AI Project Cheat Sheet: Fast-Track Guide

A retail company wants to use AI to boost customer experience with a recommendation engine. They know AI can personalize product suggestions but are stuck on where to begin. Without a clear plan, they might collect the wrong data or pick a model that doesn't work for them. The AI project cheat sheet walks them through defining the goal, choosing the right data, and picking the best algorithm. It also points them to the right tools and platforms, saving them from guesswork. The cheat sheet helps them train and test the model properly so it's accurate and fair. When it's time to go live, it tells how to plug the engine into their website. After launch, it guides them on monitoring and tweaking the AI over time. Without this cheat sheet, the project could get messy, causing delays and wasted effort. For the same purpose, you can book a call to us.

Focusing On Different Roles

When compiling an AI project cheat sheet, we focus on the key people using it to ensure it’s practical and useful.

Data Scientists and AI Engineers

These people are building the AI models directly, so they need clear guidance on the technical steps. They'll appreciate the model selection, training, testing, and deployment sections. If they’re building a recommendation engine, they might need to know when to use collaborative filtering versus a deep learning model. Including tips on avoiding overfitting and selecting the right evaluation metrics would be key for them.

Project Managers

They’re in charge of keeping the project on track, so the cheat sheet needs to be clear on timelines, dependencies, and deliverables. They’d benefit from a step-by-step breakdown of tasks, like data collection, model building, and deployment phases, to make sure everything stays aligned with business goals. Providing checkpoints on performance milestones, like when to test or validate the model, will help them stay organized.

Business Stakeholders

The cheat sheet should also speak to non-technical decision-makers, such as product managers or executives. They need to understand the business value the AI project will deliver. If the project involves using AI for customer segmentation, a section that explains how AI can increase conversion rates or reduce customer churn is helpful. They need high-level guidance on measuring ROI, making sure the cheat sheet keeps the focus on how the AI initiative aligns with their business goals.

Data Engineers

These people are responsible for gathering and cleaning the data that feeds the AI models. The cheat sheet should include clear instructions on data quality requirements, storage, and preprocessing steps. If an AI system relies on time-series data, the engineers need to know how to handle missing values or ensure the data is formatted correctly for the model.

Ethics and Compliance Officers

In any AI project, someone has to ensure the model is ethical and complies with regulations. For these stakeholders, it’s critical to highlight sections on fairness, bias detection, and regulatory compliance. If the AI model is used for hiring decisions, the cheat sheet should include steps to ensure that the model doesn't unfairly discriminate based on gender or race.

End-Users

Whether it's a marketing team using AI to predict customer trends or a sales team using AI for lead scoring, the end-users of the AI tool need to understand how it works in practice. They don't need the technical details, but a quick guide would be helpful on how the AI's predictions or recommendations fit into their daily workflow. If a marketing team uses AI to send personalized offers, they need to know how to interpret the AI's output and act on it effectively.

DATAFOREST AI Project Cheat Sheet

We accumulated enough experience during our work on AI projects to compile a cheat sheet. This is a general vision of the process; it may differ depending on the circumstances, but the basic provisions are unlikely to change radically. Book a call, get advice from DATAFOREST, and move in the right direction. Below is an extended version of the cheat sheet shown in the figure.

1. Project Inception

• Start by figuring out what problem you’re solving with AI. If a retail store wants to use AI for inventory, they might want to tackle issues like overstock and running out of items.
• Decide what a win looks like. Maybe it’s cutting inventory costs by 20% or making stock levels more accurate.
• Determine who needs to be involved—data scientists, project managers, IT folks, and business leaders.
• Take stock of what you already have, like historical sales data or analytics tools. The cheat sheet should help you spot any gaps.
• Identify what data you need. You might need sales history, supplier info, and current stock levels for the inventory project.
• Choose the AI model or technology that fits your problem. The cheat sheet should guide you, whether it's machine learning for forecasting or a recommendation system for stock optimization.
• Make a rough plan of the project phases, from planning to deployment.
• Decide how you’ll track success with KPIs like inventory turnover rate or forecast accuracy.
• Think about any ethical or legal issues, especially if you’re dealing with customer data. The cheat sheet should remind you to keep data privacy and regulations in mind.
• Anticipate possible problems and plan how to tackle them.‍

