MLOps / Gen AI World is a unique collaborative event for the Ml/Gen AI community comprised of over 20,000 ML researchers, engineers, scientists and entrepreneurs across several disciplines.
Taken from the real-life experiences of practitioners, the Steering Committee has selected the top applications, achievements and knowledge-areas to highlight across the event.
Come expand your network with ML/GenAI experts and further your own personal & professional development in this exciting and rewarding field.
The MLOPS WORLD initiative is dedicated to helping promote the development of AI/ML/Gen AI effectively, efficiently, responsibly across all Industries. As well, to help practitioners, researchers and entrepreneurs fast-track their learning process and develop rewarding careers in the field.
MLOps / Gen AI World is a unique collaborative event for the Ml/Gen AI community comprised of over 20,000 ML researchers, engineers, scientists and
entrepreneurs across several disciplines.
Taken from the real-life experiences of practitioners, the Steering Committee has selected the top applications, achievements and knowledge-areas to highlight across the event.
Come expand your network with ML/GenAI experts and further your own personal & professional development in this exciting and rewarding field.
The MLOPS WORLD initiative is dedicated to helping promote the development of AI/ML/Gen AI effectively, efficiently, responsibly across all Industries. As well, to help practitioners, researchers and entrepreneurs fast-track their learning process and develop rewarding careers in the field.
Taken from the real-life experiences of practitioners, the Steering Committee has selected the top applications, achievements and knowledge-areas to highlight across the event.
Come expand your network with ML/GenAI experts and further your own personal & professional development in this exciting and rewarding field.
The MLOPS WORLD initiative is dedicated to helping promote the development of AI/ML/Gen AI effectively, efficiently, responsibly across all Industries. As well, to help practitioners, researchers and entrepreneurs fast-track their learning process and develop rewarding careers in the field.
40 + Technical Workshops and Industry Case Studies
Renaissance Austin Hotel, Marriot 9721 Arboretum Blvd,
Austin, TX 78759, United States
Thursday, November 7th, 2024 - 9:00 AM to 5:00 PM
Friday November, 8th, 2024 -9:00 AM to 6:00 PM MDT
Guided and selected by our committee to ensure the lessons and takeaways across these two days will help you put more models into production environments, effectively, responsibly, and efficiently.
Trusted by over 20,000 ML/GenAI practitioners.
Co-located alongside
Pass Includes
Access to All Workshops
Private and Co-Working Space On-Site
Food, Drinks and Evening Events
All Video Content Included
Join us if you're working on
Business & Stakeholder Alignment
Topics to Be Covered
- Bridging ML Infrastructure/Platform Work to Company Product Impact (Committee pain point)
- Stakeholder Business Alignment for AI Applications (Committee pain point)
- Stakeholder Trust in Model Results (Committee pain point)
- Finding an Easy Way to Connect ML Infrastructure/Platform Work to Company Product Impact (Committee pain point)
- AI/GenAI Strategy and Product Development (Committee area of interest)
- Supporting Women and Underrepresented Groups and Promoting Diversity in ML Teams (Committee area of interest)
- Leadership Challenges in the ML World (Committee area of interest)
- Managing Employee Adoption of AI Tools (Committee area of interest)
- Increasing Team Efficiencies and Effectiveness Using LLMs Internally (Committee area of interest)
- Ensuring AI Safety in Production Environments (Committee area of interest)
Deployment & Integration
Topics to Be Covered
- Deployment and Monitoring (Committee pain point)
- Deploying Multimodal Models in Production (Committee pain point)
- Overcoming Challenges to Perform Production-Grade Deployment of Models while Scaling Across Cloud Environments (Committee pain point)
- Moving from Experimentation and Proof-of-Concept to Production (Committee pain point)
