WalkSelf · 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)

Workflow Automation with n8n: From Basics to AI Agents

Scaling TinyML: MLOps and Deployment for Edge Devices

Deploying Machine Learning Models on Heroku

Serverless Machine Learning: Deploying Models on AWS Lambda

Predictive Modeling and Batch Pipelines with Cloud Dataflow

Saving and Packaging Machine Learning Models for Production

PySpark Model Pipelines for Cloud Data Lakes

Deploying Scalable Machine Learning Models with Python and Flask

Machine Learning Model Deployment with Serverless and Managed Services

Building Batch Machine Learning Pipelines with Scikit-Learn

Deploying ML Models: Web Services and Model Persistence

Deploying Scalable Web Endpoints for Machine Learning Models

Deploying Machine Learning Models as Web Endpoints with Python

Serverless Machine Learning: Automated Model Updates and Refreshes

Real-Time Data Pipelines with Cloud Dataflow

PySpark MLlib: Building Batch Pipelines for Predictive Models

AWS Machine Learning and Analytics Services for Beginners

Predictive Model Prototyping for Scalable Data Pipelines

Data Science Pipelines: Models, Tools, and Cloud Environments

Automating ML Pipelines with Airflow and Kubernetes

Deploying Scalable Machine Learning Models with Serverless Cloud Functions

Smoke Testing for Machine Learning Pipelines

Publishing ML Predictions to Cloud Datastore and BigQuery

Building Scalable Machine Learning Pipelines with PySpark and MLlib