PickAClass · AI, Tech & Engineering · AI & Machine Learning

MLOps (Machine Learning Operations)

Learn the principles and practices for deploying, monitoring, and maintaining machine learning models in production. Bridge the gap between data science and DevOps.

MLOps (Machine Learning Operations)

AWS Machine Learning Data Security and Governance

SageMaker Feature Store: Build Consistent ML Features

Interactive ML Dashboards with Dash and Plotly

Extending ML Pipelines: Integrating New Models

Building Machine Learning Pipelines for Data Analytics

Deploy Machine Learning Models on Azure

MLflow for Machine Learning Model Management on Azure

Azure Compute Clusters for Machine Learning

Deploy TensorFlow Models for Batch Inference on Azure

SageMaker Feature Store: Feature Management for Production ML

Deploying ML Models on Azure: Endpoints and Deployment Options

Foundational Data Science for AI and Machine Learning

Applied AI and Machine Learning on Google Cloud

MLOps Fundamentals for TinyML at Scale

MLOps Fundamentals for Cloud Deployment

Data Science with Azure: Build & Deploy ML Models

MLOps on AWS: Deploy and Manage Machine Learning Models

MLOps on Azure: Deploy and Manage Machine Learning Models

MLOps Foundations: Deploying Models with Python, Rust, and MLflow

GCP MLOps: Deploying AI and Machine Learning Models in Production

Cloud Machine Learning Engineering and MLOps Fundamentals

AWS SageMaker for Developers: Building Machine Learning Applications

GCP Data Pipeline Automation and Optimization for MLOps

BigQuery ML: Machine Learning and Inference for Data Analysts