- Moving Through MLOps Level 0 to Level 1 and Level 2 (Committee pain point)
- Models in Production, Evaluation (Committee pain point)
- Getting LLMs to Work with API Specifications (Committee pain point)
- Implementing End-to-End AI and ML Deployment Processes (Committee area of interest)
- Implementing a Modern MLOps Stack (Committee area of interest)
- Automating MLOps Processes with LLMs (Committee area of interest)
- Enhancing Observability in MLOps and AI Systems (Committee area of interest)
- Monitoring and Observing Multimodal Generative Models (Committee area of interest)
- Ensuring Continuous Delivery with MLOps Workflows (Committee area of interest)
- Comparing MLOps and Traditional Ops: Current Challenges and Solutions (Committee area of interest)
- Adopting MLOps Best Practices in AWS, GCP, and Azure (Committee area of interest)
- Exploring the Intersection of MLOps and Data Engineering (Committee area of interest)
- Utilizing Pyspark/Scala for Distributed ML Compute (Committee area of interest)
- Implementing Real-Time ML and Distributed Training/Inference (Committee area of interest)
- Scaling Inference and Deploying Heterogeneous Systems (Committee area of interest)
- Conducting Scoring and Inference in Streaming Mode (Committee area of interest)
- Using Vector Databases for Real-Time ML Inference (Committee area of interest)
- Scaling Vector Databases with Quantization Techniques (Committee area of interest)
- Achieving Real-Time Inference with LLMs (Committee area of interest)
- Optimizing Models and Conducting Online Testing (Committee area of interest)
- Efficiently Serving ML Models at Scale (Committee area of interest)
- Optimizing and Sustaining AI Model Training and Inference (Committee area of interest)
- Re-Training and Monitoring Models Effectively (Committee area of interest)
- Managing the Lifecycle of LLMs and Multimodal Models (Committee area of interest)
- Following Best Practices for Model Testing and Validation (Committee area of interest)
- Adapting to Events that Challenge ML Model Accuracy (Committee area of interest)
- Building Multi Modal MLOps/LLMOps Pipelines and Infrastructure (Committee area of interest)
- Simplifying and Automating MLOps with LLMs (Committee area of interest)
- Implementing Multimodal Use Cases in Production (Committee area of interest)
- Implementing Cybersecurity Best Practices for ML and GenAI in Production (Committee area of interest)
- Identifying and Implementing Effective RAG Architectures (Committee area of interest)
Ethics, Governance & Compliance
Topics to Be Covered
- Meeting Security and Compliance Requirements Across the Entire Lifecycle of ML Projects and Tooling (Committee pain point)
- Addressing Challenges Associated with E2E Lifecycle of CV/ML Models Including Dataset Creation and Model Validation, Regression Testing, Diagnostics and Debugging (Committee pain point)
- Overcoming Barriers to Generative AI Solutions Due to Data Privacy Related Issues (Committee pain point)
- Ensuring Transparency and Data Privacy in AI Solutions (Committee area of interest)
- Establishing Ethical Standards and Accountability in ML Model Evaluation (Committee area of interest)
- Implementing Ethical AI Practices with Practical Tools (Committee area of interest)
- Ensuring the Responsible Use of Generative AI (Committee area of interest)
Future Trends & Benchmarks
Topics to Be Covered
- Anticipating Challenges with Current Single-Model Infrastructure While Expanding to Support Multiple Languages (Committee pain point)
- Several Situations Where LLMs are Replacing/Augmenting Older ML Techniques: How to Compare Different Approaches (Committee pain point)
- Preparing Traditional Industry to Embrace the Change of AI (Committee pain point)
- Exploring Creative Applications of Large Language Models (Committee area of interest)
- Exploring Quantum Computing's Impact on ML (Committee area of interest)
- Driving Innovations in Generative AI (Committee area of interest)
- Leveraging Generative LLMs for Code Completion (e.g., GitHub Copilot) (Committee area of interest)
- Utilizing General-purpose Prompt-answer Systems like ChatGPT (Committee area of interest)
