Optimizing CI/CD Model Management and Evaluation Workflows with Weights & Biases
Learn to enhance CI/CD pipelines for efficient deployment of top production models using Weights & Biases. Explore key concepts and benefits of effective CI/CD pipelines, implement W&B Registry as a central repository for models and datasets, and constru…
GenAI in Production with MLflow
Explore recent advancements in GenAI application lifecycle management with MLflow in this 20-minute talk by Ben Wilson, Tech Lead of the AI OSS Ecosystem team at Databricks. Discover supported integrations for GenAI application tracking, evaluation, depl…
Evaluating Open-Source Local LLMs: A Comparative Analysis with Proprietary Giants for Developer Applications
Explore the world of open-source local large language models (LLMs) in this 40-minute conference talk from Devoxx. Dive into a comparative analysis between these models and proprietary giants, focusing on their performance, flexibility, and applicability…
Running Open Large Language Models in Production with Ollama and Serverless GPUs
Explore the deployment of open large language models in production environments using Ollama and serverless GPUs. Learn why companies are increasingly interested in running open models like Gemma and Llama, which offer full control over deployment, model…
MLOps Platform Architecture for End-to-End ML Pipelines
Explore Avast's MLOps journey, tooling, and cultural shift for enhanced ML productivity. Learn strategies for model tracking, storage, orchestration, and E2E deployments in large-scale ML pipelines.
Keeping Up With ML Models in Production: Mitigating Performance Drift
Learn strategies to mitigate ML model performance drift in production environments, incorporating non-technical collaborators and integrating ML into agile frameworks for long-term success.
Train and Deploy Models Lightning Fast
Accelerate AI innovation by mastering techniques for rapid model training and deployment, addressing scalability and security challenges in complex AI applications.
Quick Deploy Model Serving in Ranking Systems
Discover how to streamline model deployment in ranking systems using ElasticSearch plugins and MLeap bundles for efficient, error-free updates and seamless integration with existing infrastructure.
Data and Machine Learning at League of Legends
Explore unique data challenges in video games, focusing on model deployment and evolving ML stacks in League of Legends since 2009.
Production ML Serving and Monitoring with Kubernetes
Gain insights into building an MLOps deployment platform using Kubernetes, integrating key technologies for serving and monitoring complex deep learning models.
Rapid Iteration and ML Model Deployment at Dropbox
Discover rapid ML model iteration and deployment techniques used at Dropbox, reducing development time from weeks to under an hour.
ML Platform Beyond Kaggle Paradigm - Policy-Centric Approach for End-to-End Automation
Explore a policy-centric approach to ML platforms, extending beyond model-centric views to enhance engineering productivity and directly improve business metrics.
Introduction to Building ML Microservices - A Hands-On Approach with Music Industry Examples
Build ML microservices using MLServer and Seldon Core. Learn to create inference graphs for A/B testing, shadow deployment, and model monitoring with music industry examples.
Closing the Production Gap with MLOps
Explore MLOps strategies to accelerate deployment, monitor models, ensure governance, and manage the model lifecycle, including automated retraining and challenger models in production.
MLOps at Acerta - Automating the Machine Learning Lifecycle for Manufacturing
Automate the machine learning lifecycle for manufacturing, enhancing efficiency, scalability, and reproducibility. Learn to build a robust MLOps platform for production-grade systems.
MLOps with PyCaret - Practical Demo for Data Science Workflows
Supercharge your data science team's productivity with PyCaret, an open-source ML library for efficient model preparation and deployment in Python.
Creating Machine Learning Retraining Pipelines with Kubeflow and Kubernetes
Develop ML retraining pipelines using Kubernetes and Kubeflow. Master advanced techniques for efficient model updates and deployment in production environments.
How to Iterate and Productionize Machine Learning Models with Speed
Explore strategies to overcome deployment challenges and accelerate the productionization of machine learning models for faster impact.
From Concept to Production: Template for the Entire ML Journey - Part 1
Develop a production-ready ML pipeline from concept to deployment using Python. Gain hands-on experience with data prep, model training, and deployment on Google Cloud Platform.
MLOps with Dataiku - Considerations for Model Deployment and Monitoring
Explore key MLOps concepts, deployment readiness, model monitoring strategies, and how Dataiku streamlines MLOps processes for efficient AI industrialization.
How Not to Let Your Data and Model Drift Away Silently
Discover strategies to prevent data and model drift, ensuring ML models maintain performance and value over time in production environments.