data-ai-systems

name	data-ai-systems
description	Master data engineering, machine learning, and AI systems including Python data science, ML frameworks, analytics, and MLOps. Use when working with data pipelines, building ML models, or implementing AI solutions.

Data & AI Systems

Quick Start

Data science and ML require integrated knowledge across multiple domains:

# Machine Learning Example with Scikit-learn
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score

# Load and prepare data
X_train, X_test, y_train, y_test = train_test_split(
    features, labels, test_size=0.2, random_state=42
)

# Train model
model = RandomForestClassifier(n_estimators=100, max_depth=10)
model.fit(X_train, y_train)

# Evaluate
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
precision = precision_score(y_test, predictions)

Python Data Science Stack

Core Libraries

NumPy & Pandas

• NumPy: Numerical computing, arrays, linear algebra
• Pandas: Data manipulation, DataFrames, time series analysis
• Data Cleaning: Handling missing values, outliers, data normalization
• Feature Engineering: Creating meaningful features from raw data

Visualization

• Matplotlib: Low-level plotting library
• Seaborn: Statistical data visualization
• Plotly: Interactive, web-based visualizations
• Tableau/Power BI: BI tools for dashboards

Machine Learning

Scikit-learn Framework

• Supervised Learning: Regression, classification, ensemble methods
• Unsupervised Learning: Clustering, dimensionality reduction
• Model Selection: Cross-validation, hyperparameter tuning
• Pipeline: Automated ML workflows

Deep Learning Frameworks

• TensorFlow/Keras: Neural networks, computer vision, NLP
• PyTorch: Dynamic computation graphs, research-friendly
• Transfer Learning: Pre-trained models (BERT, ResNet, GPT)
• Fine-tuning: Adapting models to specific tasks

Data Engineering

ETL Pipelines

• Extraction: APIs, databases, files, real-time streams
• Transformation: Data cleaning, aggregation, feature engineering
• Loading: Data warehouses, data lakes, databases
• Orchestration: Airflow, Prefect, Dbt

Big Data Tools

• Apache Spark: Distributed data processing
• Hadoop: Distributed storage and computation
• Kafka: Real-time data streaming
• Apache Hive: Data warehouse queries

Statistics & Analytics

Statistical Methods

• Descriptive Statistics: Mean, median, variance, distribution
• Hypothesis Testing: A/B testing, statistical significance
• Regression Analysis: Linear, logistic, polynomial regression
• Time Series: ARIMA, seasonal decomposition, forecasting

Analytics Metrics

• Business Metrics: KPIs, conversion rates, retention
• Model Metrics: Accuracy, precision, recall, F1, AUC-ROC
• Causal Inference: Understanding cause-effect relationships

Machine Learning Workflow

Problem Definition

1. Understand Business Context: Goals, constraints, success metrics
2. Define ML Problem: Classification, regression, clustering
3. Data Requirements: Volume, quality, collection strategy

Data Preparation

1. Exploratory Data Analysis (EDA)

   • Distribution analysis
   • Correlation analysis
   • Outlier detection

2. Preprocessing

   • Missing value handling
   • Scaling and normalization
   • Encoding categorical variables

Model Development

1. Baseline Models: Simple models for comparison
2. Feature Engineering: Creating relevant features
3. Model Selection: Trying different algorithms
4. Hyperparameter Tuning: Grid search, random search, Bayesian optimization

Evaluation

1. Train/Validation/Test Split: Proper data separation
2. Cross-validation: k-fold, stratified cross-validation
3. Performance Metrics: Task-specific evaluation criteria
4. Error Analysis: Understanding failure modes

Deployment & Monitoring

1. Model Serving: REST APIs, batch predictions
2. Performance Monitoring: Tracking metrics in production
3. Data Drift Detection: Detecting distribution changes
4. Retraining: Automated or scheduled model updates

MLOps Best Practices

Model Management

• Versioning: Tracking model versions, parameters, performance
• Reproducibility: Consistent results with same inputs
• Documentation: Model cards, performance reports

Pipeline Automation

• CI/CD for ML: Automated testing, validation, deployment
• Feature Stores: Centralized feature management
• Experiment Tracking: MLflow, Weights & Biases

Monitoring & Maintenance

• Performance Tracking: Model accuracy over time
• Data Quality: Input data validation
• Alert Systems: Detecting degradation

NLP & Computer Vision

Natural Language Processing

• Text Preprocessing: Tokenization, stemming, lemmatization
• Word Embeddings: Word2Vec, GloVe, FastText
• Transformers: BERT, GPT, attention mechanisms
• Applications: Sentiment analysis, NER, machine translation

Computer Vision

• Image Preprocessing: Resizing, normalization, augmentation
• Convolutional Networks: CNNs for image classification
• Object Detection: YOLO, R-CNN, SSD
• Segmentation: Semantic and instance segmentation

Learning Resources

• AI Engineer Roadmap: https://roadmap.sh/ai-engineer
• Scikit-learn: https://scikit-learn.org
• TensorFlow: https://www.tensorflow.org
• Fast.ai: https://www.fast.ai
• Kaggle Competitions: https://www.kaggle.com