Machine Learning Pipelines Courses and Certifications

MLOps World: Machine Learning in Production

Leveraging LLMs to Build Mixed-Data ML Pipelines and Search GitHub with Natural Language

Discover how to integrate LLMs into production ML pipelines combining structured and unstructured data, with techniques for real-time feature engineering, latency optimization, and scaled retrieval systems.

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

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

Data Science Conference

Methodology of Credit Risk Modeling Using Kedro Pipeline

Discover how to streamline credit risk modeling in fintech startups using Kedro pipeline methodology to build robust models and enhance risk culture efficiently.

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

Coursera

Machine Learning Pipelines with Azure ML Studio

831 ratings at Coursera

Build end-to-end machine learning pipeline in Azure ML Studio using Adult Income Census data to predict income levels. Train, evaluate, and deploy a Two-Class Boosted Decision Tree classifier as a web service.

• Coursera
• 1 hour 48 minutes
• Self-Paced
• Paid Course

Extreme Gradient Boosting with XGBoost

34 ratings at DataCamp

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

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

Foundations of PySpark

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

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

Data Processing in Shell

Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.

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

Docker

How to Build Machine Learning Pipelines with Docker

Explore practical applications of Docker containers for rapid ML project prototyping, including face detection, sentiment analysis, and smart bot creation.

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

GitOps for Machine Learning Pipelines

Explore GitOps for ML pipelines using Kubeflow on Kubernetes. Learn to leverage GitOps for training, inference, monitoring, and testing in ML lifecycles, with a demo on deploying Kubeflow components via Argo CD.

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

Transforming Jupyter Notebooks to Reproducible Machine Learning Pipelines with Kale

Learn to convert Jupyter notebooks into scalable Kubeflow pipelines using Kale, enabling seamless deployment of ML workflows on Kubernetes for improved reproducibility and efficiency.

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

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

The Machine Learning Engineer

Azure ML: Pipelines with Python SDK for Data Science and Machine Learning

Create ML pipelines with Azure Machine Learning Studio's Python SDK for efficient data science and machine learning workflows on Azure.

• YouTube
• 57 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

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

Building Reusable Pipeline Functions

Discover how to build modular MLOps pipeline components for data processing, model training, evaluation, and persistence that form the foundation for automated ML workflows.

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