Machine Learning Pipelines Courses and Certifications

Coursera

Optimize LLM Costs & Streamline Processes

Discover how to reduce LLM costs through quantization and streamline ML workflows using Value-Stream Mapping for operational excellence in AI projects.

• Coursera
• 1 hour 47 minutes
• Self-Paced
• Paid Course

Coursera

Automate, Optimize, and Monitor ML Models

Master MLOps skills to detect model drift, implement automated retraining workflows, and create optimized pipelines that maintain performance at scale.

• Coursera
• 1 hour 31 minutes
• Self-Paced
• Paid Course

Coursera

Automate and Evaluate ML Pipeline Tests

Master ML pipeline testing with unit, integration, and smoke tests. Detect data drift, build automated regression suites with pytest, and deploy reliably using a reusable testing framework.

• Coursera
• 2 hours 35 minutes
• Self-Paced
• Paid Course

Coursera

Choose Cost-Effective ML Algorithms Fast

Learn to evaluate ML algorithms by resource utilization, benchmark XGBoost vs Random Forest, and select cost-effective models that are efficient, predictable, and production-ready.

• Coursera
• 1 hour 59 minutes
• Self-Paced
• Paid Course

Pragmatic AI Labs

Enterprise AIOps with Amazon Q Business

Master enterprise AIOps on AWS by deploying Amazon Q Business, managing AI costs with CloudShell automation, and implementing production MLOps pipelines and RAG workflows using Amazon Bedrock.

• Coursera
• 3 hours 32 minutes
• Self-Paced
• Paid Course

Coursera

Optimize ML Models: Hyperparameter Tuning

Master hyperparameter tuning to optimize ML models using GridSearchCV, design effective search spaces, run systematic experiments, and interpret cross-validated results for real-world ML pipelines.

• Coursera
• 2 hours 30 minutes
• Self-Paced
• Paid Course

Coursera

Building Vision and NLP Workflows with TensorFlow pipelines

Master building Vision and NLP pipelines using TensorFlow, Keras, and transformers—covering tokenization, embeddings, model evaluation, and deployment for real-world AI applications.

• Coursera
• 8 hours 56 minutes
• Self-Paced
• Paid Course

Amazon Web Services;  Amazon

Lab - Orchestrate a Machine Learning Workflow using Amazon SageMaker Pipelines and SageMaker Model Registry (Simplified Chinese)

构建基于大型语言模型的聊天机器人，集成Amazon Kendra实现检索增强生成，回答AWS服务相关问题。掌握部署LLM、使用Langchain和Lex V2等技术。

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

Databricks

Developing Scalable Machine Learning Pipelines for Gaming Industry

Explore scalable ML pipelines in gaming, leveraging Apache Spark for data-driven product development and improved user acquisition through advanced LTV modeling.

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

Databricks

Productionizing Machine Learning Pipelines with Databricks and Azure ML

Jumpstart ML projects with Databricks and Azure ML. Learn to build, track, and deploy production-ready pipelines using tools like MLflow, TensorFlow, and PyTorch for efficient, reproducible data science workflows.

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

Enabling Multi-user Machine Learning Workflows for Kubeflow Pipelines

Explore multi-user workflows for Kubeflow Pipelines using Kubernetes-native solutions. Learn how to implement access control, authentication, and authorization for machine learning platforms.

• YouTube
• 26 minutes
• Self-Paced
• Conference Talk

EuroPython Conference

Industrial Machine Learning Pipelines with Python & Airflow

Learn to build scalable, distributed machine learning data pipelines using Airflow with Celery backend. Gain insights on industry challenges, comparisons with other technologies, and practical knowledge for implementation.

• YouTube
• 28 minutes
• Self-Paced
• Conference Talk

Data Council

Testing Models and Data in CI/CD Pipelines for Machine Learning

Explore best practices for testing ML models in CI/CD pipelines. Learn to validate models and data using deepchecks, addressing common pitfalls and ensuring continuous relevance and trustworthiness.

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

Building Dynamic Machine Learning Pipelines with KubeDirector

Learn to build dynamic ML pipelines using KubeDirector on Kubernetes. Explore a taxi ride prediction example, from model training to deployment, and discover how KubeDirector's "Connections" feature enables always up-to-date ML models.

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

NDC Conferences

Scaling Machine Learning Pipelines in Cloud

Discover how Kubeflow simplifies machine learning pipelines, enabling scalable and portable deployments on diverse infrastructure. Learn to abstract non-ML tasks and focus on model development.

• YouTube
• 54 minutes
• Self-Paced
• Conference Talk