2. Data Collection and Preparation

• Figure out where you’ll get your data from—internal sources like customer databases or external ones like public datasets.
• Based on your AI model and goals, determine what specific data you need. For a churn prediction model, you need customer interactions, purchase history, and demographics.
• Gather data from your sources, whether it’s querying databases, scraping websites, or using APIs.
• Make sure your data is accurate and complete. Fix any errors or inconsistencies.
• Transform data into a usable format—scale numbers, encode categories, and create new features if needed.
• Divide your data into training, validation, and test sets. Training builds the model, validation helps tweak it, and testing evaluates its performance.
• Ensure data handling complies with legal and ethical standards, susceptible ones.
• Keep track of how data was collected and processed. This helps with reproducibility and troubleshooting.
• After preparing data, review it and make any necessary tweaks. You might need more data or different preprocessing.
• Ensure the data is in the right format and ready for the model.‍

3. Exploratory Data Analysis (EDA)

• Start by understanding your data—types of variables and structure.
• Use charts like histograms and scatter plots to see how data is distributed and spot outliers.
• Identify and address any missing values that could impact your model.
• See how different variables relate to each other with scatter plots or correlation matrices.
• Look for any data points that stand out. They might skew results or indicate issues.
• Summarize data at a higher level, like average sales by region.
• Check for consistency and accuracy, looking out for anomalies or errors.
• Use mean, median, and other stats to get a snapshot of your data.
• Determine which features are most important for your model.
• Keep track of insights and findings from your EDA. This helps guide further analysis or model development.

4. Model Selection

• Determine if you’re dealing with classification, regression, clustering, etc.
• Match the model’s complexity to your problem. Simple models are easier and faster but might not handle complex patterns. Complex models can capture intricate relationships but need more data and power.
• Choose models based on your data. Deep learning works well with lots of data but might overfit with less.
• Look at how different models perform based on metrics relevant to your problem. For classification, check accuracy, precision, recall, etc. For regression, consider MSE or R-squared.
• Know the assumptions models make about your data. For instance, linear regression assumes a linear relationship, while decision trees don’t.
• Consider the computational needs of each model. Complex models need more power and memory.
• Decide how important it is to understand how the model makes decisions. Some models are easier to interpret than others.
• Try out different models to see which one works best.
• Think about how well the model will handle larger or more complex datasets.
• Keep a record of why you chose a particular model, including its fit with the problem, data, and performance metrics.

Machine Learning Algorithms Cheat Sheet

5. Model Training

• Make sure your data is divided into training, validation, and test sets.
• Choose an algorithm suitable for your model, like gradient descent for neural networks or decision trees.
• Adjust settings like learning rate and batch size. These control how the model learns.
• Start the model with appropriate parameters or weights. Some models have defaults; others might need a custom setup.
• Train your model with the training data. Keep an eye on progress to ensure it’s learning properly.
• Check the model’s performance during training with the validation set to tweak settings and avoid overfitting.
• If the model is overfitting or underfitting, adjust your approach as needed.
• Track performance metrics like loss and accuracy during training.
• Consider using early stopping-to-end training if performance on validation data stops improving, which helps prevent overfitting.
• Once training is done, save your model and document the process, including hyperparameters, training time, and performance metrics.

6. Model Evaluation

• Decide on metrics to measure your model’s performance. For classification, think accuracy, precision, recall, etc. For regression, use MSE or R-squared.
• Make sure you have different datasets for training and evaluation. Use the validation set during training and the test set for final evaluation.
• For a more reliable performance estimate, use cross-validation. This involves splitting the data into multiple folds and training/evaluating multiple times.
• The confusion matrix shows detailed performance for classification models, including true positives, false positives, and more.
• Assess if your model is overfitting or underfitting.
• See how the model performs with varying data or conditions to ensure it’s not too sensitive to specific inputs.
• Compare your model’s performance to simple models or heuristics to ensure it adds value.
• Evaluate how understandable your model is, mainly if it's used for decision-making.
• Look at the mistakes your model makes to understand its weaknesses.
• Keep a detailed record of evaluation results and insights to guide future improvements or deployment.

