What are the most common machine learning challenges to address in your portfolio and resume?

Involving stakeholders not just to define the problem definitions but to clearly define success criteria which are realistic and measurable are key elements when understanding the problem definition. Not just defining what is in scope but also understanding what can be scoped out will add hugevalue while evaluating the performance.

Clearly defining what the problem is and what is expected from the ML model can be ambiguous without domain expertise. So first and foremost thing is to gain domain knowledge related to the problem that you are trying to solve, also involve domain experts to capture all relevant aspects.

?? Keep an eagle eye on problem definition! I'd stress that the foremost challenge in ML isn't merely algorithmic or computational but conceptual. Before delving into modeling and computation, clarity on the problem and its significance can make or break the success of a machine learning project. Often, many challenges stem from a misalignment in this foundational step. Why initiate a complex ML project without a crystal-clear roadmap of objectives and expected outcomes? ????

Any ML endeavor should start with a profound understanding of the problem landscape. Before crunching numbers or coding, one should ask: What are we trying to solve? How will this add value? And importantly, how will we measure success? Moreover, the definition shouldn't be a solo endeavor. Collaborate with stakeholders, understand their needs, and be willing to zoom out to see the bigger picture. By doing so, we not only find our way but ensure we're headed in the right direction.

A good machine learning portfolio indicates your capacity to create significant challenges that support organizational objectives. Clearly state the goals, information sources, assessment criteria, and presumptions for each project. Calculate the business value, such as improved productivity, cost savings, or revenue expansion. Put a focus on technical abilities, such as gathering crucial data from stakeholders to outline the issue. To effectively express your problem definition, use clear visualizations. Potential employers will notice your strategic thinking skills if you can show them how you use a methodical approach to discover problems that need addressing.

I’m based in Lusaka where data collection is very much in its infancy. Data preparation is more difficult as is hard to find large volumes of relevant data for the market (Southern Africa). This could change with time as new regulations have caused more awareness for businesses on the potential of strong data pipelines. Some examples include applying models for customer basket analysis predictions and general payment system reporting automation

Showcase one's talent for producing insightful data visualizations and documentation. Emphasize your knowledge of handling massive, unstructured datasets using technologies like sci-kit-learn, pandas, and SQL. You could measure the effect of one's data skills by focusing on important metrics including the amount of data processed, the speed of the preprocessing, and improvements in model performance. Potential employers will feel more confident that you can handle this critical machine learning pipeline stage if you can demonstrate your ability to expertly prepare difficult data for modeling.

Data preprocessing techniques should align with the type of data, business interpretation of the data and the problem we are trying to solve. For eg- say a company like Netflix or Amazon Prime creates a model to predict customer churn and one of the features used in the model is customer viewership. Say, the database table has NaNs/blanks for cases where customer had no viewership. It would be more meaningful to impute this missing value with 0 rather than mean, median, regression or any other missing value treatment technique. Similarly, outliers might seem to be unwanted data points for many projects but for cases like credit card spends, an out of pattern payment might indicate card theft. Hence, these techniques should be wisely chosen.

Data availability is a primary challenge in business specific problems . Before diving into data preparation, it's essential to have a sufficient dataset, which isn't always possible.? ?Data augmentation, a lifeline for such scenarios. Data augmentation artificially enlarges datasets by creating varied versions of existing data. Methods include few-shot labeling, where models are trained on limited labeled data to predict on larger unlabeled sets; adding noise to enhance model robustness. Synthetic data generation, using tools like SMOTE or GANs and recently LLMs can also assist in generating domain specific datasets.

Data preparation is a significant hurdle in machine learning, encompassing tasks such as data collection, cleaning, exploration, transformation, and splitting. It also involves addressing issues like missing values, outliers, imbalances, and biases. Demonstrating the ability to apply suitable data preprocessing techniques and providing clear documentation and justification for your choices is essential. In your portfolio and resume, emphasize your skills in handling data sources, ensuring data quality, visualizing data, and employing data engineering techniques in your projects.

In model selection, it's very important to understand the underlying nature of the problem and the characteristics of the data. Often, using state-of-the-art models can be tempting, but they might not always be the best fit for every problem. The appropriateness of a model is determined not just by its prediction accuracy but also by factors like interpretability, computational efficiency, and ease of deployment.

Considering models are like my friends. Each person is unique and has their own skills. The most affordable and friendly models are linear. SVMs are anxious but successful people. Unlimited potential, but plenty of parenting is needed with neural networks. Trees believe they are the experts. Ensembles are the popular ones. I can choose my favorites for each project by constantly contrasting and analyzing my model friends. But ultimately, it comes down to matching the appropriate friend with the right career.

In domains like Natural Language Processing (NLP) and Computer Vision, the size of the model emerges as a critical factor, often influencing the model's performance, applicability, and deployment feasibility. Consider the examples of LLMs like BERT, RoBERTa, T5, GPT-3, GPT-3.5, LLAMA, and PALM2, which are available in varied sizes and capacities. Each versions is tailored to cater different requirements. My experience leans towards advocating smaller, task-specific models for business data due to their computational efficiency, fine-tuning capabilities, cost-effectiveness, and task specialization. These models, while being less resource-intensive, can easily finetuned, becoming specialist in specific tasks, offering balanced efficiency.

