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Digital Data Analytics & AI
8 minute read

Getting Started With MLOps: For Organizations

Adopting MLOps platforms gives organizations the tools they need to manage the machine learning (ML) lifecycle.

In This Article

Over the past few months, WWT’s AI R&D Program has been hard at work studying and testing many of the most popular and cutting-edge MLOps platforms. These platforms centralize the data science process with benefits throughout the entire lifecycle. We believe that organizations who master the automation, organization and architecture of these platforms will be the ones to lead the way in the future business of data science. 

MLOps platforms provide value by making the business of machine learning more efficient. These platforms ultimately lead to more productive data scientists and more performant models, accelerating the revenue generation or cost savings targets of the models themselves. They change the way organizations do data science work, centralizing the entire data science lifecycle and freeing your data scientists to spend more time on data science. It is a win-win for the organization! 

Now is the time to take on the world of MLOps, assess your own organization’s data maturity and build the lasting foundations of a modern data science operation. 

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What is MLOps?

The idea of “machine learning operations” originated as a solution to the problem of “hidden technical debt” which was first illuminated and coined in a 2015 paper. The idea is that during the traditional ML lifecycle, the process can be bogged down by the data dependencies, divergent iterations of the model and the decay of obsolescence after launch. Complications like these cost data science teams and organizations time and money to overcome. MLOps platforms relieve these drains by centralizing the disparate parts of the lifecycle. Like a company vertically integrating their business, MLOps platforms house and manage the entire ML lifecycle in one location, from data inflow to model outputs. 

Data science is an iterative building of sharper analysis and predictions. A mature data science project can take hundreds of iterations of the same model to reach a version which is productionable. MLOps aims to make the process of meticulous data science more streamlined and reproduceable.

End-to-end MLOps lifecycle

The first MLOps platforms blossomed from internal management systems developed in some of the largest companies in the world, like Google, Amazon and Microsoft, who boasted the most mature data science operations. The MLOps platform industry is now populated with many varied conceptions of what these tools can accomplish.

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What value can MLOps add? 

The value of MLOps lies in speed and agility gains for your organization’s data scientists. The hidden debts of their work are paid off before they sit down in front of a terminal because they can easily ingest data, tweak and save their models in an organized way — and analyze their results on one platform. For your organization, this means saved time and effort.

Machine learning models degrade over time unless constantly fed new data to reflect the ever-changing problem space. A main feature of MLOps platforms is that they allow for continuous training of models. New data can be added to the pipeline to retrain a model for up-to-date analysis. With the built-in performance trackers, scientists will also be able to, in real-time, monitor their product’s effectiveness. Problems can be caught early and addressed before they affect the analysis that is being done.

Another advantage of MLOps platforms over the traditional data science process is they make collaboration across workstreams simpler and frictionless. The engineering team building models is frequently not the team that productionizes the technology. This means the handoff to a production team can be slow and cumbersome as various cloud connections are validated and specific environmental conditions and package versions must be replicated, which take up both teams’ time to work out. Hosting the entire pipeline centrally solves many of these issues outright and the use of MLOps platforms enables the engineering team to present a more finished product with a pipeline already built in. 

The final, and often overlooked, benefit of MLOps is that using an MLOps platform allows models to outlive their creator’s tenure on the project. Too often, the entirety of a project’s data science lifecycle exists on a single data scientist’s laptop. They know the quirks of how to upload data and what setup their digital environment needs to run correctly. While this can work in the short-term, as soon as that data scientist rotates off a project or leaves an organization, their successors are not adequately prepared to carry on their work. MLOps platforms prevent this break in the chain of custody by hosting the model code, data pipelines and performance metrics in one place. When a new data scientist is onboarded to a project, the prior work is right there, and if setup correctly, the code will work the first-time they push “go.” 

Together, these benefits not only save time, effort and money but also produce higher quality work. MLOps is a conduit for the collaboration which drives sharper analysis and better engineering while making the set-up of a pipeline simple and more intuitive.

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How will it change your organization? 

MLOps platforms will make the lives of your data scientists, ML engineers and data engineers easier and help them work more efficiently. Less time spent tracking down documentation from the project’s former engineers is more time spent solving the organization’s next problem or experimenting with an innovative solution. 

The fresh approach and freedom from hidden technical debts has the added benefit of increasing retention rates for your organization’s talented data scientists. A problem many companies struggle with is data scientists growing weary of spending more time untangling a knot of snags and connection issues than on their machine learning work and it is another one solved by MLOps platforms.

Bringing these platforms into their workstreams will allow for more organic team growth and cohesion. The various teams that share stakes in the work will grow closer as the product becomes an integrated project. The change will be noticeable, like the difference between a potluck and a family cooking a meal together. Both produce similar results, but the act of sharing the meal’s preparation, instead of creating the discrete parts and assembling later, produces a more harmonious outcome. 

Finally, adopting MLOps platforms and maneuvering your organization’s ML work streams through the integrated pipelines will put your organization at the forefront of a new age of machine learning. Your data scientists will be armed with all the tools they need to do their best work and to pursue the reaches of what is possible through machine learning. 

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What are the startup costs? 

For most organizations, the largest cost will be the time it takes for your scientists and engineers to understand and familiarize themselves with the tools. With the likes of Microsoft and Google behind the most popular MLOps offerings, there is ample documentation and technical assistance available, but like any new product there will be growing pains. 

Luckily, many MLOps platforms easily integrate into existing cloud infrastructures. The options are ever expanding as compatibility across platforms increases with each passing month. In the first few weeks and months of the transition, there will need to be connections built, software installed and operations knowledge forged, but as the team adapts, the new tools will become indispensable.

Teams will wonder how they did their work without the platform and new teammates will only know it as the norm. The more use the platform gets, the quicker it will pay for itself in time saved and work efficiency. 

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Where should you start? 

This article is a good start, but to grasp the full potential of MLOps for your organization, you will need to understand your own data maturity. You can learn more about how to make that assessment from our AI R&D article, "Is Your Organization Ready for MLOps?”. It provides an explanation of how to measure your organization’s readiness and data maturity. An accurate evaluation of your organization’s data strengths is essential to preparing your MLOps buildout. 

As you come to understand your organization’s data maturity better, it is important to consider what your expectations are for an MLOps platform. If you have dedicated data pipeline architecture already and only need infrastructure to evaluate your models and visualize performance metrics, that is quite different than if you are starting from scratch. More than ever, now is an exciting time to be a consumer of MLOps because of the volume of options that exist today. 

The final piece of advice we can offer is to talk directly with your data scientists and data engineers, since they will be the ones utilizing this technology. Understanding their needs, concerns and excitement will pay-off in the long run and the buy-in you build will change your organization for the better. 

As you take on this new opportunity, WWT stands ready to be your guide in this space. If you are interested in furthering the discussion within your organization or ready to take the next steps, we'll be there to assist.

Get started today.