AI / ML Development
The development of artificial intelligence (AI) involves systems that make predictions, classifications, and automate processes based on data provided. Designing, building, training, deploying and maintaining are all essential steps within the Artificial Intelligence and Machine Learning development processes, Learn from data systems and processes. To assist, here’s a structured breakdown on the development of AI/ML that you either are starting from scratch or working on an advanced systems.
- Customer churn prediction (classification)
- Sales forecasting (time series)
- Chatbots / virtual assistants (NLP)
- Medical image diagnosis (computer vision)
- Fraud detection (anomaly detection)
AI / ML Development Lifecycle
Problem Definition
Define the objective: classification, regression, clustering, NLP, vision, etc.
Identify performance metrics (accuracy, F1-score, MSE, etc.)
Identify performance metrics (accuracy, F1-score, MSE, etc.)
Data Collection & Preparation
Gather structured or unstructured data
Clean and preprocess: handle missing values, normalize, encode
Perform feature engineering
Split data into train/validation/test sets
Clean and preprocess: handle missing values, normalize, encode
Perform feature engineering
Split data into train/validation/test sets
Model Selection
Classification/Regression: Logistic Regression, Random Forest, XGBoost
NLP: Transformers (BERT, GPT), RNNs
Computer Vision: CNNs, ResNet, YOLO
Unsupervised: k-Means, DBSCAN, PCA
Reinforcement Learning: DQN, PPO
NLP: Transformers (BERT, GPT), RNNs
Computer Vision: CNNs, ResNet, YOLO
Unsupervised: k-Means, DBSCAN, PCA
Reinforcement Learning: DQN, PPO
Model Training & Tuning
Train using frameworks like:
- scikit-learn (traditional ML)
- TensorFlow / Keras (deep learning)
- PyTorch (flexible deep learning)
Tune hyperparameters (e.g., grid search, Optuna, Bayesian optimization)
Evaluate with validation data
- scikit-learn (traditional ML)
- TensorFlow / Keras (deep learning)
- PyTorch (flexible deep learning)
Tune hyperparameters (e.g., grid search, Optuna, Bayesian optimization)
Evaluate with validation data
Model Evaluation
Use cross-validation for robustness
Check for bias/variance, overfitting/underfitting
Use confusion matrix, ROC curve, or regression plots
Check for bias/variance, overfitting/underfitting
Use confusion matrix, ROC curve, or regression plots
Deployment
Save model (e.g., joblib, pickle, ONNX, SavedModel)
Serve model via:
Flask or FastAPI (REST API)
Docker (containerization)
Cloud platforms (AWS SageMaker, GCP AI Platform, Azure ML)
Set up A/B testing and canary deployment (if needed)
Serve model via:
Flask or FastAPI (REST API)
Docker (containerization)
Cloud platforms (AWS SageMaker, GCP AI Platform, Azure ML)
Set up A/B testing and canary deployment (if needed)
Monitoring & Maintenance
Track performance in production
Monitor data drift, model degradation
Implement retraining pipelines
Use MLflow or DVC for experiment tracking
Monitor data drift, model degradation
Implement retraining pipelines
Use MLflow or DVC for experiment tracking