Why Most Machine Learning Initiatives Fail and What To Do About It

Many organizations see the value in Machine Learning initiatives and jump the train to adopt them quickly. Yes, ML can unlock significant opportunities for you. You can quickly scale your business, use data to make smart decisions, and offer next-level services to your clients. However, according to Gartner, 85% of these initiatives fail. Why? Because adopting ML is challenging, and doing it without a strategy, plan, or the right tools can be problematic.

To be ready and prevent the ML initiative from failing, you need to know what obstacles await you down the road. Then, you need to know what you have to do to move forward. Here are the most common reasons why most ML projects fail and what you can do to ensure success.

Too complex pipeline for Machine Learning

For an ML initiative to succeed, you need to have a good pipeline architecture and ready your data. Every company is unique in terms of how far it has undergone the digital transformation process. What does it mean for you? Your company is probably already generating volumes of data; however, this data is not ready to be fed into an ML training model. 

More importantly, your pipeline streamlining process in machine learning can be too complex. The ML pipeline should automate the ML workflow. The data has to be transformed and correlated into a model. The more sources you use for generating a pipeline, the harder it becomes to transform the data. 

You will end up with this monolithic architecture which is impossible to scale:

• deploying multiple versions of the same ML model will take more time;
• expanding your model portfolio becomes hard;
• changing the configuration of the data source calls for scripts updates which you have to do manually.

It can cause a variety of problems, such as results not resonating with the goal. Ultimately, your machine learning initiative will fail.

How to overcome this challenge?  You will need to follow the best big data pipeline building practices to split up your ML workflows into modular parts. It will enable you to pipeline whatever modular parts you want to create different models. To efficiently do it, you will need a distributed messaging platform, preferably one that supports building real-time data pipelines such as Apache Pulsar. 

Failure to understand what the business is trying to achieve with Machine Learning

Many people think of ML as this one-size-fits-all solution. It is quite the opposite. ML is not capable of solving all of your problems. Would you use accounting software to boost the number of open emails for your next marketing campaign? You wouldn’t. The same applies to ML. Since this is entirely new tech to you, stop worrying about what it can or can’t do.

ML is an exciting new tech, but it still has many experimental use cases. We still don’t know whether it can efficiently be scaled both in terms of costs and effort. You would be throwing money into it while maybe there is a cheaper solution you can use for your particular problem. If you take the first problem that comes to your mind and use ML to solve it, you might not be satisfied with the results leaving you and your superiors with no option but to cut the project. 

How to overcome this challenge? Instead of thinking about how exciting it would be to have ML solutions solving problems for you, start thinking about your business problems. This is the step that should come way before you even begin working on your ML initiative. Here is what you should do:

• Define your business problem(s);
• List out all the desired outcomes you expect;
• Check whether there are proven solutions in the market;
• Can you measure the benefits and ROI;
• Can you scale it to solve some other business challenges?

The process will help you identify and define your pressing issues and desired outcomes. It is more than enough to assess whether ML is the best way to address your problems and guarantee the success of your initiative should you decide to pursue it. 

The inability of the data scientists to communicate with DevOps and MLOps

Finally, we arrive at the third most common reason why most ML initiatives fail – data scientists, DevOps, and MLOps working in silos. How does it look in practice? 

Let’s say you can afford to hire data scientist(s) and have internal DevOps and MLOps teams. Yet, they are completely cut off from one another. 

Your data scientists are working on a project building ML models. To do it, they use their preferred tools. In their tech stack, you can usually find open-source solutions such as Jupyter notebooks. Meanwhile, they don’t communicate with DevOps nor MLOps. It means that they don’t know anything about the scalability, processing, memory, deployment, and training capabilities of the architecture DevOps and MLOps built.

The problem can emerge in three different phases. 

The first phase is when data scientists need to transition their work to a production environment, especially if it has to be distributed at scale. 

The second phase is when the new features need to be added to the ML model. The model has to be retrained, which creates more work and generates more costs.

And the third phase refers to achieving versioning, quality control, repeatability, and reliability through the ML model lifecycle. All of it becomes extremely hard when DevOps has to manage dozens of code artifacts, data sets, and learning models. 

How to overcome these challenges? You need to bring data scientists, DevOps, MLOps together through the utilization of solutions engineered to facilitate collaboration and speed up ML operationalization. You will need to ensure the following:

• Bring data scientists and DevOps together to standardize machine learning lifecycles;
• Create easily reproducible lifecycles;
• Regulate model management, tracking, monitoring, and versioning;
• Improve collaboration and knowledge-sharing;
• Streamline pushing models into production.

Machine Learning can help organizations in various verticals leverage their data, improve operations, and unlock new opportunities. However, if you take ML adoption too lightly, you can use too complex pipelines, fail to understand your goals, or keep data scientists, DevOps, and MLOps in silos. 

In either case, your ML initiative will fail. If you want to ensure the success of your ML initiative, consider checking out PandioML. This managed solution leverages cutting-edge AI orchestration platforms to connect data to pipelines and deploy them in production.