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)

Machine Learning Model Deployment: Building Scalable Web APIs

Designing Efficient Machine Learning Training Pipelines

Cloud Dataflow and Batch Data Modeling with Python

Machine Learning Model Deployment: Building Web Endpoints with Python

Serverless Model Deployment on AWS and GCP

Applied Data Science: Building Scalable Model Pipelines

Building Clean Machine Learning Workflows with Scikit-Learn Pipelines

Streaming Model Workflows for Real-Time ML Pipelines

Real-Time Machine Learning Pipelines with Kafka, PySpark, and Scikit-Learn

Automated Machine Learning: Guide to AutoML Tools and Workflows

Cloud Machine Learning Engines: Comparing AWS, Azure, and Google Cloud

Serverless Machine Learning Deployment with AWS API Gateway

Deploying Machine Learning Apps with Streamlit Cloud and PyCaret

Deploying Machine Learning Models with Dash Web Apps

Streamlit for Machine Learning: Build and Deploy Web Apps

Batch Model Pipelines with Cloud Dataflow and Apache Beam

ML Deployment and Inference Orchestration on AWS

Designing Integrated Machine Learning Pipelines with Azure Data Factory

YOLOv7 Workflow: From Custom Dataset to Edge Deployment

Building Machine Learning Training Pipelines

Scaling Deep Learning: Distributed PyTorch on Azure

Securing AWS Machine Learning Pipelines with IAM and VPCs

Introduction to SageMaker and the Machine Learning Lifecycle

Hyperparameter Tuning with Azure Machine Learning Sweep Jobs