Канал: MLOps World: Machine Learning in Production

Making Enterprise GenAI Safe and Effective - Tools and Approaches

Making Enterprise GenAI Safe and Effective - Tools and Approaches

Running prompts at CI does not make your GenAI app enterprise ready

Running prompts at CI does not make your GenAI app enterprise ready

The BEST component for your RAG system

The BEST component for your RAG system

Why AI apps don't work in prod: AI Reliability Survey

Why AI apps don't work in prod: AI Reliability Survey

What It Actually Takes to Deploy GenAI Applications to Enterprises Custom Evaluation Models

What It Actually Takes to Deploy GenAI Applications to Enterprises Custom Evaluation Models

Lessons learned from scaling large language models in production

Lessons learned from scaling large language models in production

From Idea to Production: AI Infra for Scaling LLM Apps

From Idea to Production: AI Infra for Scaling LLM Apps

LLM Fine-Tuning for Modern AI Teams: How One E-Commerce Unicorn Cut Inference Cost by 90%

LLM Fine-Tuning for Modern AI Teams: How One E-Commerce Unicorn Cut Inference Cost by 90%

Function Calling for LLMs: RAG without a Vector Database

Function Calling for LLMs: RAG without a Vector Database

Finding training inefficiencies with CentML DeepView

Finding training inefficiencies with CentML DeepView

Evaluating LLMs and RAG Pipelines at Scale

Evaluating LLMs and RAG Pipelines at Scale

Empowering Data Science Teams: Harnessing AI with Appen

Empowering Data Science Teams: Harnessing AI with Appen

Better Chatbots with Advanced RAG Techniques

Better Chatbots with Advanced RAG Techniques

Enhance Cost Efficiency in Domain Adaptation with PruneMe

Enhance Cost Efficiency in Domain Adaptation with PruneMe

Data Versioning in Generative AI: A Pathway to Cost-effective ML

Data Versioning in Generative AI: A Pathway to Cost-effective ML

Building ML and GenAI Systems with Metaflow

Building ML and GenAI Systems with Metaflow

Efficiently Fine-Tune And Serve Your Own LLMs

Efficiently Fine-Tune And Serve Your Own LLMs

The Who, What, and Why of Data Lake Table Formats

The Who, What, and Why of Data Lake Table Formats

Private, Local AI

Private, Local AI

The Journey of Building a Leading Open Source LLM Security Toolkit

The Journey of Building a Leading Open Source LLM Security Toolkit

The Secret Sauce for Deploying LLM Applications into Production

The Secret Sauce for Deploying LLM Applications into Production

Running Multiple Models on the Same GPU, on Spot Instances

Running Multiple Models on the Same GPU, on Spot Instances

Towards Robust GenAI: Techniques for Evaluating Enterprise LLM Applications

Towards Robust GenAI: Techniques for Evaluating Enterprise LLM Applications

Introducing Arize-Phoenix and OpenInference

Introducing Arize-Phoenix and OpenInference

Mitigating RAG Hallucinations with Aporia Guardrails

Mitigating RAG Hallucinations with Aporia Guardrails

LLMs From Dream to Deployed

LLMs From Dream to Deployed

Evaluation Engineering: Iterative Strategies to Testing Prompts

Evaluation Engineering: Iterative Strategies to Testing Prompts

Customizable RAG Workflows with your Own Data

Customizable RAG Workflows with your Own Data

Evaluation Techniques for Large Language Models

Evaluation Techniques for Large Language Models

Wanted: A Silver Bullet MLOps Solution for Enterprise

Wanted: A Silver Bullet MLOps Solution for Enterprise

Lessons Learned: The Journey to Real-Time Machine Learning at Instacart

Lessons Learned: The Journey to Real-Time Machine Learning at Instacart

MLOPs On Highly Sensitive Data

MLOPs On Highly Sensitive Data

Avoid ML OOps with ML Ops: A modular approach to scaling Forethought’s E2E ML Platform

Avoid ML OOps with ML Ops: A modular approach to scaling Forethought’s E2E ML Platform

