How to Learn MLOps in 2024 -The Ultimate Guide for Beginners

Follow the list of resources in this learn article to learn MLOps and land the MLOps job of your dreams.

How to Learn MLOps in 2024 -The Ultimate Guide for Beginners
 |  BY Manika

Read this article to find the right resources for learning MLOps. The blog starts with an introduction to MLOps, skills required to become an MLOps engineer, and then lays out an MLOps learning path for beginners.

MLOps is an acronym that represents the combination of Machine-Learning(ML) and Operations. It is a beautiful technique for implementing data science projects that allow businesses to increase their projects’ efficiency minimize the risk of introducing machine learning, artificial intelligence, and data-science-related technologies.


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In the past five years, the concept of MLOps has witnessed a sharp rise in the number of people interested in using it. You can easily verify this by looking at the Google Trends for the keyword ‘MLOps’ in the image below. The primary reason behind this spike is the sudden realization that using MLOps results in the improvised deployment of machine learning algorithms. Usually, data scientists do not have a strong background in engineering and cannot thus follow DevOps norms. Thus, MLOps enhances the connection among engineers and data scientists, which leads to better communication and effective collaboration on multiple projects. Also, MLOps has smoothened the process of creating scalable machine learning projects. If all these advantages excite you to dig deeper into this exciting world of MLOps and you have decided to learn more about it, continue reading this blog.

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A Beginner’s Guide: How to learn MLOps?

Before we start with the list of resources to learn MLOps, let us look at the reason behind the need for MLOps and the skills required to become an MLOps engineer.

How to learn MLOps?

The Need for MLOps: Understanding a Data Science Project’s Workflow

A data science project involves the below-mentioned steps that you should follow in sequential order. These steps are:

  1. Cleaning the data and handling different file formats.

  2. Feature Selection and Feature Engineering

  3. Choosing the relevant machine learning/deep learning algorithms.

  4. Training the algorithm and Hyperparameter Tuning

  5. Analyzing the performance of the model using statistical parameters.

  6. Improving the quality of results by revisiting the workflow.

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Depending on the project you are working on, you might add a few more steps, but these steps are elementary for every other data science project. The first step of cleaning the dataset is critical as a lot of time is spent here. Also, the better the data, the better the quality of results that one is expecting. MLOps acts as a boon in such cases as it tracks the features of data and assists in enhancing the state of the data and, therefore, the project's implementation. 

Additionally, note the last step, learning from mistakes, is crucial for any data science project as minor corrections eventually lead to an excellent trained model. MLOps makes the whole process of tracking statistical parameters for analysis quick and is therefore being largely preferred these days. Furthermore, as all the steps involve writing lengthy codes, it becomes difficult to control them single-handedly. MLOps provides the right environment and dependencies that allow easy control over the code and its reproduction. Also, with MLOps, one can automate the project workflow and flag various points in the workflow so that a data science project lifecycle is only disturbed when a change is needed.

Who Should Learn MLOps?

While many believe that MLOps should be pursued only by people who have a stronghold on DevOps practices, it is not entirely correct. DevOps engineers benefit from their experience, but anyone who does not have such exposure can also become proficient at MLOps if they have set their sights on the right skills.

A machine learning engineer can get accustomed to DevOps techniques and then work on a few projects before applying for the MLOps engineer jobs. Similarly, a data scientist can also follow a similar path and develop the required skills by practicing a few MLOps projects. For a DevOps engineer, broadening their skillset by diving into the world of machine learning algorithms will work. 

However, for a beginner, it will take a dedicated learning path that they must follow to become an MLOps engineer. We have prepared a learning path for you, but before that, let’s put first things first and look at the basic skills required to become an MLOps engineer. 

Skills Required to Become an MLOps Engineer

An MLOps Engineer is expected to possess the following skills:

  • Strong proficiency in computer programming skills.

  • Well-equipped with data handling skills.

  • Excellent knowledge of data structures, database management systems, and data modeling algorithms.

  • Ability to understand DevOps practices and abide by them.

  • Strong communication skills.

  • Experience with using Big Data tools for a data science project deployment.

  • Building and Optimizing end-to-end Data Science project solutions.

  • Using machine learning algorithms to automate the solution pipelines.

  • Ability to work in an environment where individuals across multiple teams are involved.

Do not let the list of skills scare you away from pursuing a career in MLOps. Instead, motivate yourself and fuel your passion for learning MLOps by following our list of resources in the next section.

How Do I Learn MLOps?

Check out the list below to know about the exciting resources you need to kick off your journey in MLOps.

  1. Advanced Mathematics

While most of you would have covered mathematics in your high schools necessary to understand machine learning algorithms, beginners can refer to the textbooks mentioned below to learn more about linear algebra, differential equations, probability, and statistics

  • Advanced Engineering Mathematics by Erwin Kreyszig

  • Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics by Anirban DasGupta

These two books cover all the mathematical concepts in great detail with solved examples that you can refer to for smooth learning.

