Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai

Overview

Machine Learning Engineering for Production (MLOps) Specialization on Coursera (offered by deeplearning.ai)

Programming assignments from all courses in the Coursera Machine Learning Engineering for Production (MLOps) Specialization offered by deeplearning.ai.

Courses

The GAN Specialization on Coursera contains three courses:

  1. Course 1: Introduction to Machine Learning in Production

  2. Course 2: Machine Learning Data Lifecycle in Production

  3. Course 3: Machine Learning Modeling Pipelines in Production

  4. Course 4: Deploying Machine Learning Models in Production

Why this Specialization?

  • Become a Machine Learning expert. Productionize your machine learning knowledge and expand your production engineering capabilities.

  • Skills: Managing Machine Learning Production Systems, Deployment Pipelines, Model Pipelines, Data Pipelines, Machine Learning Engineering for Production, Human-level Performance (HLP), Concept Drift, Model Baseline, Project Scoping and Design, ML Deployment Challenges, ML Metadata, Convolutional Neural Network

  • Level: Advanced

    • Some knowledge of AI / deep learning
    • Intermediate skills in Python
    • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)

About this Specialization

  • Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well.

  • Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles.

  • The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.

  • In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.

Applied Learning Project

By the end, you'll be ready to:

  • Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements
  • Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application
  • Build data pipelines by gathering, cleaning, and validating datasets
  • Implement feature engineering, transformation, and selection with TensorFlow Extended
  • Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas
  • Apply techniques to manage modeling resources and best serve offline/online inference requests
  • Use analytics to address model fairness, explainability issues, and mitigate bottlenecks
  • Deliver deployment pipelines for model serving that require different infrastructures
  • Apply best practices and progressive delivery techniques to maintain a continuously operating production system

Programming Assignments

Course 1: Introduction to Machine Learning in Production

Week 1

Week 2

Week 3


Course 2: Machine Learning Data Lifecycle in Production

Week 1

Week 2

Week 3

Week 4


Disclaimer

I recognize the hard time people spend on building intuition, understanding new concepts and debugging assignments. The solutions uploaded here are only for reference. They are meant to unblock you if you get stuck somewhere. Please do not copy any part of the code as-is (the programming assignments are fairly easy if you read the instructions carefully). Similarly, try out the quizzes yourself before you refer to the quiz solutions.

Owner
Aman Chadha
Tinkerer @ . AI @ Stanford.
Aman Chadha
A benchmark of data-centric tasks from across the machine learning lifecycle.

A benchmark of data-centric tasks from across the machine learning lifecycle.

61 Dec 28, 2022
YouTube Spam Detection with python

YouTube Spam Detection This code deletes spam comment on youtube videos based on two characteristics (currently) If the author of the comment has a se

MohamadReza Taalebi 5 Sep 27, 2022
neurodsp is a collection of approaches for applying digital signal processing to neural time series

neurodsp is a collection of approaches for applying digital signal processing to neural time series, including algorithms that have been proposed for the analysis of neural time series. It also inclu

NeuroDSP 224 Dec 02, 2022
MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees.

MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees. MooGBT optimizes for multiple objectives by defining constraints on sub-objective(s) along with a primary objective. Th

Swiggy 66 Dec 06, 2022
SPCL 48 Dec 12, 2022
Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared

Feature-Engineering Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared. When the dataset

kemalgunay 5 Apr 21, 2022
Python ML pipeline that showcases mltrace functionality.

mltrace tutorial Date: October 2021 This tutorial builds a training and testing pipeline for a toy ML prediction problem: to predict whether a passeng

Log Labs 28 Nov 09, 2022
Mesh TensorFlow: Model Parallelism Made Easier

Mesh TensorFlow - Model Parallelism Made Easier Introduction Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying

1.3k Dec 26, 2022
Classification based on Fuzzy Logic(C-Means).

CMeans_fuzzy Classification based on Fuzzy Logic(C-Means). Table of Contents About The Project Fuzzy CMeans Algorithm Built With Getting Started Insta

Armin Zolfaghari Daryani 3 Feb 08, 2022
Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them

Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them.

Anirudh Edpuganti 3 Apr 03, 2022
A data preprocessing package for time series data. Design for machine learning and deep learning.

A data preprocessing package for time series data. Design for machine learning and deep learning.

Allen Chiang 152 Jan 07, 2023
Decision Weights in Prospect Theory

Decision Weights in Prospect Theory It's clear that humans are irrational, but how irrational are they? After some research into behavourial economics

Cameron Davidson-Pilon 32 Nov 08, 2021
Transform ML models into a native code with zero dependencies

m2cgen (Model 2 Code Generator) - is a lightweight library which provides an easy way to transpile trained statistical models into a native code

Bayes' Witnesses 2.3k Jan 03, 2023
A machine learning toolkit dedicated to time-series data

tslearn The machine learning toolkit for time series analysis in Python Section Description Installation Installing the dependencies and tslearn Getti

2.3k Dec 29, 2022
PySpark ML Bank Churn Prediction

PySpark-Bank-Churn Surname: corresponds to the record (row) number and has no effect on the output. CreditScore: contains random values and has no eff

kemalgunay 2 Nov 11, 2021
Esse é o meu primeiro repo tratando de fim a fim, uma pipeline de dados abertos do governo brasileiro relacionado a compras de contrato e cronogramas anuais com spark, em pyspark e SQL!

Olá! Esse é o meu primeiro repo tratando de fim a fim, uma pipeline de dados abertos do governo brasileiro relacionado a compras de contrato e cronogr

Henrique de Paula 10 Apr 04, 2022
A library to generate synthetic time series data by easy-to-use factors and generator

timeseries-generator This repository consists of a python packages that generates synthetic time series dataset in a generic way (under /timeseries_ge

Nike Inc. 87 Dec 20, 2022
Learn how to responsibly deliver value with ML.

Made With ML Applied ML · MLOps · Production Join 30K+ developers in learning how to responsibly deliver value with ML. 🔥 Among the top MLOps reposit

Goku Mohandas 32k Dec 30, 2022
pandas, scikit-learn, xgboost and seaborn integration

pandas, scikit-learn and xgboost integration.

299 Dec 30, 2022
Exemplary lightweight and ready-to-deploy machine learning project

Exemplary lightweight and ready-to-deploy machine learning project

snapADDY GmbH 6 Dec 20, 2022