Making ChatGPT funny with Prompt Optimization

Making ChatGPT funny with Prompt Optimization

What Does it Mean to be an AI Engineer in the World of Gen

What Does it Mean to be an AI Engineer in the World of Gen

QA in ML

QA in ML

Intelligent Writing Assistants in the era of Large Language

Intelligent Writing Assistants in the era of Large Language

Applying Responsible AI with the Open-Source LangTest Library

Applying Responsible AI with the Open-Source LangTest Library

LLM economics The Cost of leveraging Large Language Models

LLM economics The Cost of leveraging Large Language Models

Lessons Learned Productionising LLMs for Stripe Support

Lessons Learned Productionising LLMs for Stripe Support

Synthetic Data: Generative AI for Enhanced Data Quality in the Era of Foundational Models

Synthetic Data: Generative AI for Enhanced Data Quality in the Era of Foundational Models

How Do You Scale to Billions of Fine-Tuned LLMs

How Do You Scale to Billions of Fine-Tuned LLMs

From Model T to Machine Learning: A Glimpse into Ford's MLOps and Hybrid Infrastructure Strategy

From Model T to Machine Learning: A Glimpse into Ford's MLOps and Hybrid Infrastructure Strategy

Low-latency Model Inference in Finance - A close look at Seldon V2

Low-latency Model Inference in Finance - A close look at Seldon V2

Evolution of ML Training and Serving Infrastructure @ Pinterest Ads

Evolution of ML Training and Serving Infrastructure @ Pinterest Ads

MLOps isn't that hard: Modular Stack with Open-Source Tools

MLOps isn't that hard: Modular Stack with Open-Source Tools

Learning from Extremes: What Fraud-Fighting at Scale Can Teach Us About MLOps Across Domains

Learning from Extremes: What Fraud-Fighting at Scale Can Teach Us About MLOps Across Domains

Supercharging Recommender Systems: Unleashing the Power of Distributed Model Training

Supercharging Recommender Systems: Unleashing the Power of Distributed Model Training

Building an MLOps Team

Building an MLOps Team

How to Design and Build Resilient Machine Learning Systems

How to Design and Build Resilient Machine Learning Systems

LLM Evaluation to Craft Delightful Content From Messy Inputs

LLM Evaluation to Craft Delightful Content From Messy Inputs

Bring AI to your existing databases without complex MLOps

Bring AI to your existing databases without complex MLOps

Apache Airflow: Where data engineers and ML engineers meet

Apache Airflow: Where data engineers and ML engineers meet

Beyond the Model Zoo: Optimizing Foundation Models for Your Application

Beyond the Model Zoo: Optimizing Foundation Models for Your Application

Keynote Ofer Hermoni

Keynote Ofer Hermoni

Generative AI World & MLOPs World 2023, Austin

Generative AI World & MLOPs World 2023, Austin

Reproducibility and Data version control for LangChain and LLM/OpenAI Models

Reproducibility and Data version control for LangChain and LLM/OpenAI Models

Spend Less Time Troubleshooting ML Production Issues

Spend Less Time Troubleshooting ML Production Issues

Building a Conversation focused LLM on Communication Data

Building a Conversation focused LLM on Communication Data

Learn Your Codebase: Fine-tuning CodeLlama with Flyte… to Learn Flyte

Learn Your Codebase: Fine-tuning CodeLlama with Flyte… to Learn Flyte

Introduction to Building ML Microservices: A Hands-On Approach With Examples From The Music Industry

Introduction to Building ML Microservices: A Hands-On Approach With Examples From The Music Industry

LLMs in Practice A Guide to Recent Techniques and Trends

LLMs in Practice A Guide to Recent Techniques and Trends

Deploying Generative AI Models: Best Practices and an Interactive Example

Deploying Generative AI Models: Best Practices and an Interactive Example

Applying GitOps Principles At Every Step Of An E2E MLOps Project - An Interactive Workshop

Applying GitOps Principles At Every Step Of An E2E MLOps Project - An Interactive Workshop

Feature Stores in Practice: Train and Deploy an End-to-End Fraud Detection Model with Featureform...

Feature Stores in Practice: Train and Deploy an End-to-End Fraud Detection Model with Featureform...

