| 1.1 · Collect and store data |
01 The ML Lifecycle and MLOps, 02 Ingestion, Streaming and Transformation, 08 Feature Engineering, Training and Experimentation |
| 1.2 · Perform data transformation, feature engineering, and pre-processing |
01 The ML Lifecycle and MLOps, 08 Feature Engineering, Training and Experimentation |
| 1.3 · Validate data quality and manage bias |
01 The ML Lifecycle and MLOps, 06 Evaluation and Responsible AI, 08 Feature Engineering, Training and Experimentation |
| 2.1 · Choose appropriate modeling approaches for ML and AI solutions |
01 The ML Lifecycle and MLOps, 03 Foundation Models and Generative AI |
| 2.2 · Train, fine-tune, and customize models for ML and AI solutions |
04 Prompt Engineering, RAG and Fine-Tuning, 08 Feature Engineering, Training and Experimentation |
| 2.3 · Analyze and evaluate the performance of ML and AI systems |
06 Evaluation and Responsible AI, 08 Feature Engineering, Training and Experimentation |
| 3.1 · Manage deployment infrastructure for ML and AI model types |
03 Foundation Models and Generative AI, 09 Model Deployment, Pipelines and Production Monitoring |
| 3.2 · Provision and configure resources for ML and AI workloads based on existing architecture and requirements |
09 Model Deployment, Pipelines and Production Monitoring |
| 3.3 · Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads |
09 Model Deployment, Pipelines and Production Monitoring |
| 4.1 · Monitor ML and AI model inference and performance |
06 Evaluation and Responsible AI, 09 Model Deployment, Pipelines and Production Monitoring |
| 4.2 · Optimize and manage ML and AI infrastructure costs and performance |
03 Foundation Models and Generative AI, 09 Model Deployment, Pipelines and Production Monitoring |
| 4.3 · Secure ML and AI workloads and model endpoints |
07 AI Security, Privacy and Governance, 09 Model Deployment, Pipelines and Production Monitoring |
