Data moats, stealthy AI, and more: AI Con 2024 notes
Themes from AI Con 2024: data moats, stealthy AI use, Chatty’s UX revolution, and enduring fundamentals.
Data Strategy
Giving up on the minimum viable data stack
Exploring why universal advice on startup data stacks is challenging, and the importance of context-specific decisions in data infrastructure.
AI/ML lifecycle models versus real-world mess
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.
Your first Data-to-AI hire: Run a lovable process
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.
Learn about Dataland to avoid expensive hiring mistakes
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.
Is your tech stack ready for data-intensive applications?
Questions to assess the quality of tech stacks and lifecycles, with a focus on artificial intelligence, machine learning, and analytics.
Dealing with endless data changes
Quotes from Demetrios Brinkmann on the relationship between MLOps and DevOps, with MLOps allowing for managing changes that come from data.
AI ain't gonna save you from bad 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.
Startup data health starts with healthy event tracking
Expanding on the startup health check question of tracking Kukuyeva’s five business aspects as wide events.
How to avoid startups with poor development processes
Questions that prospective data specialists and engineers should ask about development processes before accepting a startup role.
Plumbing, Decisions, and Automation: De-hyping Data & AI
Three essential questions to understand where an organisation stands when it comes to Data & AI (with zero hype).
Question startup culture before accepting a data-to-AI role
Eight questions that prospective data-to-AI employees should ask about a startup’s work and data culture.
Probing the People aspects of an early-stage startup
Ten questions that prospective employees should ask about a startup’s team, especially for data-centric roles.
Business questions to ask before taking a startup data role
Fourteen questions that prospective employees should ask about a startup’s business model and product, especially for data-focused roles.
Assessing a startup's data-to-AI health
Reviewing the areas that should be assessed to determine a startup’s opportunities and challenges on the data/AI/ML front.
My experience as a Data Tech Lead with Work on Climate
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.
Two types of startup data problems
Classifying startups as ML-centric or non-ML is a helpful exercise to uncover the data challenges they’re likely to face.
Avoiding AI complexity: First, write no code
Two stories of getting AI functionality to production, which demonstrate the risks inherent in custom development versus starting with a no-code approach.
Building your startup's minimum viable data stack
First post in a series on building a minimum viable data stack for startups, introducing key definitions, components, and considerations.