Finetuning a Large Language Model on a Custom Dataset

Finetuning a Large Language Model on a Custom Dataset

Stacked Up Series: A Deep Drive Into Integrating Large Language Models Into Your Proudct

Stacked Up Series: A Deep Drive Into Integrating Large Language Models Into Your Proudct

Building Your LLM Factory

Building Your LLM Factory

Protecto - Enabling AI with Security and Trust

Protecto - Enabling AI with Security and Trust

Is it too much to ask for a stable baseline?

Is it too much to ask for a stable baseline?

Fine-Tune LLMs or Integrate 3rd party APIs? A Financial Case-Study

Fine-Tune LLMs or Integrate 3rd party APIs? A Financial Case-Study

How to Finetune Your LLMs and Evaluate Performance

How to Finetune Your LLMs and Evaluate Performance

Business Panel: GenAI Use-cases Across Industry Verticals. Early Trends & ROI

Business Panel: GenAI Use-cases Across Industry Verticals. Early Trends & ROI

LLMs from Hallucinations to Relevant Responses

LLMs from Hallucinations to Relevant Responses

Data @ League

Data @ League

The Coming Nuclear Winter for AI Chip Companies

The Coming Nuclear Winter for AI Chip Companies

Your AI Applications Need Guardrails: Here's How To Build Them

Your AI Applications Need Guardrails: Here's How To Build Them

LanceDB: a unified storage layer for vector search, analytics, and model training

LanceDB: a unified storage layer for vector search, analytics, and model training

Building the NeurIPS LLM Efficiency Challenge Leaderboard

Building the NeurIPS LLM Efficiency Challenge Leaderboard

Supercharging Search with LLMs

Supercharging Search with LLMs

From Prototype to Product: Rapid iteration and ML model deployment at Dropbox

From Prototype to Product: Rapid iteration and ML model deployment at Dropbox

Read-write separation in Fennel

Read-write separation in Fennel

From Analytics to AI: How Gen AI Can Unlock Data Insights & Transform Decision-Making

From Analytics to AI: How Gen AI Can Unlock Data Insights & Transform Decision-Making

Operationalizing Data-Centric AI: Practical Tools to Quickly Improve ML Datasets

Operationalizing Data-Centric AI: Practical Tools to Quickly Improve ML Datasets

Introduction to LangChain and Retrieval Augmented Generation (RAG)

Introduction to LangChain and Retrieval Augmented Generation (RAG)

Creative Serving: A tour of Model Serving Strategies

Creative Serving: A tour of Model Serving Strategies

Production ML Serving & Monitoring with Kubernetes

Production ML Serving & Monitoring with Kubernetes

Dreambooth - Stable Diffusion for Custom Images

Dreambooth - Stable Diffusion for Custom Images

Evolved Structures: Generative Design and Digital Manufacturing at NASA

Evolved Structures: Generative Design and Digital Manufacturing at NASA

Overcoming the Challenges of Building GenAI Apps

Overcoming the Challenges of Building GenAI Apps

Data Versioning in Generative AI: A Pathway to Cost-Effective ML

Data Versioning in Generative AI: A Pathway to Cost-Effective ML

LLM, from Playgrounds to Production-ready Pipelines

LLM, from Playgrounds to Production-ready Pipelines

Goose, Chewbacca, & AI: Safely Flying the Generative AI Skies

Goose, Chewbacca, & AI: Safely Flying the Generative AI Skies

Hyper-Scaling Real-Time Personalization with Privacy via On-Device Computing

Hyper-Scaling Real-Time Personalization with Privacy via On-Device Computing

Airflow: Where data engineers and ML engineers meet

Airflow: Where data engineers and ML engineers meet

How NOT to get ML Models into Production To Maybe there's a path?

How NOT to get ML Models into Production To Maybe there's a path?

