What makes machine learning projects fail?
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What makes machine learning projects fail?

The gap between organizations that successfully employ data science and those that struggle to do so is growing. As we all know, artificial intelligence (AI) applications are based on data science and machine learning (ML), as it is through the study of data that AI learns how to interpret our world and react as we want. AI enormously influences businesses and their customers, yet companies require a new method of employing artificial intelligence to make a difference. While AI/ML may significantly benefit organizations, there are certain obstacles to overcome.

Don't invest for the technology's sake

Businesses that pursue AI/ML just because it's the latest trend in technology may waste a lot of time and money. Before anything else, figure out if you're dealing with a genuine business problem that would profit from an AI/ML solution.

Before getting started on a machine learning project, you should ask yourself two key questions. First, what are the corporate objectives of your organization? Second, is this goal capable of being articulated as an ML problem in terms that make sense to those outside the field?

You don't want to get so enamored with the notion of machine learning and overlook the common solutions to your issues. In other words, the business value of your ML project should be your top priority.

Providing the data access

Data is vital for every AI/ML project, especially training, testing, and running models. However, gathering real-world data is a challenge for many ML projects. This is because most organizations generate a lot of data and have no simple method to manage or utilize it. Furthermore, corporate data is split between on-premises and cloud data warehouses subject to compliance or quality control standards, making it even more difficult to combine and analyze.

Another issue is data silos. When teams use different tools to store and manage data sets, data silos – groups of data kept by one group but not entirely accessible to others – can emerge. However, they may also be a symptom of an entrenched organizational structure.

Many organizations benefit from integrating all of their data sources into a single pipeline to automate the data flow. Data pipelines may assist you in collecting, preparing, storing, and utilizing data for AI/ML modeling, training, and deployment.

A data pipeline is a series of processing activities with three fundamental components: a source, a processing stage, and a target. APIs and high-bandwidth, low-latency networks are being standardized to make it simpler to get data throughout the AI/ML lifecycle.

Integration with data streaming, manipulation, and analytics technologies may help you manage your data more efficiently. You'll also want to consider data governance and security when selecting tools.


Let's assume you've found a great use case for your machine learning project and that you've hired top-notch data scientists to work on it. Your data scientists have begun training the model using the collected data, and everything is proceeding according to plan.

Organizations frequently hire a group of people with machine learning PhDs and then keep them isolated in a distant room, away from the people and the applications that will use their models.

This has catastrophic consequences for machine learning projects since, as previously stated, a fragmented architecture generally leads to data silos. Data scientists cannot manage production operations independently, and looking at various data sources to build a model will not produce any useful insights.

Consider adopting an MLOps strategy. Machine Learning Operations (MLOps) is a method for improving the lifecycle management of a machine learning application in a robust, collaborative, and scalable way through a combination of methods, people, and technology. It has similar ideas to DevOps and GitOps. Teams with various skill sets must be built and empowered to work as one unit to accomplish shared objectives, which is a crucial element of the MLOps approach.

Teams should think about utilizing CI/CD (continuous integration and continuous deployment) pipelines to introduce continuing automation and monitoring throughout the ML lifecycle on the technology side. Using Git as a unified source for all code and configuration may also provide greater consistency and reproducibility across teams throughout the organization.

Infrastructure flexibility

AI/ML models, software, and applications require infrastructure for development and deployment. Compared to academy settings, where machine learning is typically research-based and solves problems in controlled circumstances, business environments need a more complicated infrastructure. This process has many moving elements, including data gathering, verification, and model monitoring. ML infrastructure allows data scientists to create and test models and serves as the mechanism for deploying them into production.

Your machine learning project will fail without flexible infrastructure. Because an ML infrastructure is permanent throughout the machine learning process, it significantly influences how much time data scientists spend on DevOps activities, how tools collaborate, and so on. Consider adopting a hybrid cloud approach if you want to develop, test, deploy, and maintain AI/ML models and applications in the same way across your infrastructure.

A hybrid cloud may assist you in boosting performance, enhance your agility now, and provide you with the "best of both worlds" of public and private clouds by allowing you to mix on-premises data centers and private clouds with one or more public cloud services. A hybrid cloud strategy gives you the most flexibility regarding your machine learning infrastructure. You can keep some data sets on-premises (for regulatory reasons) while building up the complex infrastructure for the AI stack with a public cloud. Your data scientists won't have to spend time configuring hardware and other tools, allowing them to focus on actual data science because you're utilizing a public cloud provider.

In another scenario, you may utilize on-premises solutions for preliminary testing before handing over the heavy lifting to the cloud vendors. Public clouds can also assist data scientists in building and deploying AI/ML models by linking open-source tools with business partners' platforms.

Management difficulties

ML development environments might be difficult to manage. Such systems employ complicated software stacks that are sometimes immature and dynamic. You may find yourself using open-source technologies such as TensorFlow and PyTorch on the ML framework side, Kubeflow or MLflow on the platform side, and Kubernetes for infrastructure. And all of these tools must be kept up to date and maintained. This can lead to inconsistency.

For example, if you're training a model on TensorFlow 2.5 with a dataset and your colleague is using the identical data set in TensorFlow 2.6, you'll get different results. If you don't ensure that everyone uses the same tools and hardware across multiple training environments, it isn’t easy to share code and data consistently. There may be issues with consistency, mobility, and dependency management, leading to numerous points of failure along the way.

For data scientists and developers, a managed cloud service that allows them to construct intelligent applications in a sandbox may be beneficial. Containers and machine learning development environments are also a perfect fit. Team members can easily move a containerized application between development, testing, and production environments while preserving complete application functionality. Containers can also make collaboration easier. With versioning features that track changes for transparency, teams may iteratively update and share container images.