MLOps: Smart Ways to Deploy Machine Learning Models to Production

April 16, 2022

MLOps: Smart Ways to Deploy Machine Learning Models to Production

MLOps, or DevOps for machine learning, is the practice of combining software development (Dev) and operations (Ops) to streamline the process of delivering machine learning models to production.

The goal of MLOps is to improve the quality and speed of model deployment while reducing the risk of errors.

MLOps pipelines automate the process of training, testing, and deploying machine learning models. They provide a repeatable and reliable way to go from idea to production while reducing the risk of errors. They are also designed to be flexible to be easily adjusted to accommodate changes in the data or the model.

Some of the benefits of using MLOps pipelines include:

  • Reduced risk of errors: Automated testing and validation can help to ensure that models are deployed correctly and that they continue to work as expected.
  • Increased quality: By automatically testing and validating models before deployment, MLOps can help to ensure that only high-quality models are deployed to production.
  • Increased speed: Automated processes can help to reduce the time it takes to go from idea to production.
  • Increased flexibility: MLOps pipelines can be easily adjusted to accommodate changes in the data or the model.

Related: Scalable, No-Code, AutoML Solution on Your Amazon Cloud

Best practices in MLOps

The machine learning community has developed best practices when building and deploying ML models into production. Some of these best practices are:

  • Use version control: Version control is a must for any software development project, and it is crucial for machine learning projects. Version control systems help keep track of changes to the code, making it easy to roll back changes if something goes wrong.
  • Automate everything: The goal of MLOps is to automate the process of training, testing, and deploying machine learning models. Automating as much of the process as possible can help to speed up the delivery of models to production while reducing the risk of errors.
  • Test early and often: Testing helps to ensure that models are deployed correctly and continue to work as expected. ML model testing will ensure that the model is performing as expected and that it is not overfitting to the training data. It's vital to see if our new model appropriately accounted for data drift.
  • Monitor in production: Once a machine learning model is deployed to production, it is important to monitor its performance. Monitoring can help identify issues early so that we can fix them before they cause problems.
  • Secure your ML pipeline: Security is a critical concern for any software development project, and it is crucial for machine learning projects. ML pipelines should be designed with security in mind, and we should test them to ensure that they are secure.
  • Document everything: Documentation is a critical part of any software development project, and it is crucial for machine learning projects. Documentation can help ensure that everyone involved in the project understands what is going on, making it easier to reproduce results.
  • A/B test your models: A/B testing is a technique that is often used in software development, and it can be useful for machine learning projects as well. A/B testing can help compare the performance of two different models, and it can also help identify issues with a model before it is deployed to production.
  • Scale wisely: Machine learning models can require a lot of computational resources, and they can be difficult to scale. It is important to consider how a machine learning model will be used before deployment and choose an appropriate scaling strategy for the use case.

These are just a few of the machine learning community's best practices.

Related: In the 8 Key MLOps Roles, Where Do You Fit In?

Best managed MLOps platforms

With the rise of MLOps, several companies have started to offer managed MLOps platforms. These platforms provide a complete solution for training, testing, and deploying machine learning models. Some of the most popular managed MLOps platforms include:

  • Amazon SageMaker: Amazon SageMaker is a fully managed MLOps platform offered by Amazon Web Services (AWS). SageMaker helps you manage the entire ML workflow through the platform. It makes data collection, model building, testing, and deployment effortless. Yet what makes SageMaker wonderful is its No-Code Machine learning solutions.
  • Vertex AI: Vertex AI, previously known as the AI platform, is an ML workflow tool by Google. It allows users to build, test and deploy models in Google Cloud Platform (GCP) with so many overheads automated. Vertex Auto ML solution allows you to compare multiple models and pick the best.
  • Azure ML: Azure ML is managed by Microsoft Azure. Azure ML provides many tools that make it easy to train, test and deploy machine learning models. Microsoft also claims to offer to run machine learning workloads anywhere with built-in governance, security, and compliance.
  • H20.ai: H20.ai is an open-source MLOps platform that we can use on several cloud platforms. H20.ai provides several MLOps features as well as AutoML solutions to build models with less human involvement.

These are just a few of the managed MLOps platforms that are available. Each platform has its own strengths and weaknesses, and it is important to choose the right platform for your project.

MLOps Courses

If you are interested in learning more about MLOps, there are many courses that you can take. These courses will teach you the best practices for training, testing, and deploying machine learning models.

- Udacity: Udacity offers a nano degree in MLOps. The program covers a wide range of topics including, clean coding principles to building ML Pipelines.

- Coursera: Coursera has a specialization to learn MLOps. Deeplearning.AI offers this specialization with instructors from Google conducting the courses.

- EDX: EdX has an interesting MLOps course. This program focuses specifically on scaling up TinyML. The concept of TinyML has gained popularity in recent years. And this course is all about starting there and building a larger ML system.

Related: How to Learn and Improve Your Python Coding Skills With Free Resources?

Final thoughts

In short, MLOps is essential for machine learning success at scale. It helps to ensure that models are deployed correctly and continue to work as expected. Automated processes can also help to reduce the time it takes to go from idea to production. Finally, MLOps pipelines are designed to be flexible to be easily adjusted to accommodate changes in the data or the model.

We've discussed some popular MLOps platforms and courses to get started.

If you are building machine learning models, then you should definitely consider using an MLOps platform. These platforms can help automate the process of training, testing, and deploying machine learning models. In addition, they can provide several other benefits, such as scalability and flexibility.

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