Getting Higher ROI on MLOps Initiatives

Getting Higher ROI on MLOps Initiatives

Building an end-to-end MLOPs Pipleline

Building an end-to-end MLOPs Pipleline

Training LLMs: Lessons from the Trenches

Training LLMs: Lessons from the Trenches

Chat with MLOps World: Engineering an LLM Application

Chat with MLOps World: Engineering an LLM Application

Creating the World's Premier Biological Foundation Model

Creating the World's Premier Biological Foundation Model

Retrieval Augmented Generation with LangChain: “ChatGPT for Your Data” (PT 1)

Retrieval Augmented Generation with LangChain: “ChatGPT for Your Data” (PT 1)

Solution for Black Box AI

Solution for Black Box AI

Feature Stores ≠ Storage

Feature Stores ≠ Storage

Generative AI: The Open Source Way

Generative AI: The Open Source Way

MemeGPT: Creating a Large Language Model to Generate Memes

MemeGPT: Creating a Large Language Model to Generate Memes

Retrieval Augmented Generation with LangChain: ChatGPT for Your Data (PT 2)

Retrieval Augmented Generation with LangChain: ChatGPT for Your Data (PT 2)

Oracles and Worker Bees: The future of AI models

Oracles and Worker Bees: The future of AI models

How Many Labelled Examples Do You Need for a BERT-sized Model to Beat GPT4 on Predictive Tasks?

How Many Labelled Examples Do You Need for a BERT-sized Model to Beat GPT4 on Predictive Tasks?

GenAI: Lessons Learned

GenAI: Lessons Learned

Scouter Models for Concept Drift Detection

Scouter Models for Concept Drift Detection

LLMs, Big Data, and Audio: Breaching an untapped Gold Mine

LLMs, Big Data, and Audio: Breaching an untapped Gold Mine

ML Platform Beyond Kaggle Paradigm

ML Platform Beyond Kaggle Paradigm

Train, and deploy Models, Lightning Fast

Train, and deploy Models, Lightning Fast

Workshop Sessions: Taking Aim at Bias in AI

Workshop Sessions: Taking Aim at Bias in AI

Workshop Sessions: MLOps with PyCaret

Workshop Sessions: MLOps with PyCaret

Machine Learning on Dynamic Graphs

Machine Learning on Dynamic Graphs

Workshop Sessions: Deploying an E2E ML Pipeline with AWS SageMaker - What Amazon didn't tell you

Workshop Sessions: Deploying an E2E ML Pipeline with AWS SageMaker - What Amazon didn't tell you

Workshop Sessions: Part 1 From Concept to Production - Template for the Entire ML Journey

Workshop Sessions: Part 1 From Concept to Production - Template for the Entire ML Journey

Workshop Sessions: Productionize with Kubeflow Orchestration with Feast in GCP

Workshop Sessions: Productionize with Kubeflow Orchestration with Feast in GCP

From 12 Months to 30 Days to AI Deployment: An MLOps Journey

From 12 Months to 30 Days to AI Deployment: An MLOps Journey

Training Models on the Cloud without Changing a Single Line of Code with GRID.AI

Training Models on the Cloud without Changing a Single Line of Code with GRID.AI

Connecting Data Scientists to roduction with the Tempo Python SDK

Connecting Data Scientists to roduction with the Tempo Python SDK

Machine Learning Tools: Skyline and RL-Scope

Machine Learning Tools: Skyline and RL-Scope

Workshop Sessions: Good, Fast, Cheap - How to Do Data Science with Missing Data

Workshop Sessions: Good, Fast, Cheap - How to Do Data Science with Missing Data

Workshop Sessions: Part 2 From Concept to Production - Template for the Entire ML Journey

Workshop Sessions: Part 2 From Concept to Production - Template for the Entire ML Journey

Workshop Sessions: Fixing Data Quality at Scale with Data Observability

Workshop Sessions: Fixing Data Quality at Scale with Data Observability

Systematic Approaches and Creativity; Building DoorDash's ML Platform During the Pandemic

Systematic Approaches and Creativity; Building DoorDash's ML Platform During the Pandemic

Workshop Sessions: Complete ML Lifecycle with MLflow - Learn it's Four Components

Workshop Sessions: Complete ML Lifecycle with MLflow - Learn it's Four Components

Workshop Session: ModelOps Presents - The First Step in Operationalizing AI Models