  1. Programming Language

The primary language for machine learning is Python, and for learning that, we have one of the best book recommendations for you. It is Python Programming for the Absolute Beginner by Michael Dawson. This book contains beginner-friendly codes in Python to teach basic concepts like data types, functions, loops lists, dictionaries, tuples, and data file handling. The best part about this book is that the author's tone is pretty entertaining, so one does not feel bored while reading it. Additionally, work on projects that involve web frameworks like Django and Flask, which will prove beneficial when it comes to understanding how Machine learning projects are deployed on the cloud.

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  1. Machine Learning and Deep Learning

Machine Learning (ML) and deep learning algorithms form the essential pillars of MLOps, and one can not do without diving into their nuts and bolts. So, refer to An Introduction to Statistical Learning by James Gareth et al. to understand ML algorithms in simple terms. This text is enough to avoid the rigorous mathematics involved and understand the applications of algorithms. However, suppose you are curious about the mathematics behind these algorithms. In that case, you must read Pattern Recognition and Machine Learning by Christopher Bishop and Deep Learning Ian Goodfellow, Yoshua Bengio, and Aaron Courville for deep learning algorithms.

 

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  1. DevOps

Learning DevOps will help you quickly grasp the concept of MLOps. And the one-stop solution to gain an understanding of DevOps is The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations by Gene Kim et al. This book will first teach you the importance of DevOps at the industry level and then move on to explain the principles flow, feedback, continual learning, and experimentation.

Recommended Reading: MLOps vs. DevOps! Here's How They Fit Together

  1. Big Data 

To become competent in handling large datasets, one needs to rely on Big Data tools. The three popular tools that come to everyone’s minds are GCP, AWS, and Microsoft Azure. And as these are not mathematical concepts, one can not simply read and learn about using them. The best way to master these tools is to work on practical Big Data projects that utilize one of these tools. Watch out for the next section to know more about such projects.

Apart from honing the knowledge of MLOps basics, we have a few bonus mentions to help you build a strong foundation in the subject. You can listen to a few podcasts related to MLOps on Spotify, join the MLOps community on slack, and refer to this MLOps document by Google and have a better understanding of this fantastic domain of MLOps. Lastly, follow Steve Nouri, Rahul Anand, Ashish Patel, and Sonal Singh on LinkedIn to update yourself with new MLOps and Data Science trends.

Learning MLOps the Faster Way!

After learning the prerequisites, any learning path will seem incomplete if you don’t gain hands-on experience. Implementing a few projects solutions will also bring in confidence to solve more challenging problems. So, check out the list of MLOps projects with source code below and get started with them today.

MLOps using Azure DevOps to Deploy a Classification Model

This project assumes you are new to DevOps and thus introduces the subject in a beginner-friendly way. You will learn about the advantages of DevOps in detail, along with its features and services. The project will guide you through using Azure repositories, FastAPI and its features, docker, and Azure pipelines. You will also learn how to configure those pipelines with the help of YAML and deploy them on the web.

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MLOps Project to Deploy Resume Parser Model on Paperspace

This project will teach you the difference between public cloud and private cloud along with their advantages and disadvantages. You will learn about docker commands, docker hub, and the architecture of docker. Furthermore, the project will also highlight Git commands and Paperspace applications. You will acquire the skill of deploying streamlit applications of Paperspace through this project.

MLOps Project on GCP using Kubeflow for Model Deployment

Here is another project that will increase your knowledge of MLOps by introducing Kuberflow. You will learn about the basics of Git, and Git commands. The project will teach you about the practical utilization of Docker, KuberFlow, and Flask in building an end-to-end MLOps project solution. You will have an excellent idea of Cloud Source Repository and deployment of the project using Cloud Run.

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MLOps on GCP Project for Moving Average using uWSGI Flask

This project starts will a simple introduction to MLOps and then helps you understand how to implement an MLOps project pipeline on Google Cloud Platform (GCP). You will learn about flask and uWSGI model files and how to build docker images. You will get to explore the architecture of Kubernetes and understand various components of GCP. Furthermore, the project will teach you how to create a cloud repository in GCP, Pub/Sub component, deployment on Flask and Kubernetes, and utilization of Cloud Shell Editor.

MLOps AWS Project on Topic Modeling using Gunicorn Flask

This project will explain the complete architecture of the MLOps solution pipeline. It will introduce you to various services provided by AWS S3 and help you understand how to implement a code in it. You will learn how to deploy a machine learning pipeline on Flask with the help of the Gunicorn web server. You will explore the ECS Cluster, EC2 services, and Code Build tools.

There are many more projects in the ProjectPro projects library that you can leverage to hone all the relevant skills required for becoming an MLOps engineer. With over 150+ solved end-to-end projects on Data Science and Big Data, you can swim through to achieve your career goals in these domains.

FAQs

Does MLOps require coding?

Yes, to become a professional at MLOps, one must have excellent proficiency in programming languages, most importantly, in Python. Python is used for designing machine learning algorithms, and supports web frameworks like Django and Flask for project deployment.  

How long does it take to learn MLOps?

The time required to learn MLOps depends on the skills you possess already. If you are a beginner who has just been introduced to the term, it might take a few months of dedication to honing all necessary skills. However, if you are a DevOps engineer, you only need to focus on learning machine learning algorithms, and you can easily transition to MLOps in a few weeks.

 

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About the Author

Manika

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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