Engineering, Leadership & Management
Aaron Wise
Engineering metrics for the board: report predictability, reliability, cost, risk and capacity, drop activity counts, and adapt the page as you grow.
Product Management, Engineering
Data Science, DataOps, Engineering, LLMOps, MLOps, Security
Editors Pick, Engineering, Leadership & Management
Aaron Wise
Engineering metrics for the board: report predictability, reliability, cost, risk and capacity, drop activity counts, and adapt the page as you grow.
Aaron Wise
Right-sized platform engineering balances load, risk, and growth stage to avoid over- and under-building.
Aaron Wise
Senior engineering leaders need a clear problem, founder-market fit, and rough business plan before joining a startup.
Aaron Wise
Build vs buy decisions by company stage: buy by default, build where you differ, wait with a trigger, and revisit every vendor call at Series B.
Aaron Wise
MLOps industrializes predictive models, while LLMOps operationalizes large language models for generative tasks.
Aaron Wise
Engineering metrics for the board: report predictability, reliability, cost, risk and capacity, drop activity counts, and adapt the page as you grow.
Aaron Wise
Build vs buy decisions by company stage: buy by default, build where you differ, wait with a trigger, and revisit every vendor call at Series B.
Engineering, Insights, Leadership & Management, Must Read
Senior engineering leaders need a clear problem, founder-market fit, and rough business plan before joining a startup.
The right roadmap prioritization model depends on your company’s growth stage and strategic goals.
Aaron Wise
NLP adds context to flat text through statistical and neural models, enabling machines to act on meaning rather than symbols.
Aaron Wise
Azure builds a reliable AI platform by combining native services with third-party tools, structured environments, and a cross-functional team.
Aaron Wise
LLMOps is the essential framework for transforming experimental chatbots into reliable, production-ready systems.
Senior engineering leaders need a clear problem, founder-market fit, and rough business plan before joining a startup.
Team composition must align with capital stage and execution goals to build durable, scalable organizations.
LLMOps is the essential framework for transforming experimental chatbots into reliable, production-ready systems.
MLOps bridges ML experimentation and production by systematizing pipelines, governance, and monitoring to ensure reliability and scalability.
Cross Organizational, Engineering, Insights
Team composition must align with capital stage and execution goals to build durable, scalable organizations.
Cross Organizational, DataOps, Engineering
Building a data program from scratch requires cultivating trust, defining clear value drivers, and aligning people before platforms.
