If you are a Machine Learning enthusiast or professional, you probably have some projects that demonstrate your skills and creativity. But how can you present them to potential clients, employers, or collaborators in an effective and engaging way? In this article, we will share some tips and best practices to showcase your Machine Learning projects to clients.
The first step is to select the projects that best match the needs and interests of your target audience. You want to show them that you can solve their problems, add value to their business, or inspire them with new ideas. Therefore, you should choose projects that are related to their domain, industry, or goals. For example, if you are applying for a job as a data analyst in a retail company, you might want to showcase a project that involves customer segmentation, market basket analysis, or demand forecasting.
It's important to select projects that are out of the ordinary, rather than taking the shortcuts of problems deemed too "easy". The best portfolios should illustrate your ability to reason and solve a challenge. On Kaggle, there are many competition projects, both solved and unsolved. Trying to solve some of them with a new technique to improve KPIs or model accuracy can be worthwhile, for example, for real-world problems (climate change, health, etc.). Then there are Open Source projects on GitHub or DagsHub. A track record of Open Source contribution often earns a nice profile in interviews. It shows that you appreciate taking part in making libraries or software accessible to all.
Know your target audience, know your target audience, know your target audience and give them precisely what they need to see. An oz of gold is worth more than a ton of bs.
I would say to *not* showcase a Machine Learning project as such, but instead show the customer a *successful* project. The customer should not care if the project was done using the most advanced LLM or a back of the envelope calculation. What we should care and strive for is to deliver results in a successful project, in a sustainable manner. I know, I know, showing the technical challenges that were solved using ML is important. We should never downplay or minimize the problems that we face or the solutions that we create, the complexity of was delivered. But in the end, what matters is, is the customer happy? Did we deliver something that created value? This is what we should take home everyday.
In my experience, one of the best ways to showcase your skills is end-to-end. Projects are important however they cannot be measured. A project is like a thesis or a school paper, I think the only way is to demonstrate and execute your skills through targets and a business proposal in real time. I work as a data scientist, I've been working with numbers for many years and when you want to show what you did, you write a project and that's fine, but what if you're not really the author? Technology evolves everyday and we are accustomed by inertia to do the same thing because "it has always been done this way" Newton's 1st law. Where does to point this: Originality.
Something I used to follow while applying for any job was making sure that my resume should completely align with the JD. A simple 2-step way to Tailor your Project Description in your resume - 1. Keywords: Incorporate keywords and phrases from the job description into your project descriptions. This can help your resume pass through applicant tracking systems (ATS) and catch the recruiter's attention. 2. Quantify Results: If possible, quantify the impact of your work on each project. Use metrics and numbers to showcase achievements. For example, if you improved website traffic, mention the percentage increase. 3. Relevant Skills: Highlight the skills that are relevant to the job. Modify, share and keep sharing :) #softwaredevelopment
The next step is to create a portfolio that showcases your projects in an organized and accessible way. A portfolio is a collection of your work that demonstrates your skills, achievements, and potential. You can use various platforms and tools to create your portfolio, such as GitHub, Kaggle, Medium, or your own website. The key is to make sure that your portfolio is easy to navigate, has a clear structure, and provides enough information about each project. You should include the following elements for each project: a title, a summary, a link to the code or the dataset, a description of the problem, the solution, and the results, and some visualizations or screenshots.
Create your portfolio it is very important for new aspirants to work in ML, but you need to create different solutions using different tools and models. I see every day the same samples and solutions, then you need think out of the box to create your personal portfolio.
You need to list the elements that your client can image and buying then transfer all your accuracy results based on them. Most importantly, your presents can not include any jargon no matter how “common sense” you think it is.
Develop your own brand by using a youtube channel or a blog like medium, whichever platform works best for you and your skillset. But go beyond github. Every project needs to be communicated as a story. Hiring manager's are interested in your thought process, how you approach a problem and how you gather resources to solve it.
-Ensure you highlight the portfolio on your LinkedIn. Share it as a post and pin it on top. -You can also refer to the website- ProjectPro and build your portfolio from scratch with their solved projects.
