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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.

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#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 · World Congress 2025

2:02 min

Shifting focus from isolated models to enterprise AI systems

Mohak Chadha Mohak Chadha · World Congress 2026 Europe

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 · World Congress 2024

4:32 min

Audience Q&A on tooling choices and AI application prototyping

MIlan Todorović MIlan Todorović · World Congress 2026 Europe

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