Read Time 5 mins | Written by: Ryo Hang | Yuan Zhao
As we journey through this comprehensive blog series, we’ve identified major challenges in machine learning projects (Blog 1), showcased MLOps as a transformative solution to these hurdles (Blog 2 and Blog 3), and brought these principles to life with a real-world application (Blog 4). Now it's time to empower you, the reader, to make well-informed decisions about implementing MLOps solutions in your own scenarios, leveraging your deepened understanding of these concepts.
In this article, we're here to help you navigate the vast landscape of MLOps solutions. We will explore two of the primary paths you can take: end-to-end MLOps solutions and custom-built MLOps solutions, which establish an ecosystem of tools specifically tailored to your unique needs. Each approach has its pros&cons and will come down to your specific needs, the resources at your disposal, and your project's complexity and scale.
Now that you have identified which level your company is at, you can go with one of two MLOps solutions:
- End-to-end
- Custom-built MLOps solution (the ecosystem of tools)
End-to-end MLOps solution
These are fully managed services that provide developers and data scientists with the ability to build, train, and deploy ML models quickly. The top commercial solutions are:
- Microsoft Azure MLOps suite:
- Azure Machine Learning to build, train, and validate reproducible ML pipelines
- Azure Pipelines to automate ML deployments
- Azure Monitor to track and analyze metrics
- Azure Kubernetes Services and other additional tools.
- Google Cloud MLOps suite:
- Dataflow to extract, validate, and transform data as well as to evaluate models
- AI Platform Notebook to develop and train models
- Cloud Build to build and test machine learning pipelines
- TFX to deploy ML pipelines
- Kubeflow Pipelines to arrange ML deployments on top of Google Kubernetes Engine (GKE).
Custom-built MLOps solution (the ecosystem of tools)
End-to-end solutions are great, but you can also build your own with your favorite tools, by dividing your MLOps pipeline into multiple microservices.
This approach can help you avoid a single point of failure (SPOF), and make your pipeline robust — this makes your pipeline easier to audit, debug, and more customizable. In case a microservice provider is having problems, you can easily plug in a new one.
The most recent example of SPOF was the AWS outage, it’s very rare but it can happen. Even Goliath can fall.
Microservices ensure that each service is interconnected instead of embedded together. For example, you can have separate tools for model management and experiment tracking.
Finally, there are many MLOps tools available, I’m just going to mention my top 7 picks with one honorable mention:
The journey through this blog series underscores the importance of understanding your specific needs and challenges when embarking on machine learning projects. This understanding is the foundation upon which you can build a suitable MLOps solution, whether that is an end-to-end solution for comprehensive management or a custom-built ecosystem of tools that gives you greater flexibility and customization. Each option carries its unique advantages, and your decision must hinge on your unique setup and the lessons we've covered throughout this series.
This decision-making process is, in itself, a practical application of all we've explored across the series. From assessing and understanding the challenges (Blog 1), adopting MLOps practices (Blog 2 and Blog 3), to witnessing their power in a real-world application (Blog 4), every step informs your ultimate decision and strategy.
Your path on the MLOps landscape is a crucial strategic decision that can define the efficiency and effectiveness of your machine learning endeavours. Make sure to leverage the lessons learned through our journey to ensure a successful machine learning implementation and navigate the rapidly evolving terrain of MLOps with confidence.