Канал: AI Infrastructure Alliance

Oscar Rovira – Mystic – Deploy fast, scalable and secure LLMs anywhere.

Oscar Rovira – Mystic – Deploy fast, scalable and secure LLMs anywhere.

NSQL: Closing the Performance Gap with Open Source SQL Generation with the Number Station SQL FMs

NSQL: Closing the Performance Gap with Open Source SQL Generation with the Number Station SQL FMs

Brad Munday – Modzy – Robust and Resilient Edge+Cloud Inference for LLMs

Brad Munday – Modzy – Robust and Resilient Edge+Cloud Inference for LLMs

Cameron Feenstra – Anzen – Reality Check: Overcoming the Hallucination Hurdle in Generative AI

Cameron Feenstra – Anzen – Reality Check: Overcoming the Hallucination Hurdle in Generative AI

Liam Li – HPE – Go beyond GPT-4: Optimize your model with Determined

Liam Li – HPE – Go beyond GPT-4: Optimize your model with Determined

Yaron Haviv – Iguazio – MLOps in the Era of Generative AI

Yaron Haviv – Iguazio – MLOps in the Era of Generative AI

Morgan McGuire – Weights & Biases – The Evolution of LLMs: Innovative Tooling for the Future

Morgan McGuire – Weights & Biases – The Evolution of LLMs: Innovative Tooling for the Future

Bindu Reddy – Abacus AI – Abacus.AI AI Chat - Use Generative AI to Build AI Systems at Scale

Bindu Reddy – Abacus AI – Abacus.AI AI Chat - Use Generative AI to Build AI Systems at Scale

David Smith – MLtwist – Generative AI Data Pipelines

David Smith – MLtwist – Generative AI Data Pipelines

Waleed Kadous – Anyscale – Beyond LLM v1 – how do we make LLM apps that last?

Waleed Kadous – Anyscale – Beyond LLM v1 – how do we make LLM apps that last?

Lilly Chen – Contenda – LLM Valley:A video game-themed talk through AI product markets and VC

Lilly Chen – Contenda – LLM Valley:A video game-themed talk through AI product markets and VC

Jerry Liu–LlamaIndex – Practical Data Considerations for building Production-Ready LLM Applications

Jerry Liu–LlamaIndex – Practical Data Considerations for building Production-Ready LLM Applications

Fabiana Clemente – YData – Data-Centric AI in the era of LLM - data as an unfair advantage

Fabiana Clemente – YData – Data-Centric AI in the era of LLM - data as an unfair advantage

Amber Roberts–Troubleshooting and Measuring Embedding/Vector Drift for Production Deployments of LMs

Amber Roberts–Troubleshooting and Measuring Embedding/Vector Drift for Production Deployments of LMs

Raza Habib, PhD – Humanloop – Evaluating LLMs in production

Raza Habib, PhD – Humanloop – Evaluating LLMs in production

AIShield – Navigating the GenAI Revolution: Ensuring Compliance and Security in the Era of LLMs

AIShield – Navigating the GenAI Revolution: Ensuring Compliance and Security in the Era of LLMs

Jimmy Whitaker–Pachyderm–From Crisis to Clarity: Ensuring Reproducibility for Enterprise AI at Scale

Jimmy Whitaker–Pachyderm–From Crisis to Clarity: Ensuring Reproducibility for Enterprise AI at Scale

Shreya Rajpal – Guardrails AI – Reining in the Wild West of AI Outputs

Shreya Rajpal – Guardrails AI – Reining in the Wild West of AI Outputs

Davit Buniatyan – PatentPT: How We Built an LLM-powered Solution with Enterprise-Grade Memory Agents

Davit Buniatyan – PatentPT: How We Built an LLM-powered Solution with Enterprise-Grade Memory Agents

Hemant Jain – Cohere – Efficiently serving fine-tuned LLMs

Hemant Jain – Cohere – Efficiently serving fine-tuned LLMs

Josh Reini – TruEra – Evaluating and Tracking LLM Experiments: Building Better LLM Apps with TruLens

Josh Reini – TruEra – Evaluating and Tracking LLM Experiments: Building Better LLM Apps with TruLens

