Machine Learning in Production

2.

Project Scoping and System Design

2.1.

Framing Business Problems as ML Problems

2.1.1.

Identifying Use Cases Suitable for ML

2.1.2.

Defining Problem Statements

2.1.3.

Assessing Data Availability and Suitability

2.1.4.

Understanding Business Context

2.2.

Defining Success Metrics

2.2.1.

2.2.1.1.

2.2.1.2.

2.2.1.3.

User Engagement

2.2.1.4.

Customer Satisfaction

2.2.2.

ML Performance Metrics

2.2.2.1.

Classification Metrics

2.2.2.1.1.

2.2.2.1.2.

Precision and Recall

2.2.2.1.3.

2.2.2.1.4.

2.2.2.2.

Regression Metrics

2.2.2.2.1.

Mean Squared Error

2.2.2.2.2.

Mean Absolute Error

2.2.2.2.3.

2.2.2.3.

2.2.2.4.

Multi-objective Optimization

2.3.

Establishing System Requirements

2.3.1.

Performance Requirements

2.3.1.1.

Latency and Throughput Constraints

2.3.1.2.

Real-time vs Batch Requirements

2.3.1.3.

User Experience Considerations

2.3.2.

Scalability Needs

2.3.2.1.

Anticipated Load

2.3.2.2.

Horizontal and Vertical Scaling

2.3.2.3.

Peak Load Handling

2.3.3.

Cost and Budget Considerations

2.3.3.1.

Infrastructure Costs

2.3.3.2.

Maintenance and Operational Costs

2.3.3.3.

Resource Optimization

2.3.4.

Reliability and Availability Requirements

2.3.4.1.

Uptime Requirements

2.3.4.2.

Fault Tolerance

2.3.4.3.

Disaster Recovery

2.4.

Feasibility Analysis

2.4.1.

Technical Feasibility

2.4.1.1.

Technology Stack Assessment

2.4.1.2.

Resource Requirements

2.4.2.

Data Feasibility

2.4.2.1.

Data Quality Assessment

2.4.2.2.

Data Volume and Velocity

2.4.3.

Organizational Readiness

2.4.3.1.

Team Skills and Capabilities

2.4.3.2.

Infrastructure Maturity

2.5.

Establishing Baseline Models

2.5.1.

Simple Heuristics or Rule-based Models

2.5.2.

Benchmarking Against Existing Solutions

2.5.3.

Setting Initial Performance Expectations

2.5.4.

Minimum Viable Product Definition

Previous

1. Introduction to MLOps

Go to top

Next

3. Data Engineering for Production