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Why ML Models Rarely Reach Production and What You Can Do About it

Man yelling at computer | Wallaroo.AI

Despite billions poured into AI a staggering number of machine learning models never see the light of day in production. Discover actionable strategies to ensure your models aren’t left on the sidelines.

Companies around the globe are pouring a collective $700 billion into AI and analytics, and the pressure is on businesses to successfully harness data-driven intelligence to get a return on their hefty investment. 

The problem is: only 13% of data science projects make it into production.

A 2020 State of Enterprise Machine Learning (ML) survey found that the main obstacle is an excessively long road to deployment. The average time it takes an organization to get a single ML model into production is anywhere between 31 and 90 days — with some companies spending over a year on productionizing.

But the challenge isn’t just while deploying models. Data teams also have trouble monitoring how their models are performing against live data, testing and redeploying improved models, and successfully scaling their AI/ML operations. All of this stops organizations from extracting value from their costly data science investments, and many find their ROI falling despairingly short of expectations. 

With AI becoming an increasingly critical component for organizations to stay competitive, the need has never been greater for teams to efficiently deploy, measure, and monetize their ML. So, to keep your data science projects out of the to-be-deployed pile, get to know the main challenges of productionizing ML and how you can build a smooth-running AI pipeline so no model gets left behind.

The challenges of productionizing ML


ML models can only help your customers and drive business value after you get them out the door. But, from the team handling the data to the technology stack deploying the models, the average ML model’s path to production is riddled with roadblocks. 

Siloed data teams

Data science is a team sport. Ideally, everyone collaborates with everyone else: from data scientists and engineers to BI analysts and DevOps. Except what usually happens is a data scientist hands their algorithms over to a data engineer and is essentially left in the dark until weeks or months later when the model reaches production — if it gets there at all.

The data engineer, who already has enough on their plate, often has to rewrite the algorithms in Java and then push them through a slow, tedious ML pipeline  — or has the difficult task of getting Python code into production at scale. The operations team keeps an eye on metrics but rarely keeps the data scientists in the loop. By the time a business head complains about KPIs, it’s been months and the data scientist has already given the engineer several new and improved models that are waiting to be deployed. Rinse and repeat.

To add insult to injury, this severe disconnect throughout the organization also makes it difficult to scale ML operations, since each team has its own tooling, frameworks, and languages that often don’t play well together.

No MLOps strategy

More often than not, organizations don’t handle data science projects with the same dedication as traditional development projects. Everyone has heard of the importance of DevOps, but what about MLOps

MLOps combines machine learning development with business knowledge for a more efficient ML lifecycle. It essentially applies DevOps principles to ML systems. Without it, AI pipelines typically lack the necessary monitoring, versioning, scalability, and repeatability to guarantee consistent results.

In fact, ML model versioning and reproducibility is the second most cited challenge among companies of all sizes. Since data changes so quickly, a model’s accuracy begins to degrade the second it’s put into production — which means that without best practices to ensure continuous monitoring and retraining, ML models rapidly sink into obsolescence and fail to deliver the value they were created for.

Overly-complex technology stack

According to Gartner, “75% of enterprises will shift to operationalizing AI by the end of 2024, driving a 5X increase in streaming data and analytics infrastructures.” The problem is that, currently, these infrastructures are messy, expensive, and hard to scale. 

In response to the lack of viable AI and ML solutions, most organizations have had to cobble together different open-source tooling that often introduces extra steps and tedious workarounds into their ML pipeline (like re-engineering models into Java). Furthermore, most off-the-shelf platforms lock teams into proprietary or limited ML frameworks, slowing down progress and making it difficult to put ML models into production at scale.

Not only do these inflexible technologies make it tough for teams to productionize their ML, but the complexity of using them makes it impossible for business brains to understand the impact of their AI investments. Plus, most platforms don’t support real-time data streaming, which leaves businesses unable to take advantage of current market trends and also blocks real-time operations like dynamic pricing, fraud detection, predictive maintenance, and cybersecurity.

How to make sure your ML reaches production


As with any development project, the core components of an effective AI pipeline are the people, processes, and technologies. Here’s what you need to get ML models from the hands of your data scientists into a production environment.  

Bridge the gap between data science and operations

Production AI involves several skill sets and everyone on your data team has a critical role to play. They should all understand the different components of the pipeline and maintain a continuous feedback loop for smoother operations and faster innovation.

It’s also vital to have the right people in the right roles so that each team member can focus on what they do best. If your poor data scientist is spending 25% of their time on deployment efforts, then that leaves them less time for actual data science.

