Framing ML Problems

Project: @gortium’s Journey To Become a Machine Learning Engineer

Repository: LinkedIn_Google_Cloud_Pro_ML_Eng_Cert_Framing_ML_Problems

Key terms

• machine learning: Learning algorithms
• supervised learning: Learning from data with labels
• unsupervised learning: Learning from data without labels
• Reinforcement Learning: Learning from try and error and a reward function
• ML Solution Readiness: Assessment of the readiness of a ML solution
  • Data availability
  • Data quality
  • Data limitation
  • Responsable AI practices
• ML Uses Cases: A task a ML can solve with a business context. Exemple:
  • Segmentation
  • Fraud detection
  • Demand forecasting
• ML Problem Types:
  • Classification
  • Progression
  • clustering
• Business Success Criteria: Specific measurable goal or outcomes that indicate whether a ML solution is successful in addressing the business problem. Exemple:
  • Improving accuracy
  • Reducing a cost
  • Increasing efficiency
• Bias in ML:
  • Fair model predictions
  • Unbiased model predictions
  • Accurate model predictions
• Business Impact Assessment: Impact of a specific ml solution and to communicate to stakeholders
• Data readiness: Data is useful for ML training
  • Data availability
  • Data quality
  • Data limitation
• Responsible AI Practices: Design in a way that it is ethical and trust worthy to avoid negative consequences and legal consequences

Translating business challenges into ML use cases

Github for teaching MLOps

• Reproduccibility: Codespaces
• Access to GPU: Machine Learning Codespaces
• AI Coding Assistant: Copilot
• Continuous Integration & Deploy: Github Actions

MLOps?

1. WHY?: You need to retrain and redeploy ML model continuously
2. HOW?: Github codespaces, copilot, actions
3. WHAT?: Github actions link to any platform

Transclude of Framing-ML-Problems-2024-03-17-22.23.16.excalidraw

MLOps rule of 25

No silver bullet.

• 25% DevOps
• 25% Data engineering
• 25% MLOps
• 25% Stakeholder communication

Github Codespaces

AI augmented coding

Github Copilot

• Paid option

Twinny + Ollama

(Not in the course. My own research)

• Free
• Self-hosted
• Private

Hugging Face

Three main part:

• Models
• Dataset
• Spaces

Defining ML problems

Defining business success criteria

Identifying risk to feasibility of ML solutions