Workshop Session: ModelOps Presents - The First Step in Operationalizing AI Models

Unlock the power of MLFlow with a multi-cloud compute infrastructure

Unlock the power of MLFlow with a multi-cloud compute infrastructure

Workshop Sessions: Advanced Model Deployment with Seldon Deploy

Workshop Sessions: Advanced Model Deployment with Seldon Deploy

MLOps vs ModelOps – What’s the Difference and Why You Should Care

MLOps vs ModelOps – What’s the Difference and Why You Should Care

Workshop Sessions: Essential Workshop to Exploratory Data Analysis and Feature Engineering

Workshop Sessions: Essential Workshop to Exploratory Data Analysis and Feature Engineering

Workshop Sessions: 5 Governance Capabilities You Need in MLOps

Workshop Sessions: 5 Governance Capabilities You Need in MLOps

Workshop Sessions: Hands on Workshop - How to Build Pipelines with Kubeflow

Workshop Sessions: Hands on Workshop - How to Build Pipelines with Kubeflow

Workshop Sessions: Closing the Production Gap with MLOps

Workshop Sessions: Closing the Production Gap with MLOps

Is Your ML Model Trustworthy?

Is Your ML Model Trustworthy?

Workshop Sessions: MLOps with Orchestration: Your Highway to Accelerating Deployment of AI

Workshop Sessions: MLOps with Orchestration: Your Highway to Accelerating Deployment of AI

Iterative development workflows for building AI applications

Iterative development workflows for building AI applications

Challenges for ML Operations in a Fast Growing Company

Challenges for ML Operations in a Fast Growing Company

HoloClean and Kamino: Structured Learning for Private Data Generation

HoloClean and Kamino: Structured Learning for Private Data Generation

Multi-armed Bandits in Production at Stitch Fix

Multi-armed Bandits in Production at Stitch Fix

The Critical Missing Component in the Production ML Stack

The Critical Missing Component in the Production ML Stack

Marius: Machine Learning over Billion-Edge Graphs 10x faster and 5x cheaper

Marius: Machine Learning over Billion-Edge Graphs 10x faster and 5x cheaper

MLOps Platform Architecture for e2e ML Pipelines

MLOps Platform Architecture for e2e ML Pipelines

Production Machine Learning Monitoring Outliers, Drift, Explainers & Statistical Performance

Production Machine Learning Monitoring Outliers, Drift, Explainers & Statistical Performance

How Not to Let Your Data and Model Drift Away Silently

How Not to Let Your Data and Model Drift Away Silently

Model Monitoring: What, Why, and How

Model Monitoring: What, Why, and How

Developing a Data-Centric NLP Machine Learning Pipeline

Developing a Data-Centric NLP Machine Learning Pipeline

PANAMA In network Aggregation for Shared Machine Learning Clusters

PANAMA In network Aggregation for Shared Machine Learning Clusters

Workshop Sessions: Building an ML Platform from Scratch

Workshop Sessions: Building an ML Platform from Scratch

Security Audits for Machine Learning Attacks

Security Audits for Machine Learning Attacks

Building scalable ML services for rapid development in the health and wellness marketplace

Building scalable ML services for rapid development in the health and wellness marketplace

Breaking the Monolithic ML Pipeline with a Feature Store

Breaking the Monolithic ML Pipeline with a Feature Store

Quick Deploy Model Serving in Ranking Systems

Quick Deploy Model Serving in Ranking Systems

MLOps at Acerta - Automation of the Machine Learning life cycle for Manufacturing

MLOps at Acerta - Automation of the Machine Learning life cycle for Manufacturing

