As your company begins to proof out machine learning use cases and develop models, your ML teams need to be thinking long-term. How will you deploy, operate, and manage your models once you have them?

Making AI–minded decisions like this starts with this question: should I build or buy a machine learning management platform to operate my ML lifecycle?

Rest assured that determining to build or buy a machine learning management platform will drastically impact your company’s competitive standing in its industry. Time wasted up front will not be regained down the road, so extracting value from ML as soon as possible to maintain an advantage in your industry is paramount.

Build versus buy header graphic


New whitepaper out now

Algorithmia’s newest whitepaper, “Building versus buying an ML management platform” launches today. At its core, the paper is a tool for ML practitioners, heads of data science, and tech leaders engaged in strategic ML planning for their companies who are going to be involved in this decision.

This whitepaper is the third piece in our set of tools designed to help enterprises think about and prepare their technical teams to incorporate ML into their workflows. The first was our “5 missteps of machine learning every operations manager can avoid” webinar, which we then developed into an ebook entitled, “Enterprise machine learning’s five main challenges.”

The second tool we developed was the “10 KPIs and measures for ML success” webinar. The recording can be accessed here. An ebook on this topic is forthcoming.

For more information about enterprise machine learning and managing ML operations, please visit our Resources page.

How to use the build versus buy paper

This paper is broken into “build” and “buy” sections, walking readers through the components involved in each, including steps needed to ensure feasibility, and in the “build” scenario specifically, what resources are needed for a minimum viable product.

We intend this whitepaper to speak to leaders of machine learning teams, however, anyone involved in decisions involving machine learning applications at their organization can use this whitepaper as a tool to guide the conversation.

Download the whitepaper


What’s inside?

The whitepaper contains the following sections:

1. What building a machine learning management platform entails.

2. How to make a business case for ML and share it within your organization.

3. What you should evaluate when looking to buy an existing ML management platform.

4. Our assessment that purchasing an off-the-shelf platform is the smarter solution.

What’s next?


Algorithmia CTO, Kenny Daniel, and TWIML podcast creator, Sam Charrington will host the webinar, “Building versus buying: how to achieve ML operations and management” on 9 June at 10am PT. Register here.

Build versus buy: the guide

We’ll put together a decision checklist for companies working through ML plans as a resource to ensure they’ve considered all angles and potential costs.


From the conversation generated in the webinar, we’ll publish an ebook of all that we’ve researched and learned about the build versus buy decision.


Whitney Garrett