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# LLM in Production
# Large Language Models
# DevTools
# AI-Driven Applications
DevTools for Language Models: Unlocking the Future of AI-Driven Applications
Diego Oppenheimer
Data Privacy and Security
Diego Oppenheimer, Gevorg Karapetyan, Vin Vashishta, Saahil Jain & Shreya Rajpal
Emerging Patterns for LLMs in Production
Willem Pienaar
Challenges and Opportunities in Building Data Science Solutions with LLMs
Pascal Brokmeier, Daniel Herde & Viktoriia Oliinyk
Cost Optimization and Performance
Lina Weichbrodt, Luis Ceze, Jared Zoneraich, Daniel Campos & Mario Kostelac
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# Feature Stores
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Simba Khadder · May 26th, 2023
Why We Built an Open-source Virtual Feature Store? Learnings from Serving 100M Monthly Active Users
Simba Khadder shares his insights during a talk at the MLOps Meetup in San Francisco, highlighting their experience with 100 million monthly active users. They discussed their failures and learnings, primarily focusing on building a recommended system. By utilizing embeddings, they generated candidate song recommendations and refined the rankings. Simba emphasized the significance of resolving workflow challenges to enhance the effectiveness of data scientists. They introduced the concept of a feature store, which revolutionized their approach to data science.
# Open-source
# Feature Store
# Featureform
Joao Moura · May 19th, 2023
Entreprise MLOps Patterns and Common Pitfalls
Drawing from his expertise, Joao emphasizes the significance of accessible data, standardized processes, and streamlined implementation in the MLOps landscape. He highlights the crucial roles and responsibilities of various team members involved, including data scientists and MLOps engineers, stressing the need for collaboration and cohesive efforts.
Throughout the presentation, Joao addresses common challenges faced by organizations, such as the intricate nature of reversing production deployments and the absence of clearly defined responsibilities within organizational structures. By exploring these hurdles, he equips the audience with valuable insights to overcome obstacles and optimize MLOps practices.
# MLOps Patterns
# Common Pitfalls
# AWS
Manuel Martin Gomez · May 13th, 2023
MLOPS: Desde las Trincheras
Busuu's journey from a greenfield to a mature MLOps platform is a story of growth, learning, and adaptation. As a language learning platform, Busuu relies heavily on machine learning models to provide personalized and effective learning experiences to its users.
# MLOps platform
# Language Learning Platform
# Busuu
Nils Reimers · May 13th, 2023
Large Language Model at Scale
Large Language Models with billions of parameters have the possibility to change how we work with textual data. However, running them on scale at potentially hundred millions of texts a day is a massive challenge.
Nils talks about finding the right model size for respective tasks, model distillation, and promising new ways on transferring knowledge from large to smaller models.
# Large Language Models
# LLM in Production
# Cohere
Cameron Feenstra · Apr 27th, 2023
Using LLMs to Punch Above your Weight!
As a small business, competing with large incumbents can be a daunting challenge. They have more money, more people, and more data, but they can also be inflexible and slow to adopt new technologies. In this talk, we will explore how small businesses can use the power of large language models (LLMs) to compete with large incumbents, particularly in industries like insurance. We will present two examples of how we are using LLMs at Anzen to streamline insurance underwriting and analyze employment agreements and discuss ideas for future applications. By harnessing the power of LLMs, small businesses can level the playing field and compete more effectively with larger companies.
# LLM
# LLM in Production
# Anzen
Hannes Hapke · Apr 27th, 2023
What is the role of Machine Learning Engineers in the time of GPT4 and BARD?
With the fast pace of innovation and the release of Large Language Models like Bard or GPT4, the role of data scientists and machine learning engineers is rapidly changing. APIs from Google, OpenAI, and other companies democratize access to machine learning but also commoditize some machine learning projects.
In his talk, Hannes will explain the state of the ML world and which machine learning projects are in danger of being replaced by 3rd party APIs. He will walk the audience through a framework to determine if an API could replace your current machine-learning project and how to evaluate Machine Learning APIs in terms of data privacy and AI bias. Furthermore, Hannes will dive deep into how you can hone your machine-learning knowledge for future projects.
# GPT4
# BARD
# API
# Digits Financial, Inc.
Deepankar Mahapatro · Apr 27th, 2023
Taking LangChain Apps to Production with LangChain-serve
Scalable, Serverless deployments of LangChain apps on the cloud without sacrificing the ease and convenience of local development. Streaming experiences without worrying about infrastructure
# LLM
# LLM in Production
# LangChain
# LangChain-serve
Willem Pienaar · Apr 27th, 2023
Emerging Patterns for LLMs in Production
As the landscape of large language models (LLMs) advances at an unprecedented rate, novel techniques are constantly emerging to make LLMs faster, safer, and more reliable in production. This talk explores some of the latest patterns that builders have adopted when integrating LLMs into their products.
# LLM
# LLM in Production
# In-Stealth
Adam Nolte · Apr 27th, 2023
Ensuring Accuracy and Quality in LLM-driven Products
Adam will highlight potential negative user outcomes that can arise when adding LLM-driven capabilities to an existing product. He will also discuss strategies and best practices that can be used to ensure a high-quality user experience for customers.
# LLM
# LLM-driven Products
# Autoblocks
Daniel Montilla Navas · Apr 27th, 2023
MLOps at Early-stage Start-up
Daniel Montilla Navas, a mechanical engineer turned programmer, shares his experience working in startups and his current role at Correcto, an AI tool for text correction and generation in Spanish. He discusses the challenges of developing the product, including dealing with different dialects and constantly changing objectives. Montilla Navas highlights the importance of research and decision-making in a small team, as well as the need for caution when moving quickly in the development process. Finally, he emphasizes the importance of continuous learning in the ever-evolving field of AI.
# Early-stage Start-up
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