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Today, many companies want to incorporate AI into their workflows, especially by fine-tuning large language models and deploying them into production. This demand makes MLOps engineering increasingly important. Rather than just hiring data scientists and machine learning engineers, companies are looking for people who can automate and streamline the process of training, evaluating, versioning, deploying, and monitoring models in the cloud.
This beginner's guide will help you master MLOps engineering, including environment setup, experiment tracing and version control, orchestration, continuous integration/continuous delivery (CI/CD), model serving and deployment, and model monitoring. We will focus on 7 important steps. . The final step is to build a fully automated end-to-end machine learning pipeline using various MLOps tools.
To train and evaluate machine learning models, you first need to set up both your local and cloud environments. This includes containerizing machine learning pipelines, models, and frameworks using Docker. Then, learn how to use Kubernetes to automate the deployment, scaling, and management of these containerized applications.
By the end of the first steps, you'll be familiar with your cloud platform of choice (AWS, Google Cloud, Azure, etc.) and learn how to automate cloud infrastructure setup using Terraform for Infrastructure as Code .
Note: It is important to understand the basics of Docker, Git, and be familiar with command line tools. However, if you have a background in software engineering, you may be able to skip this part.
Learn how to use MLflow for tracking machine learning experiments, DVC for model and data version control, and Git for code version control. MLflow can be used for parameters, output files, model management, and server logging.
These practices are essential to maintaining well-documented, auditable, and scalable ML workflows, which ultimately contribute to the success and efficiency of ML projects.
Check out the 7 best tools for machine learning experiment tracking and choose the best one for your workflow.
In the third step, you will learn how to automate and schedule ML workflows using orchestration tools such as Apache Airflow and Prefect. Workflows include data preprocessing, model training, evaluation, and more, ensuring a seamless and efficient pipeline from data to deployment.
These tools make each step of your ML flow modular and reusable across different projects, saving you time and reducing errors.
Learn about 5 Airflow alternatives for data orchestration that are user-friendly and have modern features. Also, check out the Prefect for Machine Learning Workflows tutorial to build and run your first ML pipeline.
Integrate continuous integration and continuous deployment (CI/CD) practices into your ML workflows. Tools like Jenkins, GitLab CI, and GitHub Actions allow you to automate testing and deployment of ML models and roll out changes efficiently and securely. Learn how to incorporate automated testing of your data, models, and code to catch problems early and maintain high quality standards.
Follow the Beginner's Guide to CI/CD for Machine Learning to learn how to use GitHub Actions to automate model training, evaluation, version control, and deployment.
Model delivery is a key aspect of effectively leveraging machine learning models in production. By adopting model serving frameworks such as BentoML, Kubeflow, Ray Serve, and TFServing, you can efficiently deploy 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 provide features to safely and efficiently deploy models to production.
Learn about the top 7 model deployment and servicing tools used by top companies to simplify and automate the model deployment process.
In the sixth step, you will learn how to implement monitoring to track model performance and detect changes in data over time. You can also use tools like Evidently, Fiddler, or write custom code for real-time monitoring and alerting. Using a monitoring framework, you can build a fully automated machine learning pipeline that triggers your CI/CD pipeline when your model's performance degrades significantly. This retrains the model on the latest dataset and ultimately deploys the latest model to production.
If you're curious about the important tools used to build, maintain, and run end-to-end ML workflows, check out our list of the top 25 MLOps tools you need to know about in 2024.
In the final step of this course, you will have the opportunity to use everything you have learned so far to build an end-to-end machine learning project. This project includes the following steps:
- Select the dataset you are interested in.
- Train your model on the dataset of your choice and track your experiments.
- Create model training pipelines and automate them using GitHub Actions.
- Deploy your model in batch, web service, or streaming.
- Monitor model performance and follow best practices.
Bookmark the page: 10 GitHub Repositories for Mastering MLOps. Use it to learn about the latest tools, guides, tutorials, projects, and free courses to learn everything about MLOps.
Enroll in the MLOps Engineering course to cover all seven steps in detail and gain the experience you need to train, track, deploy, and monitor machine learning models in production.
In this guide, you learned the seven steps you need to take to become an expert MLOps engineer. We learned about the tools, concepts, and processes engineers need to automate and streamline the process of training, evaluating, versioning, deploying, and monitoring models in the cloud.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs about machine learning and data science technology. Avid holds a master's degree in technology management and a bachelor's degree in telecommunications engineering. His vision is to build his AI products using graphs and his neural networks for students suffering from mental illness.