The third step is to explain your process and your decisions behind each project. You want to show your clients that you are not only good at coding and applying Machine Learning algorithms, but also at understanding the problem, choosing the right approach, and evaluating the outcomes. You should be able to answer questions such as: What was the motivation or the objective of the project? How did you collect, clean, and explore the data? What Machine Learning techniques did you use and why? How did you validate and test your model? What were the main challenges and limitations? How did you interpret and communicate the results?
One thing I have found helpful is to state the assumptions you've made when choosing your approach. Every approach comes from a different perspective and often with different assumptions. Explaining your decision behind each project is very much dependent on the assumptions you've made.
Hiring managers are more interested in how you think about a problem and come up with a solution. It doesn't have to be 100% right but it should showcase your thought process. What unique perspective can you bring to the organization/team.
And do not forget: there is always room for improvement or additional things that you would add given extra time and extra data. This goes in the direction of anticipating customer needs & requirements.
I have had an interesting project (ML in cybersec) recently. It's comprised entirely of challenges and limitations) To be honest, I consider it a personal failure and still believe I didn't give my best effort while working on it. On the flip side it is the cornerstone that makes the process of working on it so exhilarating and is the reason I'm eager to share it. I'm currently contemplating the optimal approach for presenting this project. In fact I'm of the view that revealing one's failures can be just as enlightening as showcasing accomplishments because sometimes such processes can be way more intriguing. Or, perhaps, even celebrate it, like X (not the ex-Twitter but ex-Google X, sounds funny) manifests in their "Moonshot Mindsets" ??
We start our projects by questioning our customers. Are they actually asking the right question? Once we validate their idea and we both understand the desired impact on their business, we analyze the data we have. Do we have what we need? Do we need to get additional data from other sources? From these two perspectives we propose the best mathematical model and algorithm. Once we measure the initial effectiveness of our models we proceed by either deep-diving into it or reshaping our initial thoughts based on our learnings.
The fourth step is to highlight your skills and your value proposition. You want to show your clients that you have the relevant knowledge, experience, and abilities to deliver high-quality Machine Learning solutions. You should emphasize the skills that are most important for the project, such as data analysis, programming, Machine Learning, statistics, domain expertise, or communication. You should also mention any certifications, courses, or awards that showcase your proficiency or recognition in the field.
Avoid overstating your skills and / or mentioning algorithms you are not experienced in. For example, don’t state that you know Computer Vision if you have only done a tutorial on the MNIST dataset. Technical interviewers will quickly spot this and challenge you.
We enjoy discussing technical aspects with our customers on a non-technical language. Once we make sure they understand what a model does, we both speak on the same language. Some of our customers even get creative and explain to us what they want to do using the new technical knowledge they acquired!
The fifth step is to demonstrate your impact and your contribution to the project. You want to show your clients that your Machine Learning solutions have a positive and measurable effect on the problem, the business, or the society. You should provide some evidence or metrics that show how your model improved the performance, the efficiency, the accuracy, or the satisfaction of the project. You should also explain how your model can be deployed, scaled, or integrated with other systems or applications.
In my experience, one common pitfall in ML projects is the sole focus on the technical aspects (like model selected, number of layers, LLMs, etc.) rather than looking for business/strategy aspects. How was the process to detect the pain points? How the process was streamlined? Is it scalable? What were the KPIs that are related to the project and the before/after? The thing I see the most is lots of solutions looking for problems instead of the opposite. Try to find a real pain and elaborate on what moves the needle after the project was delivered. If you prove that you can look as a business and solve a real pain, you will demonstrate you have the expertise on how to apply ML instead of just showing you know how to code ML stuff.
Gain a competitive advantage in the market by connecting your skills and knowledge to the actual business-value and quantifiable business outcomes of your prior projects. This is especially true for early-stage, high growth startups where Data Scientists can be asked to participate in technical sales calls and solution scoping. A good customer story tends to do the trick. A format I find effective: 1. The before scenario → and the negative consequences of it 2. The after scenario → and the positive business outcomes from it 3. The required capabilities → and the metrics to prove success
Our impact is typically measured in either sales, savings or both. Our products help our customers not only run the models, but also measure how useful they actually were for their business. Once we measure we also iterate. This way, we ensure our customers always see the benefits of working with us!
