LearnFlat · 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)

Designing ML Pipelines with SageMaker and CI/CD

Deploying Serverless Machine Learning Workflows with AWS Step Functions

Designing Scalable ML Model Serving Architectures

Scalable Machine Learning with H2O Clusters and AutoML

AWS Machine Learning Services: Core Concepts and Knowledge Review

AWS Machine Learning Services: A Beginner's Guide

Machine Learning Deployments with Azure CLI

Essential Data Preparation for AWS Machine Learning

Kubeflow for Machine Learning: Setup and Workflow Management

Machine Learning Model Monitoring with SageMaker

Azure Cloud and Machine Learning for Beginners

Reproducible Machine Learning: Building Reliable ML Pipelines

Build Python ML Pipelines: Core Structure

AWS Step Functions for Machine Learning Orchestration

Azure Machine Learning Workspace Setup with CLI and Portal

Data Collection and Ingestion for Scalable Machine Learning Systems

AWS Machine Learning and Intelligent Search Foundations

Serverless Machine Learning: Train and Evaluate Models with AWS Lambda

Building Machine Learning Pipelines in PySpark

ML Data Engineering Foundations with AWS

AWS Certified Machine Learning Engineer Associate Exam Prep

AWS Machine Learning Engineering: Model Development and SageMaker

Practical Data Bias Mitigation in ML Pipelines

SageMaker Studio: Configuring Secure ML Environments