Mechanical Engineering Projects (ML): 5 reasons for their failure

You don’t have to look far to see what the root of an enterprise’s passion for artificial intelligence projects (AI) and machine learning (ML) is – data and much more! In fact, data is the king of a number of industries and companies need AI / ML to get a meaningful understanding of it.

For example, HCA Healthcare used machine learning to create a large data analysis platform to accelerate the detection of sepsis, while BMW used it to support automotive initiatives. Although AI / ML can bring great value to a business, your team must first navigate around the overall set of challenges.

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5 traps of a machine learning project you need to be careful

According to Guillaume Mutier, a senior data engineering architect at Red Hat, several common reasons for the failure of machine learning projects have emerged. Spoiler Warning: Many of these traps can be avoided.

1. Jump in without a clearly defined use case

You can also call it the “bright object syndrome”.

If businesses follow AI / ML just because it’s the hottest technology trend, they can spend a lot of time and money. Your AI / ML initiative shouldn’t be seen as a solution to a problem-solving problem – first and foremost, identify the real business problem you want to solve and ask yourself if it really benefits from the ML approach.

How to avoid problems

There are two main questions you should ask yourself before starting a car training project. First, what are the business goals of my organization? Second, can this goal be expressed as an ML problem?

Let’s say your goal is to increase customer satisfaction. Wonderful! Maybe you can use a machine learning algorithm to create better personalization and emotion analysis for our clients. From there, you can strategize your way to acquire the right talent, gather the right information, find ways to measure success, and more.

What you want to avoid is a situation where you fall in love with the idea of ​​machine learning so much that a less expensive solution and a sane solution to your business problems are overlooked. For example, are artificially provided chats really the best way to provide better customer service, or is there an easier way to improve customer service skills in your business?

In other words, the potential commercial value of your ML project should be considered first.

2. Projects do not have access to relevant information

Data is a key component of all AI / ML initiatives – it is needed for training, testing, and operational models. However, in reality its collection of data is a thorn in the side of many of the company’s ML projects. This is because most businesses generate large amounts of data and do not have an easy way to manage or use it. In addition, most enterprise data is scattered between internal and cloud data stores, depending on compatibility or quality control requirements, which makes data consolidation and analysis even more difficult.

The data silo acts as another barrier. Data silos – data sets that are stored by one team but not fully accessible to others – can develop when teams use different tools to store and manage a data set. But they can also reflect the organizational structure.

How to avoid problems

Many organizations benefit from automating data pipelines to connect all different data sources throughout the enterprise. Data pipelines can help you in the collection, preparation, storage, and access to data sets to develop, study, and summarize AI / ML models.

You can think of a data pipeline as a set of processing activities that consists of three main elements: source, processing step, and destination. Standard application programming interfaces (APIs) and high bandwidth and low-cost networking also facilitate data access throughout the AI ​​/ ML life cycle.

Integration with open source flow tools, processing and analysis such as Apache Spark, Kafka and Presto can help you in the effective management of your data. Data management capabilities and security features should also be part of your device decisions.

3. Data scientists do not work with business teams

Let’s say you are in perfect condition for your car learning project and have hired the best knowledgeable scientists to work on your car learning project. Your data scientists have begun to study the model with all the data you have collected, and the work is progressing rapidly.

Besides, according to Mutier, the story often doesn’t end here.

This is because one of the biggest things he has observed that companies make mistakes with machine learning projects in the last two years has to do with silos – especially when they hire a group of people with machine learning doctors to hire them. keep locked in a remote room. from entrepreneurial people and far from the applications that place the models they have developed.

This has a profound negative impact on the machine learning project, as, as discussed above, the fragmented structure leads to the data silos. Data scientists cannot independently lead production operations, and looking at different data sources to study a model does not generate any meaningful insights.

How to avoid problems

Consider the MLOps approach. Machine Learning Operations (MLOps) is a practice that aims to improve the life cycle management of an ML program in a sustainable, collaborative, and scalable way by combining processes, people, and technology. It shares the same principles of DevOps and GitOps with some key differences.

