Machine Learning Pipelines Courses and Certifications

Amazon Web Services;  Amazon

AWS Foundations: How Amazon SageMaker Can Help

Explore Amazon SageMaker's role in machine learning pipelines, data formats, and model training. Learn to leverage key features for efficient ML workload management and automation.

• AWS Skill Builder
• On-Demand
• Free Certificate

Databricks

Data Microservices in Apache Spark Using Apache Arrow Flight

Explore efficient machine learning pipelines using Apache Arrow Flight in Spark, enabling faster data transport and simplified microservices for improved ML model production.

• YouTube
• 24 minutes
• On-Demand
• Free Video

Open Data Science

Managing Spatiotemporal Data Fusion at Scale Using The Geodesic Platform

Explore managing large-scale spatiotemporal data fusion using the Geodesic Platform. Learn about cloud-native solutions, data ingest challenges, and leveraging Pachyderm for traceability and pipeline management.

• YouTube
• 34 minutes
• On-Demand
• Free Video

Bryan Cafferky

The Azure Machine Learning Service - Introduction

Explore Azure ML Service's features for scaling, automating, and deploying machine learning pipelines. Learn how it enhances the data science process and addresses key challenges.

• YouTube
• 52 minutes
• On-Demand
• Free Video

IEEE

Defense Against the Dark Arts

Overview of adversarial example research in machine learning, exploring attacks, defenses, and future directions for enhancing AI security and robustness against malicious inputs.

• YouTube
• 57 minutes
• On-Demand
• Free Video

TensorFlow

TensorFlow Extended - TFX Overview and Pre-Training Workflow

Explore TensorFlow Extended's pre-training workflow, focusing on data validation and transformation for robust machine learning pipelines. Learn key components and best practices for efficient ML development.

• YouTube
• 32 minutes
• On-Demand
• Free Video

Enforcing Data Quality in Data Processing and ML Pipelines with Flyte and Pandera

Learn to enforce data quality in processing and ML pipelines using Flyte and Pandera, enhancing reliability and efficiency in data-driven projects.

• YouTube
• 47 minutes
• On-Demand
• Free Video

CNCF [Cloud Native Computing Foundation]

Scaling ML Pipelines with KALE - The Kubeflow Automated Pipeline Engine

Discover KALE, the Kubeflow Automated Pipeline Engine, for scaling ML pipelines. Learn how to transform Jupyter Notebook experiments into production-grade pipelines for efficient model training and real-time serving.

• YouTube
• 25 minutes
• On-Demand
• Conference Talk

CNCF [Cloud Native Computing Foundation]

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
• On-Demand
• Free Video

GOTO Conferences

Production Model Deployment

Explore strategies for deploying production models, including data sharing, service building, and AB testing. Learn about ML pipelines, engineering requirements, and effective handoff between data scientists and software engineers.

• YouTube
• 46 minutes
• On-Demand
• Conference Talk

ML Pipelines on Google Cloud

Learn to build and manage ML pipelines using TensorFlow Extended, Cloud Composer, and MLflow. Explore automation, reusability, and lifecycle management across multiple frameworks on Google Cloud.

• Google Cloud Skills Boost
• 2 hours 15 minutes
• On-Demand
• Free Certificate

Orchestrating a TFX Pipeline with Airflow

In this lab, you'll learn to create your own machine learning pipelines using TensorFlow Extended (TFX) and Apache Airflow as the orchestrator.

• Google Cloud Skills Boost
• 1-2 hours
• On-Demand
• Paid Course

Self Service ML Pipelines Using Dataprep and AutoML Tables

In this lab you will learn how to use Dataprep in conjunction with AutoML Tables to build and operate your machine learning pipelines.

• Google Cloud Skills Boost
• 1-2 hours
• On-Demand
• Paid Course

PyCon US

Reproducible and Maintainable Data Science Code with Kedro

Discover how Kedro, an open-source Python framework, transforms experimental data science code into production-ready ML pipelines, emphasizing reproducibility, maintainability, and modularity.

• YouTube
• 26 minutes
• On-Demand
• Conference Talk

EuroPython Conference

We Build a ML Pipeline After We Deploy

Discover essential strategies for building end-to-end machine learning pipelines, covering automation, engineering, debugging, and monitoring using Python libraries.

• YouTube
• 32 minutes
• On-Demand
• Conference Talk