How to Learn MLOps in 2024

Machine learning field is continuously growing from the last few years. Today, businesses are mostly dependent on data-driven solutions. With recent high-speed advances in ML, its operations have become a rapidly evolving sector in modern industry. It is an excellent technology to implement data science projects that help businesses to improve their projects efficiency.

Experts in this field are in high demand to manage and execute machine learning models. Mastering this field could provide better career opportunities with high scale salaries. This article contains a proper guide to learn MLOps with its career prospects.

What is MLOps?

MLOps stands for machine learning operations and it includes managing, monitoring and executing ML models in production. It is a combination of machine learning, DevOps, and data science technologies. This platform enables software engineers with data scientists to provide fast, scalable and reliable deployments of ML models. Here are some key components an expert should be aware of -

• Version control & CI/CD

• Model registries & Experiment tracking

• Orchestration

• Data lineage & Features stores

• Observation & Monitoring

• Model training & serving

• Infrastructure as code

Skills Required to Become an MLOps Expert

To become an MLOps engineer, they should be skilled in following skills:

• Expertise in programming languages like Python, Java or R.

• Strong proficiency in data handling skills.

• Good communication skills.

• Advanced mathematics to understand ML algorithms.

• Outstanding knowledge of database management systems, data structures and machine learning algorithms.

• Capacity to understand DevOps execution.

• Experience in big data tools for data science applications.

• Creating and optimizing full data science solutions.

• Automation of pipeline solutions.

• Ability to work with multiple teams.

Steps to Learn MLOps in 2024 - A Complete Guide

MLOps draws expertise across various sectors. Hence, it is crucial to learn all of the software tech related to this field. Here is a structured approach to become an MLOps expert -

Learn Python Programming

Python is a simple and easy to learn programming language, widely used in machine learning operations. It has powerful libraries like Sckit-learn and Pandas that helps to perform complex tasks. This programming language helps to build strong ML models and automate workflows. It is also used to build model frameworks that allow deployment of models in the real world. These frameworks are more like Flask, Django and TensorFlow.

2. Knowledge of Advanced Mathematics

Knowledge of advanced mathematical concepts like differential equations, algebra, statics and probability are necessary to understand ML algorithms. These concepts help to recognize the perfect algorithm for particular operations. Here are some books that can help you to enhance your skills in advanced mathematics -

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

Advanced Engineering Mathematics by Erwin Kreyszig.

3. Data Management

Database management is an essential aspect of MLOps. It makes sure that data is available and valid for decision making and model reliability. An MLOps engineer’s primary objective is to organize and store the data effectively in a cloud platform. These cloud platforms include both SQL and NoSQL.

It is very important to make sure the data is accessible and valid for model reliability and instructed decision making. Hence, the knowledge of data management systems is crucial to become an expert in MLOps.

4. Learn Core Machine Learning Concepts

Machine learning concepts are a core part of this platform. Hence, learning it is very important to become a professional. There are three core concepts experts must know about, supervised, unsupervised and reinforcement learning. It helps to choose an accurate algorithm for particular tasks. These specific tasks are model training, data splitting, hyperparameter tuning, and performance evaluation.

A complete comprehension of these concepts helps to ensure model accuracy and avoid problems like underfitting and overfitting. Here are some different types of model evaluation metrics:

• Precision

• Accuracy

• F1-score

• Recall

• Area Under Curve (AUC)

• ROC curves

5. Learn CI/CD Pipelines & Version Control

Version control systems (VCS) helps to track and manage several ML models pipelines and enable team cooperation. ML version control has three parts: code, data and model. This system helps with rollback, collaboration, debugging, traceability, and reproducibility to ML models. Integrations of CD and CI with version control helps to automate testing processes and model training.

6. Master The Orchestration

Another important part of learning MLOps is orchestration. It refers to a systematic integration management system for machine learning processes. It helps to perform various tasks by using tools, such as Apache Airflow, Kubernetes, etc. These tasks include:

• Dependency management

• Workflow scheduling

• Resource allocation

• Error handling and Recovery

• Monitoring & Logging

7. Understanding of DevOps

DevOps is a collection of methods that bring together software development and IT operations. It works on software development life cycle (SDLC) to emphasize collaboration and automation. That provides continuous feedback throughout the entire software development and operations process. It helps to reduce the system development life cycle while providing continuous supply of high-quality software.

For implementation of machine learning operations, experts need to integrate ML workflows with DevOps. It includes use of development best practices like agile methodology and VCS. Here are some operations to consider -

• Automation & Integration

• Communication & Collaboration

• CI & CD

• Logging & Monitoring

• Performance & Metrix
  

8. Learn MLOps tools

An MLOps engineer should be proficient in several tools and platforms built to streamline the ML lifecycle. Each of its tools plays a crucial role in different aspects, from data management to model monitoring and deployment. Following are some most used tools -

• Docker

• MLflow

• Kubeflow

• Kubernetes

• Apache Airflow

• Data version control (DVC)

• TensorFlow Extended (TFX)

9. Model Deployment & Monitoring

Monitoring and deploying models are vital steps in the project lifecycle. It ensures that the model is available in a production environment to make real-time predictions from new data. It also helps to create microservices and APIs that can be accessed by other applications from the same organization. This process is very important to spot problems like data drift or performance degradation.

Training Courses For MLOps

For a beginner, the above knowledge is not enough to learn MLOps completely. It requires proper dedication with a structured plan and proper guidance to nail it. There are many institutes who provide MLOps training courses with expert’s guidance. Here some of best MLOps courses -

• MLOps training online and certification course by igmGuru

• MLOps specialization by deepLearning by Coursera

• Machine learning systems design

• Made with ML’s MLOps course online

• Full stack deep learning

Wrap-Up

Understanding ‘what is MLOps?’ and how to learn it in 2024 involves a proper understanding of this field. It is a combination of three fields: ML, DevOps and data science. Hence, it requires a basic knowledge of all fields to learn and become an expert in MLOps. Field knowledge alone is insufficient, the student also has to be good in Python and advanced mathematics.

Additionally, the understanding of VCS, CI/CD pipelines, and orchestration technologies like Kubernetes is must. DevOps practices work on SDLC to increase the efficiency of integration and automation. The whole article information would not be enough for a student, without a proper study plan. As a result, it is important to join a training course to learn it quickly and effectively.