Why do we need MLOps?
First, there were statistics then came the emperor’s new clothes – machine learning, a rebranding of old methods accompanied with new ones emerged. Fast forward to today and we’re all the time talking about this thing called “AI”, the hype is real, it’s palpable because of products like Siri and Amazon Alexa.
But from a Data Scientist point of view, what does it take to develop such a model? Or even a simpler model, say a binary classifier? The amount of work is quite large, and this is only the tip of the iceberg. How much more work is needed to put that model into the continuous development and delivery cycle?
For a Data Scientist, it can be hard to visualize what kind of systems you need to automate everything your model needs to perform its task. Data ETL, feature engineering, model training, inference, hyperparameter optimization, performance monitoring etc. Sounds like a lot to automate?
This is where MLOps comes to the picture, bridging DevOps CI/CD practices to the data science world and bringing in some new aspects as well. You can see more information about MLOps from previous Solita content such as https://www.solita.fi/en/events/webinar-what-is-mlops-and-how-to-benefit-from-it/
Building an MLOps infrastructure is one thing but learning to use it fluently is also a task of its own. For a Data Scientist at the beginning of his/her career, it could seem too much to learn how to use cloud infrastructure as well as learn how to develop Python code that is “production” ready. A Jupyter notebook outputting predictions to a CSV file simply isn’t enough at this stage of the machine learning revolution.
A Jupyter notebook outputting predictions to a CSV file simply isn’t enough at this stage of the machine learning revolution.
Usually, companies that have a long track record of Data Science projects have a few DevOps, Data Engineer/Machine Learning Engineer roles working closely with their Data Scientists teams to distribute the different tasks of production machine learning deployment. Maybe they even have built the tooling and the infrastructure needed to deploy models into production more easily. But there are still quite a few Data Science teams and data-driven companies figuring out how to do this MLOps thing.
Why should you try SageMaker Pipelines?
AWS is the biggest cloud provider ATM so it has all the tooling imaginable that you’d need to build a system like this. They are also heavily invested in Data Science with their SageMaker product and new features are popping up constantly. The problem so far has been that there are perhaps too many different ways of building a system like this.
AWS tries to tackle some of the problems with the technical debt involving production machine learning with their SageMaker Pipelines product. I’ve recently been involved in project building and deploying an MLOps pipeline for edge devices using SageMaker Pipelines and I’ll try to provide some insight on why it is good and what is lacking compared to a completely custom-built MLOps pipeline.
The SageMaker Pipelines approach is an ambitious one. What if, Data Scientists, instead of having to learn to use this complex cloud infrastructure, you could deploy to production just by learning how to use a single Python SDK (https://github.com/aws/sagemaker-python-sdk)? You don’t even need the AWS cloud to get started, it also runs locally (to a point).
SageMaker Pipelines aims at making MLOps easy for Data Scientists. You can define your whole MLOps pipeline in f.ex. A Jupyter Notebook and automate the whole process. There are a lot of prebuilt containers for data engineering, model training and model monitoring that have been custom-built for AWS. If these are not enough you can use your containers enabling you to do anything that is not supported out of the box. There are also a couple of very niche features like out-of-network training where your model will be trained in an instance that has no access to the internet mitigating the risk of somebody from the outside trying to influence your model training with f.ex. Altered training data.
You can version your models via the model registry. If you have multiple different use cases for the same model architectures with differences being in the datasets used for training it’s easy to select the suitable version from SageMaker UI or the python SDK and refactor the pipeline to suit your needs. With this approach, the aim is that each MLOps pipeline has a lot of components that are reusable in the next project. This enables faster development cycles and the time to production is reduced.
SageMaker Pipelines logs every step of the workflow from training instance sizes to model hyperparameters automatically. You can seamlessly deploy your model to the SageMaker Endpoint (a separate service) and after deployment, you can also automatically monitor your model for concept drifts in the data or f.ex. latencies in your API. You can even deploy multiple versions of your models and do A/B testing to select which one is proving to be the best.
And if you want to deploy your model to the edge, be it a fleet of RaspberryPi4s or something else, SageMaker provides tooling for that also and it seamlessly integrates with Pipelines.
You can recompile your models for a specific device type using SageMaker Neo Compilation jobs (basically if you’re deploying to an ARM etc. device you need to do certain conversions for everything to work as it should) and deploy to your fleet using SageMaker fleet management.
Considerations before choosing SageMaker Pipelines
By combining all of these features to a single service usable through SDK and UI, Amazon has managed to automate a lot of the CI/CD work needed for deploying machine learning models into production at scale with agile project development methodologies. You can also leverage all of the other SageMaker products f.ex. Feature Store or Forekaster if you happen to need them. If you’re already invested in using AWS you should give this a try.
Be it a great product to get started with machine learning pipelines it isn’t without its flaws. It is quite capable for batch learning settings but there is no support as of yet for streaming/online learning tasks.
And for the so-called Citizen Data Scientist, this is not the right product since you need to be somewhat fluent in Python. Citizen Data Scientists are better off with BI products like Tableau or Qlik (which use SageMaker Autopilot as their backend for ML) or perhaps with products like DataRobot.
And in a time where software products are high availability and high usage the SageMaker EndPoints model API deployment scenario where you have to pre-decide the number of machines serving your model isn’t quite enough.
In e-commerce applications, you could run into situations where your API is receiving so much traffic that it can’t handle all the requests because you didn’t select a big enough cluster to serve the model with. The only way to increase the cluster size in SageMaker Pipelines is to redeploy a new revision within a bigger cluster. It is pretty much a no brainer to use a Kubernetes cluster with horizontal scaling if you want to be able to serve your model as the traffic to the API keeps increasing.
Overall it is a very nicely packaged product with a lot of good features. The problem with MLOps in AWS has been that there are too many ways of doing the same thing and SageMaker Pipelines is an effort for trying to streamline and package all those different methodologies together for machine learning pipeline creation.
It’s a great fit if you work with batch learning models and want to create machine learning pipelines really fast. If you’re working with online learning or reinforcement models you’ll need a custom solution. And if you are adamant that you need autoscaling then you need to do the API deployments yourself, SageMaker endpoints aren’t quite there yet. For references to a “complete” architecture refer to the AWS blog https://aws.amazon.com/blogs/machine-learning/automate-model-retraining-with-amazon-sagemaker-pipelines-when-drift-is-detected/