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Many companies today 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. Rather than hiring just data scientists or machine learning engineers, companies are looking for individuals who can automate and streamline the process of training, evaluating, versioning, deploying, and monitoring models in the cloud.

In this beginner's guide, we will focus on the seven essential steps to mastering MLOps engineering, including setting up the environment, experiment tracing and versioning, orchestration, continuous integration/continuous delivery (CI/CD), model serving and deployment, and model monitoring. In the final step, we will build a fully automated end-to-end machine-learning pipeline using various MLOps tools.

1. Local and Cloud Environment Setup

In order to train and evaluate machine learning models, you will first need to set up both a local and cloud environment. This involves containerizing machine learning pipelines, models, and frameworks using Docker. After that, you will learn to use Kubernetes to automate the deployment, scaling, and management of these containerized applications. 

By the end of the first step, you will become familiar with a 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 setup of your cloud infrastructure. 

Note: It is essential that you have a basic understanding of Docker, Git, and familiarity with command line tools. However, if you have a background in software engineering, you may be able to skip this part.

2. Experiment Tracking, Versioning, and Model Management

You will learn to use MLflow for tracking machine learning experiments, DVC for model and data versioning, and Git for code versioning. MLflow can be used for logging parameters, output files, model management, and servering. 

These practices are essential for 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 Machine Learning Experiment Tracking and pick one that works best for your workflow. 

3. Orchestration and Machine Learning Pipelines

In the third step, you will learn to use orchestration tools such as Apache Airflow or Prefect to automate and schedule the ML workflows. The workflow includes data preprocessing, model training, evaluation, and more, ensuring a seamless and efficient pipeline from data to deployment.

These tools make each step in the ML flow to be modular and reusable across different projects to save time and reduce errors.

Learn about 5 Airflow Alternatives for Data Orchestration that are user friendly and come with modern features. Also, check out the Prefect for Machine Learning Workflows tutorial to build and execute your first ML pipeline. 

4. CI/CD for Machine Learning

Integrate Continuous Integration and Continuous Deployment (CI/CD) practices into your ML workflows. Tools like Jenkins, GitLab CI, and GitHub Actions can automate the testing and deployment of ML models, ensuring that changes are efficiently and safely rolled out. You will learn to Incorporate automated testing of your data, model, and code to catch issues early and maintain high-quality standards.

Learn how to automate model training, evaluation, versioning, and deployment using GitHub Actions by following the A Beginner's Guide to CI/CD for Machine Learning.

5. Model Serving and Deployment

Model serving is a critical aspect of utilizing machine learning models effectively in production environments. By employing model serving frameworks such as 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 for you to securely and efficiently deploy models in production.

Learn about the Top 7 Model Deployment and Serving Tools that are being used by top companies to simplify and automate the model deployment process. 

6. Model Monitoring

In the sixth step, you will learn how to implement monitoring to keep track of your model's performance and detect any changes in your data over time. You can use tools like Evidently, Fiddler, or even write custom code for real-time monitoring and alerting. By using a monitoring framework, you can build a fully automated machine learning pipeline where any significant decrease in model performance will trigger the CI/CD pipeline. This will result in re-training the model on the latest dataset and eventually deploying the latest model to production.

If you want to learn about the important tools used to build, maintain, and execute the end-to-end ML workflow, you should check out the list of the top 25 MLOps tools you need to know in 2024.

7. Project 

In the final step of this course, you will have the opportunity to build an end-to-end machine learning project using everything you have learned so far. This project will involve the following steps:

1. Select a dataset that interests you.
2. Train a model on the chosen dataset and track your experiments.
3. Create a model training pipeline and automate it using GitHub Actions.
4. Deploy the model either in batch, web service or streaming.
5. Monitor the performance of your model and follow best practices.

Bookmark the page: 10 GitHub Repositories to master MLOps. Use it to learn about the latest tools, guides, tutorials, projects and free courses to learn everything about MLOps.

Conclusion

You can enroll in an MLOps Engineering course that covers all seven steps in detail and helps you gain the necessary experience to train, track, deploy, and monitor machine learning models in production. 

In this guide, we have learned about the seven necessary steps for you to become an expert MLOps engineer. We have learned about the tools, concepts, and processes required for engineers 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 on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.