Becoming an ML Platform Power Builder with ML Observability
Discover best practices for building effective ML platforms. Learn key components, justification strategies, and how ML infrastructure differs from software infrastructure.
CyclOps: A Framework for Data Extraction, Model Evaluation and Drift Detection in Clinical Machine Learning
Explore CyclOps, a unified framework for healthcare ML, enabling data extraction, model evaluation, and drift detection in clinical settings. Streamline development of robust AI models.
Airflow: Where Data Engineers and ML Engineers Meet
Explore Apache Airflow's role in operational machine learning with a real-world demo and reference implementation of GenAI in production.
Scouter Models for Concept Drift Detection in MLOps
Explore techniques for detecting concept drift in ML models without true labels using Scouter models. Learn to build, manage, and implement this critical MLOps monitoring strategy.
Introduction to Model Deployment with Ray Serve
Learn to deploy ML models using Ray Serve. Explore Ray Core APIs, create scalable architectures, and integrate with MLflow and FastAPI. Gain hands-on experience in distributed computing and model deployment.
Exploring the Impact of Agentic Workflows - MLOps Podcast Episode 268
Dive into the world of AI agents in production with insights on deployment strategies, real-world applications, and practical advice for integrating AI into everyday systems while maintaining human oversight.
How to Systematically Test and Evaluate LLM Apps - MLOps Podcast
Dive into systematic testing and evaluation of LLM applications with insights on defining metrics, tracking experiments, and implementing comprehensive unit tests for confident deployment.
TensorFlow Extended
Learn TensorFlow Extended (TFX) for production ML pipelines, covering workflows, metadata, distributed processing, model understanding, and continuous retraining for effective MLOps implementation.
MLOPS
Learn MLOps fundamentals, implementation, deployment, and continuous integration using tools like MLFlow in this comprehensive program.
Understanding the Machine Learning Process and Embedding Models into Apps
Learn to build ML models with Azure ML and VS Code, operationalize them in apps, and implement MLOps practices. Covers PyTorch Lightning, model prediction, and integration with DevOps and Kubernetes.
Azure Machine Learning Batch Endpoints - Episode 42
Explore Azure Machine Learning Batch Endpoints with Tracy Chen. Learn about batch inference, deployment, invocation, and setting up CI/CD pipelines for efficient large-scale data processing.
High-Level Introduction to MLOps - AI Show Episode 33
Microsoft data scientists discuss MLOps challenges, principles, and best practices, offering insights on successful implementation and tips for overcoming common obstacles in AI projects.
MLOps - Unifying MLOps at Microsoft
Explore MLOps v2 fundamentals, approach, and architectures with Microsoft experts. Learn about unifying MLOps in Azure, customer challenges, and see new features demonstrated.
AI Show Live - Unifying MLOps at Microsoft
Explore MLOps v2 fundamentals, approach, and architectures with Microsoft experts. Learn about unifying MLOps in Azure, see live demos, and discover how to simplify, secure, and accelerate machine learning operations.
Writing Unit Tests for Data Science Code
Learn best practices for unit testing data science code, including handling data inputs, model training, and evaluation. Gain insights on bridging software engineering and data science for more robust ML applications.
Putting MLOps into Practice
Streamline your machine learning lifecycle with MLOps best practices, from project initiation to model deployment and monitoring. Learn practical implementation strategies and reference architectures.
MLOps Platforms From Zero - Databricks, MLFlow-MLRun-SKLearn
Comprehensive guide to building MLOps platforms using Databricks, AutoML, SKLearn, and AWS. Covers end-to-end process from project setup to model deployment, including CI/CD, experimentation, and containerization.
MLOps Crash Course for Beginners
Learn to create and deploy reproducible MLOps pipelines, covering key concepts like continuous training, drift detection, and model deployment for real-world data science value beyond Jupyter notebooks.
MLOps Masterclass - Theory to DevOps to Cloud-Native to AutoML
Comprehensive guide to MLOps: from theory to implementation, covering DevOps, cloud-native solutions, and AutoML. Learn to build, deploy, and manage machine learning systems across various platforms and technologies.
AI & ML Workflows with Kubernetes
Explore strategies to accelerate and automate AI/ML workflows using Kubernetes, leveraging open-source tools like Jupyter Notebooks and TensorFlow for efficient development and deployment.
Cloud Directions, MLOps and Production Data Science
Explore cloud directions, MLOps, and production data science with Prof. Hellerstein. Learn key challenges, lifecycle management, and innovative solutions for scaling data-intensive applications in the cloud.