Machine Learning Pipelines Courses and Certifications

DevConf

SDG Hub - An Open-Source Toolkit for Synthetic Data Generation and LLM Customization

Discover SDG Hub, Red Hat's open-source toolkit for customizing language models with synthetic data. Learn core components, teacher model selection, and build pipelines through real examples.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Introduction to Data Versioning with DVC

416 ratings at DataCamp

This course offers a comprehensive introduction to Data Version Control (DVC), a tool designed for efficient management and versioning of machine learning data.

• DataCamp
• 3 hours
• Self-Paced
• Free Trial Available

Extreme Gradient Boosting with XGBoost

273 ratings at DataCamp

Do you know the basics of supervised learning and want to use state-of-the-art models on real-world datasets?

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Building a Reproducible Model Workflow

Build a reproducible, end-to-end ML pipeline with MLflow, validating data with pytest and tracking experiments in Weights & Biases, then release a NYC rental-price predictor.

• Udacity
• 21 hours
• Self-Paced
• Paid Course

Google DeepMind: Build Your Own Small Language Model

In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level of machine learning development pipelines.

• DataCamp
• 6 hours
• Self-Paced
• Free Trial Available

ML Pipelines on Google Cloud - 한국어

여러 머신러닝 프레임워크의 파이프라인을 자동화·재사용하고 TFX, Cloud Composer, MLflow로 지속적 학습과 전체 수명 주기를 관리합니다.

• Coursera
• 4 hours
• Self-Paced
• Paid Course

ML Pipelines on Google Cloud - Français

Apprenez à orchestrer, automatiser et gérer des pipelines de machine learning en production avec TFX, CI/CD, plusieurs frameworks, Cloud Composer et MLflow.

• Coursera
• 4 hours
• Self-Paced
• Paid Course

ML Pipelines on Google Cloud - Português

Aprenda a criar, orquestrar e automatizar pipelines de machine learning com TFX, Cloud Composer e MLflow em vários frameworks.

• Coursera
• 4 hours
• Self-Paced
• Paid Course

BSidesLV

Incubated Machine Learning Exploits: Backdooring ML Pipelines Using Input-Handling Bugs

Explore vulnerabilities in ML pipelines through input-handling bugs, introducing incubated ML exploits and backdoors. Learn about security implications and guidelines for ML practitioners.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

DEFCONConference

Incubated ML Exploits: Backdooring Machine Learning Pipelines with Input Handling Bugs

Explore machine learning pipeline vulnerabilities and learn to identify, exploit, and defend against model backdoors through input handling bugs and systematic security approaches.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

GitOps for Machine Learning Pipelines

See how GitOps can manage machine learning work end to end, from training experiments and hyperparameter tuning to deploying a Kubeflow pipeline.

• YouTube
• 16 minutes
• Self-Paced
• Free Video

Canonical Ubuntu

Building Machine Learning Pipelines with Kubeflow

Master building ML pipelines using Kubeflow's components, from key considerations to best practices, and learn effective implementation strategies for scaling machine learning projects.

• YouTube
• 52 minutes
• Self-Paced
• Free Video

Career Certificate

Deploying ML Models in Production

Deploy machine learning models in production by building reusable pipelines, serving models with FastAPI, and automating retraining with Apache Airflow.

• CodeSignal
• 7 hours
• Self-Paced
• Free Certificate
• 3 courses

Career Certificate

Advanced Machine Learning on Google Cloud

1549 ratings at Coursera

Build and scale production ML models on Google Cloud for structured data, images, time series, and text, ending with recommendation systems.

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

Scalable Machine Learning on Big Data using Apache Spark

1252 ratings at Coursera

Scale machine learning to petabyte datasets with Apache Spark: work with RDDs, SparkSQL and Catalyst, and build SparkML pipelines for supervised and unsupervised learning.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course