In my These elements require a certain degree of in-depth expertise, and they (usually) have well-defined metrics and established processes that are within our control.<\/p>\n Now it\u2019s time to consider\u2026<\/p>\n The human aspects of machine learning<\/p>\n<\/blockquote>\n Yes, this may seem like an oxymoron! But it is the interaction with people \u2014 the ones you work with and the ones who use your application \u2014 that help bring the technology to life and provide a sense of fulfillment to your work.<\/p>\n These human interactions include:<\/p>\n I also want to touch on the impact to people\u2019s jobs, both positive and negative, as AI becomes a part of our everyday lives.<\/p>\n As in my previous articles, I will gear this discussion around an image classification application. With that in mind, these are the groups of people involved with your project:<\/p>\n Let\u2019s dive right in\u2026<\/p>\n You may be a part of a team or a lone wolf. You may be an individual contributor or a team leader.<\/p>\n Whatever your role, it is important to see the whole picture \u2014 not only the coding, the data science, and the technology behind AI\/ML \u2014 but the value that it brings to your organization.<\/p>\n Your company faces many challenges to reduce expenses, improve customer satisfaction, and remain profitable. Position yourself as someone who can create an application that helps achieve their goals.<\/p>\n Deep technical discussions with your peers is probably our comfort zone. However, to be a more successful AI\/ML Engineer, you should be able to clearly explain the work you are doing to different audiences.<\/p>\n With practice, you can explain these topics in ways that your non-technical business users can follow along with, and understand how your technology will benefit them.<\/p>\n To help you get comfortable with this, try creating a PowerPoint with 2\u20133 slides that you can cover in 5\u201310 minutes. For example, explain how a neural network can take an image of a cat or a dog and determine which one it is.<\/p>\n Practice giving this presentation in your mind, to a friend \u2014 even your pet dog or cat! This will get you more comfortable with the transitions, tighten up the content, and ensure you cover all the important points as clearly as possible.<\/p>\n Creating a technical presentation is a lot like the\u00a0Feynman Technique<\/em>\u00a0\u2014 explaining a complex subject to your audience by breaking it into easily digestible pieces, with the added benefit of helping you understand it more completely yourself.<\/p>\n These are the people that deploy your application, manage data pipelines, and monitor infrastructure that keeps things running.<\/p>\n Without them, your model lives in a Jupyter notebook and helps nobody!<\/p>\n These are your technical peers, so you should be able to connect with their skillset more naturally. You speak in jargon that sounds like a foreign language to most people. Even so, it is extremely helpful for you to create documentation to set expectations around:<\/p>\n It is easy to have a more informal relationship with your MLOps team, but remember that everyone is trying to juggle many projects at the same time.<\/p>\n Email and chat messages are fine for quick-hit issues. But for larger tasks, you will want a system to track things like user stories, enhancement requests, and break-fix issues. This way you can prioritize the work and ensure you don\u2019t forget something. Plus, you can show progress to your supervisor.<\/p>\n Some great tools exist, such as:<\/p>\n We are all professionals, so having a more formal system to avoid miscommunication and mistrust is good business.<\/p>\n These are the team members that have the most experience working with the data that you will be using in your AI\/ML project.<\/p>\n SMEs are very skilled at dealing with messy data \u2014 they are human, after all! They can handle one-off situations by considering knowledge\u00a0outside<\/strong>\u00a0of their area of expertise. For example, a doctor may recognize metal inserts in a patient\u2019s X-ray that indicate prior surgery. They may also notice a faulty X-ray image due to equipment malfunction or technician error.<\/p>\n However, your machine learning model only knows what it knows, which comes from the data it was trained on. So, those one-off cases may not be appropriate for the model you are training. Your SMEs need to understand that clear, high quality training material is what you are looking for.<\/p>\n In the case of an image classification application, the output from the model communicates to you how well it was trained on the data set. This comes in the form of\u00a0error rates<\/strong>, which is very much like when a student takes an exam and you can tell how well they studied by seeing\u00a0how many<\/strong>\u00a0questions \u2014 and\u00a0which ones<\/strong>\u00a0\u2014 they get wrong.<\/p>\n In order to reduce error rates, your image data set needs to be objectively \u201cgood\u201d training material. To do this, put yourself in an analytical mindset and ask yourself:<\/p>\n Be sure to communicate to your SMEs that model performance is directly tied to data quality and give them clear guidance:<\/p>\n This also means that you should become familiar with the data they are working with \u2014 perhaps not expert level, but certainly above a novice level.<\/p>\n Lastly, when working with SMEs, be cognizant of the impression they may have that the work you are doing is somehow going to replace their job. It can feel threatening when someone asks you how to do your job, so be mindful.<\/p>\n Ideally, you are building a tool with honest intentions and it will enable your SMEs to augment their day-to-day work. If they can use the tool as a second opinion to validate their conclusions in less time, or perhaps even avoid mistakes, then this is a win for everyone. Ultimately, the goal is to allow them to focus on more challenging situations and achieve better outcomes.<\/p>\n I have more to say on this in my closing remarks.<\/p>\n These are the people you will have the closest relationship with.<\/p>\n Stakeholders are the ones who created the business case to have you build the machine learning model in the first place.<\/p>\n They have a vested interest in having a model that performs well. Here are some key point when working with your stakeholder:<\/p>\n Your role as an AI\/ML Engineer is to bring to life the vision of your stakeholders. Your application is making their lives easier, which justifies and validates the work you are doing. It\u2019s a two-way street, so be sure to share the road.<\/p>\n These are the people who are using your application. They may also be your harshest critics, but you may never even hear their feedback.<\/p>\n Recall above when I suggested to \u201cthink like a computer\u201d when analyzing the data for your training set. Now it\u2019s time to put yourself in the shoes of a non-technical user of your application.