Model selection is a significant challenge in machine learning, involving the choice of the most suitable model from a range of options. This decision necessitates weighing factors like complexity, accuracy, interpretability, and generalization concerning your data and objectives. Demonstrating your ability to compare and evaluate different models using techniques like cross-validation, testing, and validation sets, along with appropriate performance metrics and error analysis, is crucial. In your portfolio and resume, highlight the models you implemented, the libraries and frameworks utilized, and the achieved results as evidence of your expertise in model selection.

Model selection is the process of choosing the best machine learning model for a given problem, from a set of potential models. This involves comparing and evaluating different models using methods such as cross-validation, validation sets, and performance metrics. The goal of model selection is to choose a model that is both highly accurate and has low complexity, to avoid overfitting to training data. Proper model selection is important in machine learning, as a poorly chosen model can result in poor performance and unreliable results.

A quick tip: Tell me a time when all models gave the same results and you had to "tune" the model to achieve a higher accuracy. To stand out in such interview questions, one needs to demonstrate a bit of practical knowledge beyond auto-tuning syntax. While the "auto" libraries will give you the best parameters for a given criteria, they may not produce the most stable forecasts (which is important for building trust among business stakeholders and thereby adoption). A minor change in the test/train period or small changes in the tuned parameters may lead to a significantly different forecast (or prediction). Going with the 2nd or 3rd best option that produces a more business-relevant and stable could be a part of your tuning exercise.

Model tuning is a crucial challenge in machine learning, involving the adjustment of model parameters and hyperparameters to optimize performance and prevent overfitting or underfitting. Demonstrating your ability to employ methods like grid search, random search, or Bayesian optimization to identify optimal model settings and effectively report and visualize these findings is essential. In your portfolio and resume, highlight your strategies, criteria, and outcomes for model tuning to showcase your proficiency in this area.

Model tuning, also known as hyperparameter tuning, is the process of adjusting the hyperparameters of a machine learning model to improve its performance. Hyperparameters are the parameters that govern the behavior of a machine learning algorithm and are not learned from the data like model weights. Model tuning involves selecting the optimal configuration of hyperparameters based on performance metrics such as accuracy, recall, or precision. This process is often done through techniques such as grid search or random search, which systematically search the hyperparameter space to find the best combination that results in optimal model performance.

It is important to understand the nuances between deploying a model for batch inferencing, real-time inferencing and for in-memory inferencing. Some solutions require batch inferencing which can also be referred to as "Offline inferencing" where a bulk of predictions are inferenced at once via a script. The deployment for offline inferencing needs special considerations. For real-time inferencing, the deployment needs to facilitate record by record inferencing whereas in-memory inferencing is what is needed for a model such as KNN prediction where we need a bulk of data in memory to predict the classification. All of these nuances are to be considered depending on the problem we are trying to solve.

Although there are many tool that does this easily but it is highly recommended to doing this in a old fashioned way to understand what is happening in the whole process. Deploying your own model on an cloud provider, setting up logging and db services and building dashboards to observe the model’s response gave me lots of confidence.

Model deployment makes machine learning models operational in real-world applications. Key considerations include: Scalability: Platforms like AWS SageMaker, Google AI Platform, and Azure ML ensure models scale with demand. Reliability: Tools like Docker and Kubernetes maintain consistent performance. Security: Ensure encrypted data transmission and secure API access. Monitoring: Continuously assess model health and performance. Feedback loops help refine the model. Metrics: Track accuracy, latency, uptime, and other vital metrics to measure the model's impact.

In a resume it’s good to talk about the impact of the model in business or customer outcome terms. Eg Model reduced fraud by 7%. A slightly inferior way of putting it is in terms of model performance . But then be sure to use known measures like precision and recall. If you want to mention things like R-squared or F1 there is a risk the other party may not get it. You can use complexity measures like number of parameters but do remember it’s going to be hard for the other party to understand why it was important.

Effectively communicating machine learning models and their results is a final and crucial challenge. This involves tailoring your explanations to various audiences, whether technical, non-technical, or domain-specific, and providing clarity on your model, its assumptions, limitations, and implications. Effective communication may require the use of visual aids such as charts, graphs, or dashboards and engaging storytelling techniques. Your portfolio and resume should highlight the communication formats, channels, and skills you employed in your projects to demonstrate your ability to convey complex information in a comprehensible and persuasive manner.

Audience: Adapt explanations for technical, non-technical, or domain-specific listeners. Explanation: Clearly outline the model, its assumptions, and limitations. Visual Aids: Use charts or dashboards to simplify complex data. Storytelling: Make findings relatable with engaging narratives. Mediums: Use reports, slides, or videos based on context. Portfolio Tip: Highlight communication formats, channels, and instances where you influenced decisions. Effective model communication ensures broad comprehension and emphasizes data-driven insights.

I always believe the best way to explain any model and underlying mathematics in a manner that everyone understands what you did and why you did it helps a lot to any organization. The problem with the industry is skepticism, and the best way to address it is by adding a story layer to your mathematics and coding layers.

If you possess strong writing skills, venturing into Prompt Engineering offers a valuable, low-effort opportunity to enhance both your portfolio and resume. This area allows you to leverage your expertise in writing while diversifying your technical skill set, making you a more well-rounded candidate in the job market.

In my journey with model communication, collaboration has been key. I've teamed up with domain experts, like in healthcare, to contextualize model insights for specific audiences. After presentations, I value feedback to address misconceptions and refine the model further. I regularly engage in workshops to sharpen my skills and stay updated. It's essential to remember that machine learning insights extend beyond just models; on a customer behavior project, the real value emerged from understanding the reasons behind the data, illuminated further by qualitative research.