ML projects don’t have to be this spooky
We’ve all seen these eerie statistics: 85% to 87% of Machine Learning (ML) projects fail (Gartner) or never make it into production (VentureBeat). Somewhere in the process, creepy ghosts and monsters emerge, preventing machine learning projects to make it from PoC to deployment. The ones that do manage to make it to deployment can’t operationalize at scale or become too expensive to maintain. Let’s explore some of the 8 spookiest reasons why machine learning projects fail, and how to avoid their predestined gory outcomes.
1. Haunting Talent Deficits
One of the first scary machine learning moments occurs when companies realize that they lack the necessary skills to embark on their machine learning project. The first experts they plan for, logically, are data scientists to build the models. They then realize that they also need data engineers to import and clean the data. IT to create the infrastructure, maintain it, and scale it. Cloud engineers to deploy the project on the cloud. It can be a gruesome moment – as these experts can be hard to find. Companies out-bid each other for the best skills leaving the others with the less experience ones, resulting in longer project times and sub-optimal results. Most companies only find out that they need an army of experts once the project is launched, which frequently leads to project abandonment.
2. Frightening Communication Challenges
Companies fortunate enough to have gathered the right skills, understand often too late, that the stake holders mentioned above have a hard time communicating and aligning their objectives. Aligning timelines and resources objectives between the exploration phase and the build/production phase seems more like a trick than a treat. During the exploration phase, business users elaborate their strategy and goals. The data scientists explore different ML model options to meet these goals. But since organizations don’t think about the production phase during the exploration phase, they run into the IT werewolves. IT is not ready to retrieve the data, put in place the infrastructure, support the production life cycle and the 24/7 required up time, or set-up the right security levels. These two worlds did not communicate at the beginning, resulting in the project being delayed or even sent to the graveyard.
3. Spooky Data
One of the most frequent scary moments is identifying and retrieving the needed data to run the machine learning models. Companies find that there isn’t enough data, or that they can’t connect to it, or that the data is of poor quality. They’ll also find that the data is unrepresentative of the machine learning use case and/or includes irrelevant features. Another spookiness is that the data cannot be retrieved for security, compliance, or technical reasons. Spooky data can lead to disenfranchisement with ML and complete project failure.
4. Creepy Infrastructure
Once companies figure out how to fix their spooky data challenge, they run into creepy infrastructure requirements. One of these is storage. Calculating and predicting your ML project’s data storage needs is not easy. ML algorithms generally work better the more data they have. Another is the computational capacity. The compute power required for production is much larger than the one needed for training, and often increases more rapidly than predicted. Security is another issue. As ML projects typically involve personal and/or otherwise sensitive data such as financial information or patient records, companies can’t afford to have a leaky ML infrastructure. Creepy infrastructures are another reason for project desertion.
5. Deadly Application
The organizations lucky enough to make it through to this stage, realize that they need an application to share the insights discovered by their machine learning models. They struggle finding the tools and skills needed to build a proper end-user application and often end-up carving out jack-o-lantern-like applications that are hard to sync with changing data and model outputs. These nightmare applications can easily delay the ML project and if not built correctly can lead machine learning initiative straight to the grave.
6. Bloody Deployments
Unfortunately, ML project deployment to production is often an afterthought, leading to bloody deployments. Deployment vampires come in many shapes and forms: They include unstable models, erratic model dependencies, unreliable pipelines, defective cloud infrastructures, and security miss-management. Companies realizing this late in the process, are drawn the unfortunate conclusion that ML is not for them.
7. Terrifying high availability
The fortunate few that make it this far run into terrifying replication and high availability zombies. They realize that planning for service downtimes can be expensive. Their cloud infrastructures providers offer service continuity at much higher costs than predicted. These costs include setting up and maintaining a reliable service replication system, implementing trustworthy failover processes, mirroring services across several regions while maintaining consistency across data centers creates additional bandwidth expenses.
8. Witching time
Witching time resilience can lead to the slow death of a machine learning project even if it managed to make it to deployment. Companies not able to manage their model maintenance, monitoring, updates, and modifications as their datasets expand and new data becomes available, are doomed! These monstrous issues are difficult to solve and take time to resolve.
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