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Computer Science
Artificial Intelligence
Machine Learning
Machine Learning Pipelines
1. Fundamentals of ML Pipelines
2. Core Stages of an ML Pipeline
3. Designing and Building ML Pipelines
4. Tools and Frameworks for ML Pipelines
5. Operationalizing Pipelines
6. Advanced Topics in ML Pipelines
Tools and Frameworks for ML Pipelines
Foundational Libraries
Scikit-learn Pipelines
Pipeline Construction
Sequential Processing
Parallel Processing
Custom Transformers
Advanced Components
FeatureUnion
ColumnTransformer
Pipeline Composition
Integration Patterns
Data Processing Libraries
Pandas
Data Manipulation
Data Transformation
Pipeline Integration
NumPy
Numerical Computing
Array Operations
Performance Optimization
Dask
Parallel Computing
Distributed Processing
Lazy Evaluation
Open-Source Orchestration Frameworks
Apache Airflow
Core Concepts
DAGs
Operators
Tasks
Schedulers
Advanced Features
Dynamic DAG Generation
Task Dependencies
Error Handling
Monitoring and Alerting
Integration Ecosystem
Kubeflow Pipelines
Kubernetes-native Pipelines
Component Development
Pipeline Authoring
Experiment Management
Multi-tenancy Support
MLflow
Experiment Tracking
Model Registry
Model Deployment
Project Management
Integration Capabilities
TensorFlow Extended
TFX Components
Data Validation
Transform
Trainer
Evaluator
Pipeline Orchestration
Production Deployment
Prefect
Modern Workflow Engine
Dynamic Task Generation
State Management
Cloud Integration
ZenML
ML Pipeline Abstractions
Stack Management
Integration Framework
Reproducibility Features
Cloud-Based Managed Services
Amazon Web Services
SageMaker Pipelines
Pipeline Authoring
Step Functions Integration
Managed Execution
Step Functions
Batch Processing
Lambda Functions
Google Cloud Platform
Vertex AI Pipelines
Kubeflow Pipelines Integration
Managed Services
AutoML Integration
Cloud Composer
Dataflow
Cloud Functions
Microsoft Azure
Azure Machine Learning Pipelines
Designer Interface
SDK Integration
Compute Management
Azure Data Factory
Azure Functions
Azure Batch
Specialized Tools
Data Processing Tools
Apache Spark
Apache Beam
Dask
Ray
Model Serving Frameworks
TensorFlow Serving
TorchServe
MLflow Models
Seldon Core
Monitoring and Observability
Prometheus
Grafana
Weights & Biases
Neptune
Tool Selection and Comparison
Evaluation Criteria
Functionality Requirements
Scalability Needs
Integration Capabilities
Cost Considerations
Comparative Analysis
Feature Comparison
Performance Benchmarks
Ecosystem Maturity
Migration Strategies
Tool Migration Planning
Data Migration
Process Migration
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3. Designing and Building ML Pipelines
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5. Operationalizing Pipelines