It is always about the BUSINESS impact, results and benefits. I would recommend stating the impact very early on after the project title and 1-2 sentences for project overview. You can then expand on the model performance and how it supported driving business value.
Framing the problem technically and commercially is extremely important. Technical framing sets the scope of the project. What datasets and systems are part of the project. How is the data quality and infrastructure Commercial framing explains impact of the project on the business. What is the new value, costs and risk. Together they set the expectation for decision makers about what to expect. It also sets the business case for investment decisions. It is important to onboard and enroll all stakeholders from an early stage to ensure buy-in, adoption and feedback throughout the project.
The final step is to engage your audience and make them interested in your work. You want to show your clients that you are passionate, curious, and creative about Machine Learning and that you are eager to learn more and collaborate with them. You should use a clear, concise, and friendly language to explain your projects and avoid jargon or technical terms that might confuse or bore them. You should also use visual aids, such as graphs, charts, images, or videos, to illustrate your points and make your projects more appealing. You should also invite feedback, questions, or suggestions from your audience and show that you are open to new ideas or challenges.
Before you engage your audience, make sure you KNOW your audience - research and preparation is half the battle. Consider partnering with a reputable SE or AE that has extensive experience selling ML/AI solutions. They can provide objective feedback, help refine your delivery, and even recommend strategies to engage your audience.
Understanding your audience and their expectations is fundamental to any form of communication or presentation. You might have created brilliant ML project but if you don’t understand the audience, their expertise you cannot sell. Knowing your audience’s background, interests, and level of knowledge helps you adjust your language, tone, and content. This ensures that your message is not just heard, but understood and retained. This will ensure the more engaging involvement of the audience, through Q&A, polls, or hands-on exercises. “Tell me and I forget. Teach me and I remember. Involve me and I learn.”
Apart from knowing your audience, make sure you explain how your projects and experience is relevant to that audience. Not everyone, especially non-technical audience, can make the mental connections between a solution and business challenge so it is important to tell the story from their perspective. They will also appreciate that you are already thinking about solving their specific problems.
Stay relevant! continuously update your portfolio as you complete new projects. This demonstrates that you're actively engaged in your field and staying up-to-date with the latest advancements.
Bonus Tips: Don't neglect to join discussion groups on LinkedIn, Discord and online events organized by your favorite firm. It's a great way to get noticed and easily connect with like-minded professionals and enthusiasts, or even boost your chances of landing your dream job, who knows? While crafting your portfolio, as a precaution, avoid using real datasets from companies where you've worked, so as not to expose sensitive data to the public. A good way to avoid this blunder is to anonymize or mask referential clues with special tools such as Informatica, Delphix or Accutive.
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Explainability and interprebility Also, give importance to explainability and interprebility to ensure client trust and acceptance of your AI solutions. One of the major resistance to use of AI is the lack of understanding how that results are obtained. With explainability, AI systems offer understandable reasoning behind their choices, bridging the gap between complexity and comprehension. Meanwhile, interpretability focuses on making the inner workings of models accessible, allowing experts to dissect decision pathways. As regulations highlight the need for explainable AI, embracing these qualities isn't just a technical consideration—it's a moral imperative to ensure responsible and collaborative AI integration in our lives.
One of the things that many developers (specially juniors) don't realize is that making a demo video of their project and then adding the link everywhere (LinkedIn, GitHub, portfolio, etc.) can make it really easy for others to see what kind of things you've been working on. A lot of times, you'll find yourself talking about your super-technical project with someone from non-technical backgrounds and in these cases, it really helps to have a demo video ready.
Showing versatility in manipulating and treating different datasets could be a crucial point to be defined as a ML expert. As an example, a PhD student having explored big data coming from the High Energy Physics field should demonstrate to apply Machine Learning techniques when other samples (Medical Physics, Condensed Matter Physics) are needed. In this case, the clients and companies will recognize a professional value as a Data Scientist.