- Democratizing AI in Enterprise Environments Beyond ML Practitioners (Committee area of interest)
- Classic ML vs. Generative AI Use Cases Beyond RAG (Committee area of interest)
Infrastructure & Scalability
Topics to Be Covered
- Managing GPU Clusters (Committee pain point)
- Scaling Data and Model Pipelines (Committee pain point)
- Scaling Workflows, Tracking Lineage, and Ensuring a Smooth Dev Experience (Committee pain point)
- Model Training and Serving at Scale with GPU (Committee pain point)
- Distributed Training of Large Models (Committee pain point)
- Scaling Up LLMs (Committee pain point)
- Reusability and Scalability of Previously Proven ML Solutions for Similar but New Problems (Committee pain point)
- Using Internal Legacy Infrastructure to Train and Deploy ML Models, as an Alternative to Cloud (Committee pain point)
- Utilizing Pyspark/Scala for Distributed ML Compute (Committee area of interest)
- Scaling ML Model Training (Including with Spark and Ray) (Committee area of interest)
- Efficiently Serving ML Models at Scale (Committee area of interest)
- Ensuring Performance Monitoring Post-Deployment (Committee area of interest)
- Accelerating ML with Hardware-Agnostic Techniques (Committee area of interest)
- Running Models in Hybrid Cloud Environments (Committee area of interest)
- Strategies to Increase Efficiency and Reduce Carbon Footprint of Large Models (Committee area of interest)
- Infrastructure for Loading Models On-demand Based on Specific Use Case Requirements (Committee area of interest)
- Engineering Perspectives on Infra Costs for Projects like ChatGPT (Committee area of interest)
- Best Practices for Application Models with Dynamic Infrastructure (Committee area of interest)
Introduction to MLOps & GenAI
Topics to Be Covered
- Making Complex MLOps Setups Easy for Engineers with Little ML Experience (Committee pain point)
- Automating MLOps Processes with LLMs (Committee area of interest)
- Enhancing Observability in MLOps and AI Systems (Committee area of interest)
- Comparing MLOps and Traditional Ops: Current Challenges and Solutions (Committee area of interest)
- Simplifying and Automating MLOps with LLMs (Committee area of interest)
Model Dev, Training, Architecture
Topics to Be Covered
- Data Annotation (Committee pain point)
- Accessing and Managing Large Scale of Datasets in Generative AI Use Cases (Committee pain point)
- Improving ML Techniques Through Data Augmentation (Committee pain point)
- Continual Learning for Fine-Tuned Model to Adapt to the Growing (and Sometimes Drifting) Data (Committee pain point)
- Improving Transformer Architectures and Comparing Performance with Current SOTA Results (Committee pain point)
- Executing LLM Based Approaches Through Prompt Engineering or Fine-Tuning (Committee pain point)
- Commercializing Generative AI for ML Professionals (Committee area of interest)
- Implementing Generative AI Use Cases in Production (Committee area of interest)
- Effectively Fine-Tuning Large Language Models (Committee area of interest)
- Best Practices for Fine-Tuning Larger Models for Specific Applications (Committee area of interest)
- Implementing Federated Learning for IoT ML Projects (Committee area of interest)
- Managing the Lifecycle of LLMs and Multimodal Models (Committee area of interest)
- Applying Meta Learning and Graph Neural Networks in Production (Committee area of interest)
Performance Optimization & Efficiency
Topics to Be Covered
- Reducing Inference Costs (Committee pain point)
- Reducing Cost of Running in Production (Committee pain point)
- Optimization of Cost Associated with Infrastructure of Model Training and Inference (Committee pain point)
- Optimizing GPU Usage and Costs for ML Workloads (Committee area of interest)
- Ensuring Performance Monitoring Post-Deployment (Committee area of interest)
- Optimizing and Sustaining AI Model Training and Inference (Committee area of interest)
- Accelerating ML with Hardware-Agnostic Techniques (Committee area of interest)
- Running Models in Hybrid Cloud Environments (Committee area of interest)
