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How to Choose the Best MLOps Platform For your Organization

In this special guest feature, Manasi Vartak, Founder and CEO of Verta, takes a look at the MLOps space and highlights the options available in the market. She also came up with a list of essential criteria for an MLOps platform to be evaluated. Verta is an MIT-spinoff building software to enable high-velocity machine learning. The Verta platform enables data scientists and ML engineers to robustly version ML models, collaborate and share ML knowledge, and when models are ready for graduation, to deploy and monitor models in production environments. Verta grew out of Manasi’s Ph.D. work at MIT on ModelDB, the first open-source model management system deployed at Fortune-500 companies. Manasi previously worked on deep learning for content recommendation as part of the feed-ranking team at Twitter and dynamic ad-targeting at Google.

MLOps (Machine Learning Operations) facilitates the collaboration between data scientists, ML engineers, and IT operations. The idea behind this practice is to have one place to deploy, manage, and govern machine learning models, in order to increase efficiency and lower failure rate.

Simultaneously with the term, MLOps emerged different providers of MLOps platforms, helping enterprise data science and DevOps teams to take models from research to production while simplifying the ML development process. If you’re part of a data science team looking for a way to bring more models into production and organize your ML effort, you must have come across a variety of tools.

Let’s take a look at the options available in the market. These platforms can be put under four categories:

  • Open-source – There are several open-source options available, like kubeflow, ModelDB, Seldon, and mlflow. You can pick the open-source products and stitch them together. Companies looking to build their own custom MLOps solution may choose this route. However, be wary of the implementation effort and time-to-market impact. If available, do sign up for the enterprise support plan to successfully run these products at scale.
  • Cloud providers – Most of the popular cloud providers also offer solutions to help manage the entire ML life cycle including MLOps: from data preparation to annotation to model training to deployment. The kitchen sink approach may make things overly complex, but it’s worth considering if you are looking for a one-stop-shop solution.
  • Model training platforms – Several popular data platforms came into existence a few years back trying to solve data preparation and training needs. As the industry matured, the problem shifted to operational challenges and some of these platforms with model training as the core focus have now forayed into building MLOps capabilities.  
  • Platform with MLOps as the core offering – These are the new breed of MLOps platforms with a primary focus on solving machine learning operations, monitoring, and deployment issues. MLOps being their core competency, these platforms deliver specialized features and architectural principles purpose-built for running machine learning at scale.

Doing due diligence while choosing an MLOps platform that best suits your organizational needs can be time-consuming but highly necessary. We recommend to go through a pilot phase and thoroughly evaluate your top choices. Include key users in the decision and establish clear objectives and success metrics for the pilot.   

This is why we came up with a list of essential criteria for an MLOps platform to be evaluated. Let’s get started!

  1. Data scientist-friendly – The platform should work out of the box with popular ML frameworks so you are not hindering innovation by restricting your data science teams to work on specific tools and frameworks.
  2. Ease of use – Data scientists are not software engineers and should not worry about deployment pipeline setup, infrastructure component tuning, or following complex steps for model instrumentation or custom dashboard creation. Hence, look for these three aspects when evaluating a platform: easy to install, easy to set up, and easy to customize.
  3. Interoperability – MLOps is a new layer in your software stack, and the platform should play well with your existing ecosystem of model training, deployment pipeline, monitoring, and approval workflow tools. A platform with OOB integrations and developer-friendly open APIs can make the experience seamless and avoid disruption of the current workload.
  4. Reproducibility – Managing a model life cycle begins with the ability to version and make them accurately reproducible. Reproducibility is critical whether you are collaborating with team members, debugging a production failure, or iterating an existing model. Ensure that the MLOps platform is built with reproducibility as a first-class citizen.
  5. Scalability – When we talk about production operations, a scalable platform is a must-have. Choose a platform that not only meets your current needs but can scale for the future. Look for a platform that can elegantly scale for both real-time and batch workloads, serve high throughput scenarios, scale automatically with the increasing traffic, manage cost versus user experience efficiently, follow safe deployment and release best practices.

Now that you have the key questions to ask when looking for a platform to successfully implement MLOps in your organization, you can research the different platforms out there and see to which extent they match with your organization’s requirements and resources. Happy comparing!

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