Machine Learning Pipelines Courses and Certifications

Alberta Machine Intelligence Institute Career Certificate

Machine Learning: Algorithms in the Real World

1073 ratings at Coursera

Work through an entire machine learning project: define the problem, identify appropriate data, train a classification algorithm, improve results, and deploy while avoiding common pitfalls.

• Coursera
• 4 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 4 courses

Amazon Web Services;  Amazon

Digital Classroom - Machine Learning Pipeline on AWS (Português)

Projeto prático para criar, treinar, avaliar, ajustar e implantar modelos de machine learning no Amazon SageMaker para resolver problemas empresariais reais.

• AWS Skill Builder
• 1 day
• Self-Paced
• Paid Course

Amazon Web Services;  Amazon

Digital Classroom - Machine Learning Pipeline on AWS (Español LATAM)

Curso práctico para recorrer una canalización completa de machine learning en AWS y resolver un problema empresarial con Amazon SageMaker.

• AWS Skill Builder
• 1 day
• Self-Paced
• Paid Course

Foundations of AI and Machine Learning

288 ratings at Coursera

Explore AI/ML infrastructure: build data pipelines, evaluate model frameworks and pretrained LLMs, and prepare models for production deployment with version control and scalable platforms.

• Coursera
• 5 weeks, 7 hours a week
• Self-Paced
• Paid Course

Machine Learning with Apache Spark

116 ratings at Coursera

Build regression, classification, and clustering models with SparkML: connect to Spark clusters, run ETL and Structured Streaming jobs, and assemble machine learning pipelines.

• Coursera
• 4 weeks, 4 hours a week
• Self-Paced
• Paid Course

Build and Operate Machine Learning Solutions with Azure

71 ratings at Coursera

Use the Azure Machine Learning Python SDK to train models, orchestrate pipelines, deploy real-time and batch inference services, tune hyperparameters, apply Fairlearn, and monitor data drift.

• Coursera
• 6 weeks, 5 hours a week
• Self-Paced
• Paid Course

CertNexus

Follow a Machine Learning Workflow

23 ratings at Coursera

Work through the machine learning workflow end to end: collect and analyze datasets, prepare data, train and refine models in Python, then finalize and deploy them.

• Coursera
• 6 weeks, 3 hours a week
• Self-Paced
• Paid Course

Career Certificate

Machine Learning Engineering for Production (MLOps)

Build and maintain production machine learning systems: design data lifecycles, modeling pipelines, and model deployment that keep running on continuously evolving data at minimum cost.

• Coursera
• 17 weeks, 6 hours a week
• Self-Paced
• Paid Course
• 4 courses

CertNexus

Finalize a Data Science Project

Wrap up a data science project: report model results to stakeholders, demonstrate models in a basic web app, and automate training, tuning, and deployment pipelines.

• Coursera
• 4 weeks, 3 hours a week
• Self-Paced
• Paid Course

Coursera

Engineer Features and Evaluate Models for Production

Build reproducible feature engineering pipelines with scikit-learn's ColumnTransformer, then use TensorBoard to diagnose overfitting and defend a production-ready model choice.

• Coursera
• 2 weeks, 1 hour a week
• Self-Paced
• Paid Course

Orchestrating a TFX Pipeline with Airflow

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you'll learn to create your own machine learning pipelines using TensorFlow Extended (TFX) and Apache Airflow as the orchestrator.

• Coursera
• 1 hour 30 minutes
• Self-Paced
• Paid Course

Edureka Career Certificate

Data Engineering for AI and ML Pipelines

Build end-to-end Databricks data pipelines: Spark and PySpark foundations, Delta Lake Bronze/Silver/Gold medallion layers, schema and quality enforcement, and feature stores feeding ML workflows.

• Coursera
• 8 weeks, 5 hours a week
• Self-Paced
• Paid Course
• 3 courses

Coursera

Building, Optimizing, and Validating Machine Learning Models

Train and compare models with ML APIs, tune hyperparameters, validate with k-fold and stratified sampling, explain results with SHAP, and automate end-to-end ML pipelines.

• Coursera
• 9 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Tidymodels in R: Building tidy machine learning models

Build a tidymodels pipeline in R: preprocess clinical data, then train and evaluate machine learning models that predict hospital readmissions.

• Coursera
• 2 hours 54 minutes
• Self-Paced
• Paid Course

Coursera Career Certificate

Level Up: Java-Powered Machine Learning

Build production ML systems in Java: apply SOLID design, optimize JVM memory, implement decision trees, entropy models and ensemble methods with Weka, Deeplearning4j, Maven and Gradle.

• Coursera
• 8 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 15 courses