Max Cembalest, Rowan Cheung – Arthur – LLMs for Evaluating LLMs

Max Cembalest, Rowan Cheung – Arthur – LLMs for Evaluating LLMs

Chinar Movsisyan – Harnessing Generative AI for Managing Insights about Model Performance with Manot

Chinar Movsisyan – Harnessing Generative AI for Managing Insights about Model Performance with Manot

Daniel Jeffries – AIIA – The Power of Agents and How to Build Them Right

Daniel Jeffries – AIIA – The Power of Agents and How to Build Them Right

Olivio Sarikas – How AI will redefine Culture and Entertainment

Olivio Sarikas – How AI will redefine Culture and Entertainment

The AI Times – Webinar #4 – Building the Perfect Dataset

The AI Times – Webinar #4 – Building the Perfect Dataset

The AI Times – Webinar #3 – The Promises and Possibilities of Generative AI

The AI Times – Webinar #3 – The Promises and Possibilities of Generative AI

The AI Times – Episode #2 – AI Predictions in 2023

The AI Times – Episode #2 – AI Predictions in 2023

The AI Times – Webinar #2 – Out of the Ivory Tower: AI in the Real World

The AI Times – Webinar #2 – Out of the Ivory Tower: AI in the Real World

The AI Times – Episode #1 – The Ethics of AI

The AI Times – Episode #1 – The Ethics of AI

Herbie Bradley – EleutherAI – Speeding up inference of LLMs with Triton and FasterTransformer

Herbie Bradley – EleutherAI – Speeding up inference of LLMs with Triton and FasterTransformer

Daniel Jeffries – AIIA – The Age of Industrialized AI and What It Means for the Future

Daniel Jeffries – AIIA – The Age of Industrialized AI and What It Means for the Future

Daniel Wibowo – TruEra – The 4 Types of ML Model Testing that Drive AI Performance

Daniel Wibowo – TruEra – The 4 Types of ML Model Testing that Drive AI Performance

Luca Antiga – Lightning AI – Scaling Up Foundation Models with Lightning

Luca Antiga – Lightning AI – Scaling Up Foundation Models with Lightning

Miriam Santos – DCAI Community – Auditing Data Quality with Pandas Profiling

Miriam Santos – DCAI Community – Auditing Data Quality with Pandas Profiling

Alex Havrilla – CarperAI – Open and Efficient Reinforcement Learning from Human Feedback

Alex Havrilla – CarperAI – Open and Efficient Reinforcement Learning from Human Feedback

Daniel Langkilde – Kognic – Optimal Representations for Human Feedback

Daniel Langkilde – Kognic – Optimal Representations for Human Feedback

Fabiana Clemente – YData – Scaling DataPrepOps

Fabiana Clemente – YData – Scaling DataPrepOps

Eric Landau – Encord – Active Learning & the ML Team of the Future

Eric Landau – Encord – Active Learning & the ML Team of the Future

Victor Sonck – ClearML – Hyperparameter Optimizing a Transformer on an Autoscaling Cluster

Victor Sonck – ClearML – Hyperparameter Optimizing a Transformer on an Autoscaling Cluster

Bernease Herman –WhyLabs–Truly scalable monitoring and observability for massive NLP and CV datasets

Bernease Herman –WhyLabs–Truly scalable monitoring and observability for massive NLP and CV datasets

Greg Throne – FeatureBase – Maintaining Up-to-the-Millisecond Freshness Across 20B Devices

Greg Throne – FeatureBase – Maintaining Up-to-the-Millisecond Freshness Across 20B Devices

Gilad Shaham – Iguazio – Production-First Approach: How to Scale up your ML Operations

Gilad Shaham – Iguazio – Production-First Approach: How to Scale up your ML Operations

Donghoon Han – Superb AI – Labeling and Managing Massive Datasets In The Age of Industrialized AI

Donghoon Han – Superb AI – Labeling and Managing Massive Datasets In The Age of Industrialized AI

Amber Roberts – Arize – Fairness Metrics and Bias Tracing in Production

Amber Roberts – Arize – Fairness Metrics and Bias Tracing in Production

Seth Clark – Modzy – Deploy once, run anywhere: A new approach for scaling production ML

Seth Clark – Modzy – Deploy once, run anywhere: A new approach for scaling production ML