- Strategies to Increase Efficiency and Reduce Carbon Footprint of Large Models (Committee area of interest)
- Engineering Perspectives on Infra Costs for Projects like ChatGPT (Committee area of interest)
- Best Practices for Application Models with Dynamic Infrastructure (Committee area of interest)
- Scaling Vector Databases with Quantization Techniques (Committee area of interest)
- Increasing Relevance of Search Across Different Platforms (Committee pain
Real world Apps & Case Studies
Topics to Be Covered
- Better Execution Model Validation, Governance, and Training (Committee pain point)
- Discovering the Decisive Features that Led to a Diagnostic Result (Committee pain point)
- Better Prompt and LLM Support by Big Cloud System (Committee pain point)
- Optimization of Digital Ads Campaign Strategies (Committee pain point)
- End User Case Studies Around Developing and Implementing MLOps Lifecycles (Committee area of interest)
- Commercializing Generative AI for ML Professionals (Committee area of interest)
- Implementing Generative AI Use Cases in Production (Committee area of interest)
- Leveraging Generative LLMs for Code Completion (e.g., GitHub Copilot) (Committee area of interest)
- Utilizing General-purpose Prompt-answer Systems like ChatGPT (Committee area of interest)
- Implementing Federated Learning for IoT ML Projects (Committee area of interest)
- Applying Meta Learning and Graph Neural Networks in Production (Committee area of interest)
- Supporting Women and Underrepresented Groups and Promoting Diversity in ML Teams (Committee area of interest)
- Ensuring AI Safety in Production Environments (Committee area of interest)
- Leadership Challenges in the ML World (Committee area of interest)
- AI use cases in finance ( Denys added based on confirmed)
Security & Privacy
Topics to Be Covered
- Enabling Security Patching for Open Source MLOps Tooling (Committee pain point)
- Inference and Training on the Edge (Committee pain point)
- Deploying Edge Computing, Heterogeneous ML Pipeline Orchestration, and Digital Twin Technology (Committee pain point)
- Implementing Cybersecurity Best Practices for ML and GenAI in Production (Committee area of interest)
- Ensuring Privacy and Security with Federated Learning (Committee area of interest)
- Incorporating Privacy by Design in ML Pipelines (Committee area of interest)
- Ensuring Transparency and Data Privacy in AI Solutions (Committee area of interest)
- Establishing Ethical Standards and Accountability in ML Model Evaluation (Committee area of interest)
- Implementing Ethical AI Practices with Practical Tools (Committee area of interest)
- Ensuring the Responsible Use of Generative AI (Committee area of interest)
Current Accepted Speakers
(more coming soon)
Thank you
Sponsors and Partners
Thank you to the sponsors of the 5th Annual MLOps World, taking place alongside the Generative AI Summit.
Platinum Sponsor
Gold Sponsors
Silver Sponsors
Bronze Sponsors
Start-Up Corner
Community Partners
Interested in exhibiting/sponsoring? Contact faraz@mlopsworld.com for details.
Get inspired
We’ve planned 4 tracks to help tackle different perspectives, from speakers around the world;
Tracks include:
Industry Case Studies
Technical Workshops
Research and Technical Lessons
Business Alignment
Get skilled-up
Designed for anyone working with ML/AI
Pick the workshops and sessions
to hone your skills.
Get familiarity with:
- Agentic Model Infrastructure for Scalability
- Different Types of LLM Evaluation Methods and which work best for your use-case
- Quantization, Distillation, & other Techniques for more Cost-effective, Efficient Model Hosting
- Different Types of RAG Implementation Strategies
and MORE!
Explore the city. Build your community
Designed for anyone working with ML/AI
"I can barely begin to explain how much your invite changed my life.
Not only did I learn a TON at the conference but the knowledge I gained completely changed the trajectory of my career"
Not only did I learn a TON at the conference but the knowledge I gained completely changed the trajectory of my career"
See 2024 MLOps/Gen AI World Steering Committee
Become a partner
Email for Brochure: faraz@mlopsworld.com
