Alejandro Saucedo

The State of GenAI & Machine Learning in 2025

Are your AI developer tools creating a hidden productivity bottleneck? This talk explores the downstream costs of GenAI and the new infrastructure required for building agentic systems.

The State of GenAI & Machine Learning in 2025
#1about 5 minutes

The long history and rapid market growth of AI

AI is not a new field, but its market size and user base are growing exponentially, creating significant business opportunities.

#2about 4 minutes

Analyzing the developer productivity funnel for GenAI tools

Most GenAI developer tools focus on the top of the funnel (writing code), creating bottlenecks in testing, operations, and monitoring.

#3about 2 minutes

Overcoming the key challenges of building with GenAI

Adopting GenAI introduces challenges like vendor complexity, moving beyond simple chatbots, and a lack of established best practices for AI systems.

#4about 5 minutes

Deconstructing the modern agentic systems stack

Building robust GenAI requires moving from a model-centric to a system-centric view, encompassing orchestration, data, guardrails, and security.

#5about 3 minutes

Agentic infrastructure and the critical role of data

Effective agentic systems rely on complex infrastructure for non-deterministic data flows and specialized hardware scheduling, underscoring the "garbage in, garbage out" principle.

#6about 2 minutes

New security vulnerabilities and monitoring for AI systems

AI systems introduce unique security risks like data poisoning and require specialized monitoring for performance, explainability, and model drift.

#7about 1 minute

Implementing responsible AI principles by design

To address challenges like algorithmic bias, responsible AI principles must be embedded directly into the design of platforms and infrastructure.

#8about 3 minutes

AI maturity, new roles, and appropriate use cases

AI maturity is a non-linear journey focused on time-to-value, creating new roles like the AI engineer and requiring careful consideration of when not to use GenAI.

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Matching moments

Understanding the GenAI lifecycle and its operational challenges
05:39 MIN

Understanding the GenAI lifecycle and its operational challenges

LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices

Understanding the key challenges in operationalizing GenAI projects
04:28 MIN

Understanding the key challenges in operationalizing GenAI projects

From Traction to Production: Maturing your GenAIOps step by step

Navigating the challenges of GenAI adoption
03:21 MIN

Navigating the challenges of GenAI adoption

The Future of Developer Experience with GenAI: Driving Engineering Excellence

AI's growing impact on developer tools and roles
06:45 MIN

AI's growing impact on developer tools and roles

The Evolving Landscape of Application Development: Insights from Three Years of Research

Overcoming the common challenges in generative AI adoption
03:37 MIN

Overcoming the common challenges in generative AI adoption

From Traction to Production: Maturing your LLMOps step by step

The rise of MLOps and AI security considerations
02:06 MIN

The rise of MLOps and AI security considerations

MLOps and AI Driven Development

GenAI applications and emerging professional roles
05:32 MIN

GenAI applications and emerging professional roles

Enter the Brave New World of GenAI with Vector Search

Defining GenAIOps and its relationship to MLOps
04:26 MIN

Defining GenAIOps and its relationship to MLOps

From Traction to Production: Maturing your GenAIOps step by step

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