Common mistakes to avoid while executing Machine Learning solutions

[Paul_Santilli]

HPE OEM customers are looking at ways to invest in infusing Machine Learning (ML) into their business processes. However, they face many challenges in developing solutions while using the latest technology. Machine learning presents a multitude of issues that companies need to manage, such as implementing problems for OEMs and their customers, security concerns, legacy hardware, siloed data and workflows, inefficient processes, competitive issues, and daunting costs.

When launching transformative technologies such as machine learning, organizations assemble all the pieces of their machine learning models and move the implementations into production. According to Rajesh Vijayarajan, a distinguished technologist at Hewlett Packard Enterprise, once a machine learning model is put into production, there is the issue of data drift and the need to continually improve upon and update the model as the dataset the machine learning was trained on expands. In addition to this, the concept of reproducibility, or explain ability, of the model—something critical in highly regulated industries. That would mean very rigorous versioning of all it takes to actually build that model, including the new network architecture and the specific version of the datasets that were actually put to use.

It takes a data engineer

Data engineering is key, when it comes to deployments comprising hundreds, or even thousands, of models. "The moment you say there is an ensemble of a few hundred models that are trying to solve a problem, what it really requires you to do is create what are called data pipelines for the whole consumption of that model to happen. It's essentially an output of another model that is feeding into it or some sort of a pre-processing logic", Vijayarajan explains.

In large, complex machine learning deployments, consistent data management capabilities like data tiering and time-based data decommissioning are critical. As ML is becoming an important tool in many industries and fields of science, its important organizations avoid common mistakes.

There are many challenges associated with Machine Learning and customer solution technology advancements. HPE OEM Solutions has the products and services that can align your business goals with the latest in Artificial intelligence and edge-to-cloud solutions that will not only solve your current problems, but prepare you for digital transformation journey towards solutions of the future. At HPE, the OEM team has been consistent in driving innovation and Machine Learning that has opened up an accelerated path to stay ahead of the competition and further enhance the value of your solutions.

Click here to learn how HPE Artificial Intelligence and Data-Driven Services will help you in every step of the way from big data foundation to AI-driven automated business outcomes.

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Paul Santilli
Hewlett Packard Enterprise

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