While data scientists should always be involved even after models are in production, most of their time is better spent on innovation as well as proactively retraining, testing, and sharpening their predictive accuracy as new data rolls in. 

It’s worth noting that creating an AI-driven organization that embraces data and thrives on cross-functional collaboration isn’t just a matter of hiring new talent. For a long-lasting cultural shift, focus on company-specific training of both existing and new employees to bake in agile development and seamless communication from the start. 

Adopt MLOps Practices 

Machine learning model lifecycle

MLOps is usually the missing piece that keeps teams from falling into “model debt.” Machine learning demands faster iteration than traditional software, so if you want to compete in the era of AI and analytics, you’ll need an iron-clad pipeline that standardizes continuous monitoring, versioning, and retraining.

Monitoring is a particularly critical component. “Concept drift” puts your models at risk of churning out bad predictions as new data might not match the data the model was trained on. With monitoring baked into your pipeline, your team can detect this drift early on so data scientists can take corrective action before your business or customers can be impacted. Monitoring is also essential for production release (e.g. looking at model behavior in a staging environment before releasing to production).

MLOps also enables another important component: rapid experimentation and testing of new models. This lets data science teams define experiments, compare models against each other, and select the models that will lead to the highest ROI or business impact. Having this step in your pipeline ensures only the highest-performing models are put into production — rather than wasting your computing resources on duds.

Choose the right technologies 

Giving the right people the wrong tools can break your entire ML operation. Technology is what ties all your ML efforts together, so you need to choose your platforms and tools wisely. This generally means solutions that provide:

  • Flexibility to integrate with your existing tools and frameworks
  • Support for both real-time data streaming and periodic processing
  • Detailed analytics to measure and improve business impact
  • Enterprise features that enable easy AI and ML scaling

In 2019, Gartner assured that the “increased use of commercial AI and ML will help to accelerate the deployment of models in production, which will drive business value from these investments.” 

With more enterprise-class solutions popping up, organizations can finally move away from patchwork tooling and adopt a more uniform and flexible setup that allows rapid scaling, real-time data processing, seamless integrations, and robust model management — which open-source platforms currently lack.

Make Every ML Model Count with Wallaroo

End-to-end machine learning model deployment

With almost every company rushing to enhance their ML operations to sharpen their competitive edge, there’s simply no time for delays due to slow technologies and shoddy processes. 

Although not every company has the resources to optimize its operations. As MIT Review puts it:

“Most companies aren’t generating substantially more output from the hours their employees are putting in. Such productivity gains are largest at the biggest and richest companies, which can afford to spend heavily on the technology infrastructure necessary to make AI work well.” 

But a successful ML ecosystem shouldn’t be restricted to just the companies with the most resources to throw at it. 

Meet Wallaroo, an enterprise platform for production AI that levels the playing field for organizations of all sizes by making it fast, simple, and low cost to productionize ML models. Wallaroo flips the script by allowing you to:

  • Deploy models in seconds: Data science teams can swiftly upload, deploy, test, and iterate ML models using the open-source frameworks they already know. This cuts deployment time down to mere seconds, clearing out “model debt” and giving data scientists the confidence that their hard work will reach production as soon as possible.
  • Streamline MLOps: Wallaroo provides the tools you need for simplified monitoring, scalability, experimentation, and repeatability right out of the box. Plus, you can integrate Wallaroo with popular versioning and governance management systems for robust version control and model reliability. 
  • Leverage real-time data: As the fastest platform on the market for production AI, you can analyze data 100X faster and react to market changes in real-time to jump ahead of your slower-moving competitors.
  • Easily monitor performance: Built-in analytics and real-time metrics enable data teams to quickly track, measure, and iterate their models. The intuitive dashboard also gives business heads visibility into how their AI investments are performing, so you can make sure you’re always running the best models.
  • Scale at lower cost: Lightning-speed computing and the ability to run multiple models on a single server cuts infrastructure and maintenance costs by 80%. With a simple, unified setup that does much more using fewer resources, you can easily scale your AI and ML with a drastically lower investment.

Without a doubt, the companies that manage to put their ML models into production at scale will have a clear advantage over their competitors — and the lion’s share of trillions in potential revenue. With an integrated, intuitive system like Wallaroo, you can finally give your data team the technology they need to swiftly productionize ML, capitalize on their efforts, and squeeze the most business value out of each and every model.

Ready to make sure your boldest data ideas always go live? Get in touch to get started.

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