The AI Captain; A study of ML at the Edge

The AI Captain; A study of ML at the Edge

Next gen AI Processors and Why We Need to Think Differently About SW 2 0

Next gen AI Processors and Why We Need to Think Differently About SW 2 0

Introduction to Drifter ML

Introduction to Drifter ML

The Rapid Evolution of the Canonical Stack for Machine Learning

The Rapid Evolution of the Canonical Stack for Machine Learning

Scaling AI in Production with PyTorch

Scaling AI in Production with PyTorch

Diagnosing Failure Modes in Your ML Organization

Diagnosing Failure Modes in Your ML Organization

MLSecOps and Shift-left your security gears in Model Lifecycle

MLSecOps and Shift-left your security gears in Model Lifecycle

Catch Me If You Can: Keeping Up With ML Models in Production

Catch Me If You Can: Keeping Up With ML Models in Production

The Ops In MLOps

The Ops In MLOps

The Privacy Considerations of Production Machine Learning

The Privacy Considerations of Production Machine Learning

Machine Learning for Entertainment Challenges and Opportunities

Machine Learning for Entertainment Challenges and Opportunities

Opening Remarks - Sonya David

Opening Remarks - Sonya David

Hands on Workshop: Create Machine Learning Retraining Pipelines

Hands on Workshop: Create Machine Learning Retraining Pipelines

Continuously Improving Large Scale Recommenders with MLOps Tools and Practices

Continuously Improving Large Scale Recommenders with MLOps Tools and Practices

Machine Learning Optimizations and Strategy for Post-Covid Era

Machine Learning Optimizations and Strategy for Post-Covid Era

Parallelizing Your ETL with Dask on Kubeflow

Parallelizing Your ETL with Dask on Kubeflow

MLOPs with Dataiku Considerations For Model Deployment Monitoring

MLOPs with Dataiku Considerations For Model Deployment Monitoring

Building Real Time ML Pipelines with a Feature Store

Building Real Time ML Pipelines with a Feature Store

Building an ML Platform from Scratch

Building an ML Platform from Scratch

Using Reproducible Experiments To Create Better Machine Learning Models

Using Reproducible Experiments To Create Better Machine Learning Models

Shopping Recommendations at Pinterest

Shopping Recommendations at Pinterest

Deploying Model Artifacts in Hard Mode

Deploying Model Artifacts in Hard Mode

Robustness and Security for AI and the Dangerous Dismissal of Edge Cases

Robustness and Security for AI and the Dangerous Dismissal of Edge Cases

Reproducible Machine Learning How Not to Repeat the Past

Reproducible Machine Learning How Not to Repeat the Past

ML Observability: A Critical Piece in the ML Stack

ML Observability: A Critical Piece in the ML Stack

5 Techniques to Increase Adoption Rates via Interpretability

5 Techniques to Increase Adoption Rates via Interpretability

Build High Performance MLOps With ML Monitoring and AI Explainability

Build High Performance MLOps With ML Monitoring and AI Explainability

Machine Learning Infrastructure at Facebook Scale

Machine Learning Infrastructure at Facebook Scale

Best Practices Deploying MLOps Solutions in Enterprise

Best Practices Deploying MLOps Solutions in Enterprise

Intro

Intro

Intro to MLOps: Build, Deploy, and Retrain a Model in Production

Intro to MLOps: Build, Deploy, and Retrain a Model in Production

The Ops in MLOps

The Ops in MLOps

Automated Monitoring in Production for Continuous Model Improvements

Automated Monitoring in Production for Continuous Model Improvements

A Zero Downtime Set up for Models: How and Why

A Zero Downtime Set up for Models: How and Why

Data Prep Ops: The Last Frontier of MLOps

Data Prep Ops: The Last Frontier of MLOps

Build High Performance MLOps With ML Monitoring and AI Explainability

Build High Performance MLOps With ML Monitoring and AI Explainability

Leaner, Greener and Faster Pytorch Inference with Quantization

Leaner, Greener and Faster Pytorch Inference with Quantization

The Critical Missing Component in the Production ML Stack

The Critical Missing Component in the Production ML Stack

A Gentle Introduction to Sparsity with a Concrete Example

A Gentle Introduction to Sparsity with a Concrete Example

What's in the Box: Automatic ML Model Containerization

What's in the Box: Automatic ML Model Containerization

Deploy High Scale ML Models Without the Hustle

Deploy High Scale ML Models Without the Hustle

A Framework for a Successful Continuous Training Strategy

A Framework for a Successful Continuous Training Strategy

Deploying Efficient Data centric AI 100x Faster

Deploying Efficient Data centric AI 100x Faster