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)

Setting Up H2O for Distributed Machine Learning

Running and Tracking Jobs in Azure Machine Learning

AWS Machine Learning Services for Beginners

Machine Learning Model Deployment with Gradio

Cloud Machine Learning: Training SVM Models with Azure ML and CLI

Ethical Machine Learning: Mitigating Bias and Harm in ML Pipelines

Azure Machine Learning: Build and Deploy Your First Project

AWS Streaming Data Pipelines for Real-Time Machine Learning

Streamlit App Deployment with Streamlit Sharing

Managing Azure Machine Learning Environments for Reproducible Models

AWS Machine Learning Cost Optimization and Governance

AWS Pre-Built Machine Learning Services for Beginners

MLOps on Azure: Automating ML Workflows with Data Factory

Building Machine Learning Pipelines with Kedro, MLflow, and Metaflow

Designing Data Infrastructure for Machine Learning Systems

Azure Machine Learning Pipelines: Building End-to-End Workflows

Adapting Machine Learning Pipelines for New Datasets

Deploying Custom ML Docker Containers on AWS Lambda

Designing Machine Learning Pipelines for System Design Interviews

Configuring Azure Machine Learning Pipelines with YAML

Designing End-to-End Machine Learning Pipelines

AWS Machine Learning: Integrating Cloud AI into Applications

AWS Multi-Account MLOps: Hub-and-Spoke Deployment Strategies

Data Privacy and Security in Machine Learning Pipelines