MLOPs

A summary of tools for Data Scientists going into MLOPs

The idea of this repository is to publish my summaries about tools that are useful for Machine Learning Engineers. The summaries are updated constantly.

Machine Learning

Supoorting Topics

1. Feature processing
2. Evaluating Models

Supervised Models

1. Linear Regression
2. Lasso Regression
3. Ridge Regression
4. OLS - Ordinary Least Squares
5. Naive Bayes
6. SGD - Stochastic Gradient Descent
7. SVM - Support Vector Machines
8. Logistic Regression
9. Decision Trees
10. Perceptron
11. Neural Networks

Unsupervised Models

1. KNN - K-Nearest Neighbours
2. PCA - Principal Component Analysis

Time Series Forecasting

1. Use Cases for Time series
2. ETS Models

• 2.1 ETS - Simple Exponential Smoothing Method
• 2.2 ETS - Holt's Linear Trend Method
• 2.3 ETS - Exponential Trend Method
• 2.4 ETS - Holt-Winters Seasonal Method

Reinforcement Learning

1. Markov Decision Process
2. Q-learning
3. Temporal Difference

Causal Modelling

1. Introduction
2. Experimental Design and Statistical Controls
3. Correlation
4. Hypothesis and Probability
5. Prediction and Proof
6. Induction and Deduction

Deployment

1. APIs
2. Docker
3. Kubernetes
4. Airflow

Cloud

Google Cloud Platform

1. GCP AI Platform
2. Machine Learning Models on GCP
3. Machine Learning Pipelines on GCP
4. Kubeflow Pipelines

MLOPs Frameworks

1. FastAPI
2. MLFlow
3. KubeFlow
4. MetaFlow
5. ZenML
6. Kedro

DevOPs (Infra)

1. Helm
2. Terraform

Systems Designs

1. Design Fundamentals
2. Client-Server Model