- Machine Learning Basics
- Machine Learning - Home
- Machine Learning - Getting Started
- Machine Learning - Basic Concepts
- Machine Learning - Python Libraries
- Machine Learning - Applications
- Machine Learning - Life Cycle
- Machine Learning - Required Skills
- Machine Learning - Implementation
- Machine Learning - Challenges & Common Issues
- Machine Learning - Limitations
- Machine Learning - Reallife Examples
- Machine Learning - Data Structure
- Machine Learning - Mathematics
- Machine Learning - Artificial Intelligence
- Machine Learning - Neural Networks
- Machine Learning - Deep Learning
- Machine Learning - Getting Datasets
- Machine Learning - Categorical Data
- Machine Learning - Data Loading
- Machine Learning - Data Understanding
- Machine Learning - Data Preparation
- Machine Learning - Models
- Machine Learning - Supervised
- Machine Learning - Unsupervised
- Machine Learning - Semi-supervised
- Machine Learning - Reinforcement
- Machine Learning - Supervised vs. Unsupervised
- Machine Learning Data Visualization
- Machine Learning - Data Visualization
- Machine Learning - Histograms
- Machine Learning - Density Plots
- Machine Learning - Box and Whisker Plots
- Machine Learning - Correlation Matrix Plots
- Machine Learning - Scatter Matrix Plots
- Statistics for Machine Learning
- Machine Learning - Statistics
- Machine Learning - Mean, Median, Mode
- Machine Learning - Standard Deviation
- Machine Learning - Percentiles
- Machine Learning - Data Distribution
- Machine Learning - Skewness and Kurtosis
- Machine Learning - Bias and Variance
- Machine Learning - Hypothesis
- Regression Analysis In ML
- Machine Learning - Regression Analysis
- Machine Learning - Linear Regression
- Machine Learning - Simple Linear Regression
- Machine Learning - Multiple Linear Regression
- Machine Learning - Polynomial Regression
- Classification Algorithms In ML
- Machine Learning - Classification Algorithms
- Machine Learning - Logistic Regression
- Machine Learning - K-Nearest Neighbors (KNN)
- Machine Learning - Naïve Bayes Algorithm
- Machine Learning - Decision Tree Algorithm
- Machine Learning - Support Vector Machine
- Machine Learning - Random Forest
- Machine Learning - Confusion Matrix
- Machine Learning - Stochastic Gradient Descent
- Clustering Algorithms In ML
- Machine Learning - Clustering Algorithms
- Machine Learning - Centroid-Based Clustering
- Machine Learning - K-Means Clustering
- Machine Learning - K-Medoids Clustering
- Machine Learning - Mean-Shift Clustering
- Machine Learning - Hierarchical Clustering
- Machine Learning - Density-Based Clustering
- Machine Learning - DBSCAN Clustering
- Machine Learning - OPTICS Clustering
- Machine Learning - HDBSCAN Clustering
- Machine Learning - BIRCH Clustering
- Machine Learning - Affinity Propagation
- Machine Learning - Distribution-Based Clustering
- Machine Learning - Agglomerative Clustering
- Dimensionality Reduction In ML
- Machine Learning - Dimensionality Reduction
- Machine Learning - Feature Selection
- Machine Learning - Feature Extraction
- Machine Learning - Backward Elimination
- Machine Learning - Forward Feature Construction
- Machine Learning - High Correlation Filter
- Machine Learning - Low Variance Filter
- Machine Learning - Missing Values Ratio
- Machine Learning - Principal Component Analysis
- Machine Learning Miscellaneous
- Machine Learning - Performance Metrics
- Machine Learning - Automatic Workflows
- Machine Learning - Boost Model Performance
- Machine Learning - Gradient Boosting
- Machine Learning - Bootstrap Aggregation (Bagging)
- Machine Learning - Cross Validation
- Machine Learning - AUC-ROC Curve
- Machine Learning - Grid Search
- Machine Learning - Data Scaling
- Machine Learning - Train and Test
- Machine Learning - Association Rules
- Machine Learning - Apriori Algorithm
- Machine Learning - Gaussian Discriminant Analysis
- Machine Learning - Cost Function
- Machine Learning - Bayes Theorem
- Machine Learning - Precision and Recall
- Machine Learning - Adversarial
- Machine Learning - Stacking
- Machine Learning - Epoch
- Machine Learning - Perceptron
- Machine Learning - Regularization
- Machine Learning - Overfitting
- Machine Learning - P-value
- Machine Learning - Entropy
- Machine Learning - MLOps
- Machine Learning - Data Leakage
- Machine Learning - Resources
- Machine Learning - Quick Guide
- Machine Learning - Useful Resources
- Machine Learning - Discussion
Machine Learning - MLOps
MLOps (Machine Learning Operations) is a set of practices and tools that combine software engineering, data science, and operations to enable the automated deployment, monitoring, and management of machine learning models in production environments.
MLOps addresses the challenges of managing and scaling machine learning models in production, which include version control, reproducibility, model deployment, monitoring, and maintenance. It aims to streamline the entire machine learning lifecycle, from data preparation and model training to deployment and maintenance.
MLOps Best Practices
MLOps involves a number of key practices and tools, including −
Version control − This involves tracking changes to code, data, and models using tools like Git to ensure reproducibility and maintain a history of all changes.
Continuous integration and delivery (CI/CD) − This involves automating the process of building, testing, and deploying machine learning models using tools like Jenkins, Travis CI, or CircleCI.
Containerization − This involves packaging machine learning models and dependencies into containers using tools like Docker or Kubernetes, which enables easy deployment and scaling of models in production environments.
Model serving − This involves setting up a server to host machine learning models and serving predictions on incoming data.
Monitoring and logging − This involves tracking the performance of machine learning models in production environments using tools like Prometheus or Grafana, and logging errors and alerts to enable proactive maintenance.
Automated testing − This involves automating the testing of machine learning models to ensure they are accurate and robust.
Python Libraries for MLOps
Python has a number of libraries and tools that can be used for MLOps, including −
Scikit-learn − A popular machine learning library that provides tools for data preprocessing, model selection, and evaluation.
TensorFlow − A widely used open-source platform for building and deploying machine learning models.
Keras − A high-level neural networks API that can run on top of TensorFlow.
PyTorch − A deep learning framework that provides tools for building and deploying neural networks.
MLflow − An open-source platform for managing the machine learning lifecycle that provides tools for tracking experiments, packaging code and models, and deploying models in production.
Kubeflow − A machine learning toolkit for Kubernetes that provides tools for managing and scaling machine learning workflows.