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Today, many companies want to incorporate ai into their workflow, specifically by fine-tuning large language models and deploying them to production. Due to this demand, MLOps engineering has become increasingly important. Instead of hiring only data scientists or machine learning engineers, companies are looking for people who can automate and optimize the process of training, testing, version control, deploying, and monitoring models in the cloud.
In this beginner's guide, we'll focus on the seven essential steps to master MLOps engineering, including environment setup, experiment tracking and versioning, orchestration, continuous integration/continuous delivery (CI/CD). , model servicing and deployment, and model monitoring. . In the final step, we will create a fully automated end-to-end machine learning pipeline using various MLOps tools.
To train and test machine learning models, you will first need to set up an on-premises and cloud environment. This involves containing machine learning pipelines, models, and frameworks using Docker. After that, you'll learn how to use Kubernetes to automate the deployment, scaling, and management of these containerized applications.
At the end of the first step, you will become familiar with the cloud platform of your choice (such as AWS, Google Cloud, or Azure) and learn how to use Terraform for Infrastructure as Code to automate the configuration of your cloud infrastructure.
Note: It is essential that you have basic knowledge of Docker, Git and are familiar with command line tools. However, if you have a background in software engineering, you may be able to skip this part.
You'll learn how to use MLflow to track machine learning experiments, DVC for model and data versioning, and Git for code versioning. MLflow can be used for logging parameters, output files, model and server management.
These practices are essential to maintaining a well-documented, auditable, and scalable ML workflow, ultimately contributing to the success and efficiency of ML projects.
Check out the 7 best tools for tracking machine learning experiments and choose the one that best fits your workflow.
In the third step, you will learn how to use orchestration tools like Apache Airflow or Prefect to automate and schedule ML workflows. The workflow includes data preprocessing, model training, evaluation, and more, ensuring a smooth and efficient process from data to deployment.
These tools make each step of the ML flow modular and reusable across different projects to save time and reduce errors.
Learn five airflow alternatives for data orchestration that are easy to use and packed with modern features. Also, check the Perfect for machine learning workflows tutorial to build and run your first ML pipeline.
Integrate continuous integration and continuous deployment (CI/CD) practices into your machine learning workflows. Tools like Jenkins, GitLab CI, and GitHub Actions can automate testing and deployment of machine learning models, ensuring changes are deployed efficiently and safely. You'll learn how to incorporate automated testing of your data, model, and code to detect issues early and maintain high-quality standards.
Learn how to automate model training, testing, version control, and deployment using GitHub Actions by following the instructions A Beginner's Guide to CI/CD for Machine Learning.
Model serving is a critical aspect of effectively using machine learning models in production environments. By employing model serving frameworks like BentoML, Kubeflow, Ray Serve, or TFServing, you can efficiently deploy your models as microservices, making them accessible and scalable across multiple applications and services. These frameworks provide a seamless way to test model inference locally and offer features to deploy models to production safely and efficiently.
Learn the top 7 model deployment and servicing tools used by leading companies to simplify and automate the model deployment process.
In the sixth step, you will learn how to implement monitoring to track the performance of your model and detect any changes in your data over time. You can use tools like Evidfully, Fiddler or even write custom code for real-time monitoring and alerting. By using a monitoring framework, you can create a fully automated machine learning pipeline where any significant decline in model performance will trigger the CI/CD pipeline. This will result in retraining the model with the latest data set and eventually deploying the latest model to production.
If you want to learn about the important tools used to build, maintain, and run the end-to-end machine learning workflow, you should check out the list of Top 25 MLOps Tools You Need to Know in 2024.
In the final step of this course, you will have the opportunity to create an end-to-end machine learning project using everything you have learned so far. This project will involve the following steps:
- Select a data set that interests you.
- Train a model on your chosen data set and track your experiments.
- Create a model training pipeline and automate it using GitHub Actions.
- Deploy the model either in batch, web service, or streaming.
- Monitor your model's performance and follow best practices.
Bookmark the page: 10 GitHub Repositories to Master MLOps. Use it to learn about the latest free tools, guides, tutorials, projects, and courses to learn everything about MLOps.
You can enroll in an MLOps engineering course that covers all seven steps in detail and helps you gain the experience needed to train, track, deploy, and monitor machine learning models in production.
In this guide, we have learned the seven steps required for you to become an expert MLOps engineer. We have learned about the tools, concepts and processes necessary for engineers to automate and optimize the process of training, testing, versioning, deploying and monitoring models in the cloud.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a master's degree in technology management and a bachelor's degree in telecommunications engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
