Mobile Application
- User speaks to app
- Records audio clip
- Makes API call to server
Full Cycle Project
Full Cycle of a Machine Learning Project
Section titled “Full Cycle of a Machine Learning Project”Beyond Model Training
Section titled “Beyond Model Training”Training a model is just one part of building a valuable machine learning system. The complete project cycle involves multiple critical phases that ensure successful deployment and maintenance.
Complete ML Project Lifecycle
Section titled “Complete ML Project Lifecycle”1. Scope the Project
Section titled “1. Scope the Project”Define the problem and objectives
- Decide what you want to work on
- Set clear goals and success metrics
- Example: Speech recognition for voice search on mobile phones
2. Collect Data
Section titled “2. Collect Data”Gather training data for your system
- Decide what data is needed
- Collect audio recordings and transcripts
- Ensure data quality and representativeness
3. Train the Model
Section titled “3. Train the Model”Develop and optimize the learning algorithm
- Train speech recognition system
- Conduct error analysis and bias/variance analysis
- Iteratively improve model performance
4. Deploy in Production
Section titled “4. Deploy in Production”Make system available to users
- Implement in production environment
- Handle real-world traffic and usage
- Monitor system performance continuously
- Project Scoping → Data Collection → Model Training
- Model Training ↔ Data Collection (iterative improvement)
- Model Training → Production Deployment
- Production Deployment → Monitoring & Maintenance
- Monitoring → Model Training (continuous improvement)
Data Collection Insights
Section titled “Data Collection Insights”Iterative Data Collection
Section titled “Iterative Data Collection”- Initial training often reveals data gaps
- Error analysis guides additional data collection
- Example: Poor performance on car noise → collect more car audio data using data augmentation
Production Deployment Architecture
Section titled “Production Deployment Architecture”Inference Server Setup
Section titled “Inference Server Setup”Common deployment pattern:
Inference Server
- Receives audio via API
- Runs ML model prediction
- Returns text transcript
- Handles multiple concurrent requests
API Flow:
- Mobile app sends audio input (x) to inference server
- Server applies machine learning model
- Server returns prediction (ŷ) as text transcript
- Mobile app displays results to user
Software Engineering Considerations
Section titled “Software Engineering Considerations”Scalability Requirements
Section titled “Scalability Requirements”Small scale: Laptop deployment for handful of users Large scale: Data center infrastructure for millions of users
Technical Implementation Needs
Section titled “Technical Implementation Needs”- Reliable predictions: Consistent model performance
- Efficient processing: Optimized computational costs
- Scaling infrastructure: Handle growing user base
- Data logging: Store inputs and predictions (with user consent)
- System monitoring: Track performance and detect issues
Data Management
Section titled “Data Management”Logging capabilities (with privacy/consent considerations):
- Input data: Audio recordings, user queries
- Prediction outputs: Generated transcripts
- System metrics: Response times, error rates
- Usage patterns: Peak times, geographic distribution
Continuous Monitoring & Maintenance
Section titled “Continuous Monitoring & Maintenance”Why Monitoring is Critical
Section titled “Why Monitoring is Critical”Example scenario: Speech recognition system trained on historical data
- New celebrities become well-known
- Elections bring new politicians into prominence
- People search for names not in training set
- System performance degrades on new vocabulary
Data Drift Detection
Section titled “Data Drift Detection”Monitoring helps identify:
- When data distribution changes
- When model accuracy decreases
- When new patterns emerge in user behavior
- When retraining is needed
Model Updates
Section titled “Model Updates”Systematic approach:
- Detect performance degradation through monitoring
- Retrain model with updated data
- Validate improvements on test sets
- Deploy updated model to replace old version
MLOps: Machine Learning Operations
Section titled “MLOps: Machine Learning Operations”MLOps encompasses the systematic practices for building, deploying, and maintaining ML systems.
MLOps Responsibilities
Section titled “MLOps Responsibilities”- Reliable systems: Ensure consistent performance
- Scalable architecture: Handle user growth efficiently
- Comprehensive logging: Track system behavior
- Monitoring infrastructure: Detect issues early
- Update processes: Systematically improve models
Resource Optimization
Section titled “Resource Optimization”Large-scale considerations:
- Optimized implementations: Reduce computational costs
- Efficient serving: Minimize latency and resource usage
- Cost management: Balance performance and expenses
- Infrastructure planning: Prepare for traffic spikes
Team Structure Considerations
Section titled “Team Structure Considerations”Role Specialization
Section titled “Role Specialization”- ML Engineers: Focus on model training and algorithm development
- DevOps/MLOps Teams: Handle deployment and infrastructure
- Product Teams: Define requirements and user experience
- Data Teams: Manage data collection and quality
Collaborative Approach
Section titled “Collaborative Approach”Different teams may handle different phases, requiring:
- Clear handoff processes
- Shared understanding of requirements
- Consistent monitoring and evaluation metrics
- Regular communication about system performance
The full cycle emphasizes that successful ML systems require much more than just training good models - they need robust engineering, continuous monitoring, and systematic maintenance to deliver lasting value to users.