WeAreDevelopers Logo WeAreDevelopers
Sign in
For You LIVE
World Congress 2024 • Aug 22, 2024 • Session details

AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment

Joy

Are you still treating generative AI like traditional machine learning? Learn how to pivot your MLOps lifecycle toward RAG, vector databases, and secure prompt architecture.

Pause
Mute Enter Fullscreen
#1 about 2 min

Motivation for creating unified machine learning operations templates

The necessity of tracking diverse data science and development tools drives the creation of unifying structural templates.

#2 about 3 min

Bridging the gap between model management and devops

Integrating machine learning developers with operational teams prevents bottlenecking and improves overall deployment efficiency.

#3 about 3 min

Baseline architecture of retrieval-augmented generation systems

Converting raw domain knowledge into digestible embeddings enables language models to generate accurate and contextual answers.

#4 about 6 min

Breaking down the traditional machine learning life cycle

Structuring operations into distinct quadrants organizes the ecosystem of data management, development, validation, and deployment tools.

#5 about 5 min

Adapting operational workflows and infrastructure for RAG systems

Adopting modern generative architectures shifts operational focus away from raw model training toward efficient model import and context augmentation.

#6 about 4 min

Enhancing generative prompt outcomes through dynamic context tuning

Appending user interaction history and relevant data to queries provides base models with necessary nuance for accurate responses.

#7 about 2 min

Consolidating infrastructure toolchains to minimize operational context switching

Mapping out core infrastructure requirements before selecting software prevents functionality overlap and reduces context switching between operations.

#8 about 4 min

Audience questions on AI agents and pipeline vectorization

Establishing API connections and recurring ingestion routines ensures vector databases reliably process scalable data sources.

#9 about 2 min

Audience questions on model evaluation and data chunking

Balancing document chunk sizes against context windows prevents model confusion when retrieving extensive domain knowledge resources.

Matching moments

3:22 min

Evaluating advanced artificial intelligence platforms for daily recruitment

Rudi Bauer Rudi Bauer +1 · Cappuccino with HR

4:42 min

The generative AI application lifecycle

Anshul Jindal Anshul Jindal · WWC 2025

2:02 min

Shifting focus from isolated models to enterprise AI systems

Mohak Chadha Mohak Chadha · WWC Europe 2026

2:08 min

Essential engineering roles in the generative AI space

Mary Grygleski Mary Grygleski · LIVE

2:52 min

Scaling generative AI use cases across large enterprises

Mike Butcher Mike Butcher +3 · WWC 2024

4:32 min

Audience Q&A on tooling choices and AI application prototyping

MIlan Todorović MIlan Todorović · WWC Europe 2026

More from World Congress 2024

Beyond the Hype: Real-World AI Strategies Panel thumbnail
24:07
10 months ago

Beyond the Hype: Real-World AI Strategies Panel

Mike Butcher, Jürgen Müller, Katrin Lehmann, Tobias Regenfuss

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation thumbnail
21:17
almost 2 years ago

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation

Carl Lapierre

Coding an Immersive Copilot using Unity / .NET and Azure OpenAI! thumbnail
31:50
almost 2 years ago

Coding an Immersive Copilot using Unity / .NET and Azure OpenAI!

Zaid Zaim

Building a diverse tech company: The power of Diversity, Equity, Inclusion, and Belonging thumbnail
29:43
almost 2 years ago

Building a diverse tech company: The power of Diversity, Equity, Inclusion, and Belonging

Sue Quackenbush

Dapr in Practice thumbnail
24:08
almost 2 years ago

Dapr in Practice

marcklefter

Minimising the Carbon Footprint of Workloads thumbnail
27:46
almost 2 years ago

Minimising the Carbon Footprint of Workloads

Michael Mueller

Upcoming sessions on this topic

Open session

World Congress 2026 North America

No Single Model to Rule Them All: Building Resilient AI Agents Across Open & Closed LLMs

Emmanuel Acheampong

Senior Manager Developer Relations at Crusoe AI

Emmanuel Acheampong
Open session

World Congress 2026 North America

Building Stuff with GenAI - The Open Minded Workshop beyond OpenAI

Andreas Erben

CTO for Applied AI and Metaverse at daenet

Andreas Erben
Open session

World Congress 2026 North America

AI-Powered Incident Triage: How We Built GenAI Agents with MCPs to Automate On-Call Workflows

Prakshal Doshi

Site Reliability Engineer

Prakshal Doshi
Open session

World Congress 2026 North America

You Can’t Re-Run Sunlight: Designing ML Data Architectures for Physical AI

An Phan

Senior Data Infrastructure Engineer @ Hippo Harvest

An Phan
Open session

World Congress 2026 North America

AI Agents are Only as Smart as their Context: Building a Real-Time Context Engine at Intuit

Bharat Patel

Lead Software Engineer at Intuit

Bharat Patel
Open session

World Congress 2026 North America

DeepAgents: Build Multi-Agent AI Systems That Actually Work

Apoorva Jaiswal, Anjana Umapathy, Anagha Rumade

Apoorva Jaiswal
Anjana Umapathy
Anagha Rumade

Related videos

Make it simple, using generative AI to accelerate learning thumbnail
26:54
WWC 2024 • almost 2 years ago

Make it simple, using generative AI to accelerate learning

Duan Lightfoot

DevOps for AI: running LLMs in production with Kubernetes and KubeFlow thumbnail
34:21
LIVE • almost 2 years ago

DevOps for AI: running LLMs in production with Kubernetes and KubeFlow

Aarno Aukia

Building Blocks of RAG: From Understanding to Implementation thumbnail
26:25
LIVE • over 1 year ago

Building Blocks of RAG: From Understanding to Implementation

Ashish Sharma

From Traction to Production: Maturing your LLMOps step by step thumbnail
41:45
LIVE • over 1 year ago

From Traction to Production: Maturing your LLMOps step by step

Maxim Salnikov

How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge thumbnail
42:26
Coffee With Developers • over 1 year ago

How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge

Meta Atamel, Guillaume Laforge

Bringing the power of AI to your application. thumbnail
32:26
WWC 2024 • almost 2 years ago

Bringing the power of AI to your application.

Krzysztof Cieślak

Related articles

From learning to earning

Jobs that call for the skills explored in this talk.