Niccolò Zanichelli – OpenBioML – AI at scale in computational biology

Niccolò Zanichelli – OpenBioML – AI at scale in computational biology

Amit Phadke – Bosch AIShield – Securing AI systems at Scale

Amit Phadke – Bosch AIShield – Securing AI systems at Scale

Alejandro (Alex) Muller – Savvy AI – Mind the GAP

Alejandro (Alex) Muller – Savvy AI – Mind the GAP

Ed Shee – Seldon – Optimizing Inference For State Of The Art Python Models

Ed Shee – Seldon – Optimizing Inference For State Of The Art Python Models

Davit Buniatyan – Activeloop – Training Large Visual Models with Deep Lake

Davit Buniatyan – Activeloop – Training Large Visual Models with Deep Lake

Jennifer Prendki – Alectio – What the *%& is DataPrepOps?

Jennifer Prendki – Alectio – What the *%& is DataPrepOps?

The AI Times – Webinar #1 – AI at the Edge

The AI Times – Webinar #1 – AI at the Edge

ClearML–From a simple CLI tool to a powerful platform: your data management options in plain English

ClearML–From a simple CLI tool to a powerful platform: your data management options in plain English

Collibra – The Data Mesh: a new hope in the modern data stack

Collibra – The Data Mesh: a new hope in the modern data stack

Modzy – Leveraging Prediction Data for Model Retraining

Modzy – Leveraging Prediction Data for Model Retraining

Toloka – Search relevance evaluation using crowdsourcing

Toloka – Search relevance evaluation using crowdsourcing

Panel Discussion – Production Data Centric AI and What It Means

Panel Discussion – Production Data Centric AI and What It Means

Panel Discussion – The Impact of Academia on AI/ML Innovation

Panel Discussion – The Impact of Academia on AI/ML Innovation

Meaning – Lhotse: a speech data representation library for the modern deep learning ecosystem

Meaning – Lhotse: a speech data representation library for the modern deep learning ecosystem

MLCommons – MLCommons and public data

MLCommons – MLCommons and public data

Metaphor Data – Solving Data Discovery with Modern Metadata Platform

Metaphor Data – Solving Data Discovery with Modern Metadata Platform

Iguazio – Transforming Snowflake into an MLOps ‘Feature Factory’ using Iguazio

Iguazio – Transforming Snowflake into an MLOps ‘Feature Factory’ using Iguazio

HPE – Refining ML models with ML

HPE – Refining ML models with ML

Modulos – How to build better and fairer models with Data-Centric AI

Modulos – How to build better and fairer models with Data-Centric AI

Landing AI – AI – Moving Beyond the Software Industry

Landing AI – AI – Moving Beyond the Software Industry

Ternary Data – Data Engineering is the Foundation of Data-Centric AI

Ternary Data – Data Engineering is the Foundation of Data-Centric AI

Pachyderm – Rethinking ML Development - A Data-Centric Approach

Pachyderm – Rethinking ML Development - A Data-Centric Approach

Capital One – Enhancing ML with Data Profiles

Capital One – Enhancing ML with Data Profiles

Superb AI – Data Curation for Computer Vision 101

Superb AI – Data Curation for Computer Vision 101

Panel Discussion – Investments trends for Data-Centric AI domain

Panel Discussion – Investments trends for Data-Centric AI domain

FeatureBase – Bitmaps: A Tale Of Handling Scale Without Having To Scale

FeatureBase – Bitmaps: A Tale Of Handling Scale Without Having To Scale

Made With ML – Evaluating Machine Learning Models: Industry adoption and research trends

Made With ML – Evaluating Machine Learning Models: Industry adoption and research trends

Run:AI – Building the compute foundation for your AI infrastructure

Run:AI – Building the compute foundation for your AI infrastructure

YData – Hands-on Data-Centric AI: Data preparation tuning - why and how?

YData – Hands-on Data-Centric AI: Data preparation tuning - why and how?