A key part of the MLOps process is building teams with a diverse set of skills – not just scientific knowledge skills – and empowering them to work together as a unit to achieve common goals.

In addition to MLOps technology, teams should consider the use of CI / CD pipelines to implement automation and ongoing monitoring throughout ML life. Furthermore, using Git as a truly central source for all codes and configurations – and often execution – can give teams additional compatibility and repeatability throughout the organization.

4. Infrastructure is inflexible

AI / ML models, software, and applications require infrastructure to develop and deploy.

Unlike training settings, where machine learning is often research-oriented and solves problems in a controlled environment, business settings need to have a more sophisticated infrastructure. Many of the moving parts include data collection, data testing, and model monitoring. Your ML infrastructure not only allows data scientists to develop and test models, but also serves as a way to place models in production.

And as you can probably imagine, without a flexible infrastructure, your machine learning project could fall on deaf ears. This is because the ML infrastructure supports each stage of the machine learning process. It affects how much time data scientists spend on DevOps tasks, communication between tools, and so on.

If you want to design, test, deploy, and manage AI / ML models and applications in the same way across all parts of your infrastructure, think about the cloud hybrid method.

How to avoid problems

If you want to design, test, deploy, and manage AI / ML models and applications in the same way across all parts of your infrastructure, think about the cloud hybrid method.

A hybrid cloud model allows you to combine local internal data centers and private clouds with one or more public cloud services, a hybrid cloud model can improve computer performance, increase your agility now and later, and give you “the best of both worlds” – to give. private cloud.

But why is a hybrid cloud so important to your ML infrastructure? Simply put, the hybrid cloud approach gives you more flexibility for your machine learning infrastructure. For example, you can store some data sets indoors (for compatibility reasons), but use a mass cloud to install the complex AI stack infrastructure. By using a public cloud provider, your data scientists should not have to spend time configuring hardware and other tools and allowing them to focus on the actual work of data science.

In another example, you can use built-in technology for pre-testing, while allowing cloud providers to perform heavy lifting to develop ready-to-manufacture models. Community clouds can also help data scientists develop and deploy AI / ML models by integrating open source applications with partner technology.

5. Managing a software stack is difficult

Mechanical learning environments can be… chaotic. Software stacks used in such environments are complex, sometimes powerless, and, above all, constantly evolving.

For example, you can use open source tools such as TensorFlow and PyTorch next to the ML framework, Kubeflow or MLflow next to the platform, and Kubernetes for infrastructure. And all of these tools need to be constantly maintained and updated.

This can open the door for incompatibility. For example, if you use a set of data in TensorFlow 2.5 to teach a model, and your colleagues use the same set of data in TensorFlow 2.6, you will get different results.

If you are not sure that everyone uses the same tools and hardware in different learning environments, it is difficult to reliably share code and data sets between (and within) teams. Problems with coordination, transfer, and dependency management can arise, creating multiple points of potential failure along the way.

[ Want more advice from ML experts and your peers? Read also: AI/ML workloads in containers: 6 things to know. ]

How to avoid problems

More about artificial intelligence

Tools like Red Hat OpenShift Data Science, a managed cloud service that helps scientists and developers develop intelligent applications in a sandbox, can add flexibility.

Thus containers can be used as part of a machine learning environment. Team members can clean a containerized application between environments (development, testing, production) while maintaining the full functionality of the application. Containers can also simplify collaboration. Teams can replicate and share container images with the ability to create variations that track changes for transparency.

When you receive containers, you need an efficient way to manage and scale them, where a container orchestration tool like the Kubernetes and Kubernetes operators comes into play. Kubernetes Enterprise platforms such as Red Hat OpenShift are also of interest for AI / ML development tasks.

[ Read also: OpenShift and Kubernetes: What’s the difference? ]

If your organization does not want to manage and maintain Kubernetes itself, you may want to consider cloud services such as OpenShift Dedicated, Red Hat OpenShift on AWS (ROSA) and Microsoft Azure Red Hat OpenShift (ARO).

[ Want to learn more? Get the eBook: Modernize your IT with managed cloud services. ]

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