<\/p>\n End-users of an image classification model communicate their understanding of what\u2019s expected of them by way of poor images. These are like the students that didn\u2019t study for the exam, or worse didn\u2019t read the questions, so their answers don\u2019t make sense.<\/p>\n Your model may be really good, but if end-users misuse the application or are not satisfied with the output, you should be asking:<\/p>\n Inform your stakeholders of your observations. There may be simple fixes to improve end-user satisfaction, or there may be more complex work ahead.<\/p>\n If you are lucky, you may discover an unexpected way to leverage the application that leads to expanded usage or exciting benefits to your business.<\/p>\n Most AI\/ML model are considered \u201cblack boxes\u201d that perform millions of calculations on extremely high dimensional data and produce a rather simplistic result without any reason behind it.<\/p>\n The Answer to Ultimate Question of Life, the Universe, and Everything is 42. Depending on the situation, your end-users may require more explanation of the results, such as with medical imaging. Where possible, you should consider incorporating model explainability techniques such as LIME, SHAP, and others. These responses can help put a human touch to cold calculations.<\/p>\n Now it\u2019s time to switch gears and consider higher-ups in your organization.<\/p>\n These are the people who promote the use of your hard work. If your end-users are completely unaware of your application, or don\u2019t know where to find it, your efforts will go to waste.<\/p>\n The marketing team controls where users can find your app on your website and link to it through social media channels. They also see the technology through a different lens.<\/p>\n The above hype cycle is a good representation of how technical advancements tends to flow. At the beginning, there can be an unrealistic expectation of what your new AI\/ML tool can do \u2014 it\u2019s the greatest thing since sliced bread!<\/p>\n Then the \u201cnew\u201d wears off and excitement wanes. You may face a lack of interest in your application and the marketing team (as well as your end-users) move on to the next thing. In reality, the value of your efforts are somewhere in the middle.<\/p>\n Understand that the marketing team\u2019s interest is in promoting the use of the tool because of how it will benefit the organization. They may not need to know the technical inner workings. But they should understand what the tool can do, and be aware of what it cannot do.<\/p>\n Honest and clear communication up-front will help smooth out the hype cycle and keep everyone interested longer. This way the crash from peak expectations to the trough of disillusionment is not so severe that the application is abandoned altogether.<\/p>\n These are the people that authorize spending and have the vision for how the application fits into the overall company strategy. They are driven by factors that you have no control over and you may not even be aware of. Be sure to provide them with the key information about your project so they can make informed decisions.<\/p>\n Depending on your role, you may or may not have direct interaction with executive leadership in your company. Your job is to summarize the costs and benefits associated with your project, even if that is just with your immediate supervisor who will pass this along.<\/p>\n Your costs will likely include:<\/p>\n Highlight the savings and\/or value added:<\/p>\n Business leaders, much like the marketing team, may follow the hype cycle:<\/p>\n I hope you can see that, beyond the technical challenges of creating an AI\/ML application, there are many humans involved in a successful project. Being able to interact with these individuals, and meet them where they are in terms of their expectations from the technology, is vital to advancing the adoption of your application.<\/p>\n Key takeaways:<\/p>\n And most importantly\u2026<\/p>\n Don\u2019t let machines have all the fun learning!<\/p>\n<\/blockquote>\n Human nature gives us the curiosity we need to understand our world. Take every opportunity to grow and expand your skills, and remember that human interaction is at the heart of machine learning.<\/p>\n Advancements in AI\/ML have the potential (assuming they are properly developed) to do many tasks as well as humans. It would be a stretch to say \u201cbetter than\u201d humans because it can only be as good as the training data that humans provide. However, it is safe to say AI\/ML can be\u00a0faster<\/strong>\u00a0than humans.<\/p>\n The next logical question would be, \u201cWell, does that mean we can replace human workers?\u201d<\/p>\n This is a delicate topic, and I want to be clear that I am not an advocate of eliminating jobs.<\/p>\n I see my role as an AI\/ML Engineer as being one that can create tools that aide in someone else\u2019s job or enhance their ability to complete their work successfully. When used properly, the tools can validate difficult decisions and speed through repetitive tasks, allowing your experts to spend more time on the one-off situations that require more attention.<\/p>\n There may also be new career opportunities, from the care-and-feeding of data, quality assessment, user experience, and even to new roles that leverage the technology in exciting and unexpected ways.<\/p>\n Unfortunately, business leaders may make decisions that impact people\u2019s jobs, and this is completely out of your control. But all is not lost \u2014 even for us AI\/ML Engineers\u2026<\/p>\n There are things we can do<\/p>\n<\/blockquote>\n This is all part of being human.<\/p>\n The post Learnings from a Machine Learning Engineer \u2014 Part 6: The Human Side<\/a> appeared first on Towards Data Science<\/a>.<\/p>\n<\/div>\n\n
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Overview<\/h2>\n
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AI\/ML Engineer<\/h2>\n
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Understand the business needs<\/h2>\n
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Communication<\/h2>\n
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MLOps team<\/h2>\n
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Subject matter experts<\/h2>\n
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Think like a computer<\/h2>\n
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Stakeholders<\/h2>\n
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End-users<\/h2>\n
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Think like a human<\/h2>\n
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Explainability<\/h2>\n
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<\/strong>\u2014 The Hitchhikers Guide to the Galaxy<\/p>\n<\/blockquote>\nMarketing team<\/h2>\n
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Leadership team<\/h2>\n
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Conclusion<\/h2>\n
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Closing remarks<\/h2>\n
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