Data-Centric AI Community – Data-Centric AI Community

Data-Centric AI Community – Data-Centric AI Community

Activeloop – Deep Lake: Optimizing Data Lakes for Deep Learning

Activeloop – Deep Lake: Optimizing Data Lakes for Deep Learning

Visual Layer – Large Image Datasets are a Mess

Visual Layer – Large Image Datasets are a Mess

AIIA – An Epic Overview of the AI Infrastructure Ecosystem of 2022

AIIA – An Epic Overview of the AI Infrastructure Ecosystem of 2022

Panel Discussion – Wrangling Big Datasets in the Real World

Panel Discussion – Wrangling Big Datasets in the Real World

LAION – Democratizing AI: Mastering the Massive Open Datasets that Power Imagen and Stable Diffusion

LAION – Democratizing AI: Mastering the Massive Open Datasets that Power Imagen and Stable Diffusion

WhyLabs – Can we adapt experimental data-centric AI principles for production ML systems?

WhyLabs – Can we adapt experimental data-centric AI principles for production ML systems?

Galileo – Principles for Building High Quality Models using High Quality Data at Scale

Galileo – Principles for Building High Quality Models using High Quality Data at Scale

TruEra – Fundamentals of ML Monitoring in 10 Minutes

TruEra – Fundamentals of ML Monitoring in 10 Minutes

DataRobot – Extreme ML Automation: A March Madness Case Study

DataRobot – Extreme ML Automation: A March Madness Case Study

Arize – Data Centric AI for Unstructured Use Cases

Arize – Data Centric AI for Unstructured Use Cases

Aporia – Why Cloud Native MLOps Rocks 🎸

Aporia – Why Cloud Native MLOps Rocks 🎸

Arize - How To Build Trust In Your Models In Production With ML Monitoring and Observability

Arize - How To Build Trust In Your Models In Production With ML Monitoring and Observability

Why Labs - Monitoring on Structured Data, Unstructured Data and Beyond

Why Labs - Monitoring on Structured Data, Unstructured Data and Beyond

Seldon - Explain the Behaviour of ML models - Illustrated with Anchor and SHAP Explainability

Seldon - Explain the Behaviour of ML models - Illustrated with Anchor and SHAP Explainability

TruEra - AI Monitoring and Explainability: The Critical Hidden Connection

TruEra - AI Monitoring and Explainability: The Critical Hidden Connection

Toloka - Easy ML monitoring with Toloka

Toloka - Easy ML monitoring with Toloka

Superwise - Why dashboards Kill Your Model Observability

Superwise - Why dashboards Kill Your Model Observability

Modzy - Monitoring Drift in Production

Modzy - Monitoring Drift in Production

Infuse AI - Data Observability Across Pipelines

Infuse AI - Data Observability Across Pipelines

Iguazio - Model Serving and Drift Detection with the Iguazio MLOps Platform

Iguazio - Model Serving and Drift Detection with the Iguazio MLOps Platform

Data Robot - Build Trustworthy AI with MLOps

Data Robot - Build Trustworthy AI with MLOps

Fiddler - Model Performance Management in Action

Fiddler - Model Performance Management in Action

Clear ML - Model Serving Monitoring and Traceability: The Bigger Picture.

Clear ML - Model Serving Monitoring and Traceability: The Bigger Picture.

Bosch AIShield - The Need to Secure AI

Bosch AIShield - The Need to Secure AI

Aporia - ML Monitoring on Edge Devices

Aporia - ML Monitoring on Edge Devices

Seldon - AIIA Model Serving Event - April 2022

Seldon - AIIA Model Serving Event - April 2022

Valohai - AIIA Model Serving Event - April 2022

Valohai - AIIA Model Serving Event - April 2022

Modzy - AIIA Model Serving Event - April 2022

Modzy - AIIA Model Serving Event - April 2022

Iguazio - AIIA Model Serving Event - April 2022

Iguazio - AIIA Model Serving Event - April 2022

Iguazio Experiment Tracking March 2022

Iguazio Experiment Tracking March 2022

Valohai - Experiment Tracking - March 2022

Valohai - Experiment Tracking - March 2022

Determined AI - Experiment Tracking - March 2022

Determined AI - Experiment Tracking - March 2022

ClearML - ExperimentTracking - March 2022

ClearML - ExperimentTracking - March 2022

SFL Scientific - MLOps Innovator Series

SFL Scientific - MLOps Innovator Series

WhyLabs - MLOps Innovators Series

WhyLabs - MLOps Innovators Series

Cognizant - MLOps Innovators Series

Cognizant - MLOps Innovators Series