities.

Reflecting on an interview with Jason Warner, CEO of poolside.

Advice for hiring a startup’s first data person: match skills to business needs, consider contractors, and get help from data people.

Shifting focus to ‘Data & AI for Impact’, with more startup-related content, increased posting frequency, and deeper audience engagement.

When focusing on a market segment defined by personal beliefs, it’s often fine to position yourself as a generalist in your craft.

Repeated exposure to media personas creates relationships that help justify premium fees.