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Complete ML Ops With Projects Series

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Pic credits : gridai

Youtube for all the implemented projects and tech interview resources -- Ignito Youtube Channel

Complete Cheat Sheet for Tech Interviews - How to prepare efficiently

I took theses Projects Based Courses to Build Industry aligned Data Science and ML skills

Part 1 - How to solve Any ML System Design Problem


Pre-requisite : Day 1 — Day 60 : Quick Recap of 60 days of Data Science and ML


This repository contains everything you need to become proficient in MLOps

1. MLOps Basics and Principles


What is MLOps?

Purpose

What's important?


2. Data


Complete Python with projects

Pandas and Numpy

Exploratory Data Analysis

Data preprocessing ( Collecting, Labeling and Validating data)

Data Labelling and Advanced Data Labeling Methods

Data Splitting

Feature Engineering

Data Augmentation

3. Aggregations


Aggregation Functions

Analytical Functions

4. Window Functions


Advanced windowing techniques

5. BigQuery


BigQuery Basics

SELECT, FROM, WHERE and Date and Extract in BigQuery

Common Expression Table

UNNEST Clause

SQL vs NoSQL Database

6. Advanced Functions


Triggers

Pivot

Cursors

Views

Indexes

Auto Increment

7. Performance Tuning SQL Queries


Query Optimizations in. SQL

Performance Tuning in SQL

8. MySQL, PostgreSQL and MongoDB


Introduction to MySQL

Introduction to PostgreSQL

Introduction to Mongo DB

Comparison between MySQL and PostgreSQL and Mongo DB

Introduction to SQL and NoSQL Databases

MySQL in Depth

PostgreSQL in Depth

9. Modeling


Model Training and Evaluation

Model Baselines

Model Tuning and Optimization

Model Review and governance

Automated Model retraining

Model Deployment and monitoring

Model Inference and Serving

Model Resource Management Techniques

Model Analysis

High-Performance Modeling

10. Developing


End — to — End ML Workflow Cycle

ML workflows

MLOps Logging and Documentation

MLOps Makefile

ML Lake

ML Pipelines and toolkits

MLOps tools and Frameworks

11. Testing and Reproducibility


Git

Versioning

Docker

12. Production


Continuous Integration

Continuous Delivery and Deployment

Monitoring and Logging

Feature Stores

MLOps architecture and Infrastructure Stack

Model Serving Patterns and Infrastructures


MLOps (Amazing) Papers

Some amazing MLOps research papers that I have read over the years to help you boot up to the industry standards and what's next in this field.


Some of the other best Series-

Complete 60 Days of Data Science and Machine Learning Series

30 days of Machine Learning Ops

30 Days of Natural Language Processing ( NLP) Series

Data Science and Machine Learning Research ( papers) Simplified **

30 days of Data Engineering with projects Series

60 days of Data Science and ML Series with projects

100 days : Your Data Science and Machine Learning Degree Series with projects

23 Data Science Techniques You Should Know

Tech Interview Series — Curated List of coding questions

Complete System Design with most popular Questions Series

Complete Data Visualization and Pre-processing Series with projects

Complete Python Series with Projects

Complete Advanced Python Series with Projects

Kaggle Best Notebooks that will teach you the most

Complete Developers Guide to Git

Exceptional Github Repos — Part 1

Exceptional Github Repos — Part 2

All the Data Science and Machine Learning Resources

210 Machine Learning Projects


6 Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) - 

  1. Complete Data Scientist : https://bit.ly/3wiIo8u

Learn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud.


  1. Complete Data Engineering : https://bit.ly/3A9oVs5

Learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets


  1. Complete Machine Learning Engineer : https://bit.ly/3Tir8ub

Learn advanced machine learning techniques and algorithms - including how to package and deploy your models to a production environment.


  1. Complete Data Product Manager : https://bit.ly/3QGUtwi

Leverage data to build products that deliver the right experiences, to the right users, at the right time. Lead the development of data-driven products that position businesses to win in their market .


  1. Complete Natural Language Processing : https://bit.ly/3T7J8qY

Build models on real data, and get hands-on experience with sentiment analysis, machine translation, and more.


  1. Complete Deep Learning: https://bit.ly/3T5ppIo

Learn to implement Neural Networks using the deep learning framework PyTorch


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