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How to Evaluate Different Machine Learning Deployment Solutions

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The real magic of ML projects happens when your models leave the drawing board and start working in the real world. However, the crucial step of machine learning deployment comes with its own set of hurdles. In this blog, we’ll explore how to sift through different machine learning deployment solutions, making the journey from model development to operational success less bumpy, and ensuring your business fully taps into the power of ML to meet your strategic goals.

The emergence of Big Data in decision-making to achieve strategic business objectives has led to machine learning (ML) becoming a key enabler for driving growth, achieving operational excellence, and bringing innovative products to market. This shift has come about as the primary obstacles for ML are being overcome: data engineering at scale and model development are no longer daunting to enterprises given the many efficient and simple solutions provided by cloud or 3rd-party vendors. As a result, ML went from something only the bleeding edge innovators (such as Netflix and Amazon) were doing, to now a strategic enabler for organizations in the “early majority” stage of adoption. 

However, enterprises soon find that building a machine learning model isn’t the end of the road but just the beginning of a new set of challenges: 

Because this is all so new, most enterprises do not have a pre-defined set of parameters to evaluate the different solutions for operationalizing ML models. As a result, they are not sure which attributes will allow their AI-enabled products and operations to scale in the long term as they add more models, use more data, or build more complex models. 

Criteria for evaluating ML deployment solutions

When we are educating our customers on how to understand the full lifecycle of ML in production, we ask them to evaluate each solution along with four main attributes: 

  1. Ease of deployment: Does your organization have a fast, repeatable, automated process or is it manual, involving several different steps and significant labor and time investment to deploy machine learning models into production? Is it easy to update models without downtime to the business?
  2. Computational efficiency: Can you scale to run larger ML pipelines with complex models and multimodal datasets without becoming too costly from a compute perspective? 
  3. Observability and Model insights: Can you quickly and easily detect model under-performance and data drift with many models in production? Do you have full visibility into who is updating models, what is running, what data was used to produce a prediction, and what the system performance is?
  4. Standardized workflow across data environments/tools: Are you replicating ML engineering effort for every new use-case or data science team across the organization instead of following a common repeatable process? Is your ML engineering team forced to develop new deployment pipelines for the different data tools different teams use?

How do different ML deployment solutions stack up?

We have in-depth benchmark evaluations against the most common deployment approaches like containerization, end-to-end MLOps platforms like SageMaker and Vertex, and managed Apache Spark but you can see a red/yellow/green summary (where red=blocker, yellow=passable but not performant, and green=highly performant) overview of how they perform along each of the four criteria.

Of course, you would need to run your own benchmark evaluation using your own data in your own data environment. If you are interested in running a test with Wallaroo to see how we can make last-mile ML simpler, faster, and more efficient, reach out at

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