7. Model Deployment

• Double-check that your model’s performance is solid and ready for the real world. Make sure it meets your business needs.
• Decide where the model will live—whether on your own servers, in the cloud, or on edge devices.
• Make sure all the tech stuff is in place—servers, databases, and networks.
• Hook the model up to the app or system that will use it. This could mean setting up APIs or embedding them directly into software.
• Set up monitoring to track how the model’s doing and logging to catch any errors.
• Keep track of different versions of your model so you can update or roll back if needed.
• Ensure your model and its data are secure with proper access controls and encryption.
• Run some tests before going full-scale—try a pilot run or A/B testing to see how it performs in the real world.
• Be ready to scale up if needed. If the model needs to handle more data or traffic, have a plan in place.
• Keep a record of how you deployed the model—configurations, integrations, and monitoring setups. Make sure everyone involved knows the details.

8. Monitoring and Maintenance

• Get tools in place to monitor your model’s performance in real time. This means tracking accuracy, response times, and system load.
• Decide which metrics matter most for your model. This could be how accurate it is, error rates, or how fast it responds.
• Configure alerts to let you know if something goes wrong—like if accuracy drops or there's an error.
• Check-in on how the model performs periodically. Look at the data to spot any trends or problems.
• Keep the model up-to-date by retraining it with new data or tweaking settings as needed.
• Keep an eye out for model drift, where the model’s performance starts to change because of new patterns or data.
• Make sure the data your model uses is still good quality. If the data gets messy, the model's performance can mess up.
• Keep notes on any updates or issues and how you fixed them. This helps track what's been done and makes it easier to troubleshoot in the future.
• Monitor the overall system where your model runs—like server load and response times. Ensure everything’s running smoothly.
• Update your team or stakeholders on how the model’s doing and any changes made. This keeps everyone informed and helps with decision-making.

9. Documentation and Reporting

• Write down what you’re aiming to achieve with your AI project. What’s the end game?
• Keep a record of where your data comes from and how you’ve prepped it—like cleaning, transforming, and feature engineering.
• Track the process of building your model—what algorithms you used, any tweaks, and settings.
• Document your model's performance by noting metrics like accuracy, error rates, etc.
• Create reports that summarize key insights, findings, and results. Include charts, performance stats, and any useful takeaways.
• Log any major decisions and why you made them—like changes in strategy or model tweaks.
• If others will use your model, write easy-to-follow guides on how to use it and interpret results.
• Make sure to update your documentation as things change. Keep everything current so it reflects the latest status of the project.
• Be ready for reviews or audits by having all your documentation in order. This includes everything from data details to model performance.
• Regularly update stakeholders on what's happening in the project. Share progress reports and key findings.

Generative AI vs. Regular AI Project Cheat Sheets

The difference between a Generative AI project cheat sheet and a regular AI project cheat sheet mainly revolves around the specific considerations and techniques relevant to each type of AI.

Even Experienced Tech Companies Need AI Project Cheat Sheets

Even if experienced service technology companies, like DATAFOREST, are pros in the field, cheat sheets for AI projects are still super useful. They are handy reminders or quick reference guides—they keep everyone on the same page. AI projects can be complex, with many moving parts, so having a cheat sheet helps streamline the process and ensures nothing gets overlooked.

Cheat sheets also help in communicating with clients or stakeholders who might not be as tech-savvy. They can simplify complex ideas and ensure everyone understands the project scope and goals. Plus, they're great for onboarding new team members quickly, helping them get up to speed without digging through endless documentation.

Please complete the form, and let's finally test how the cheat sheet works in practice.

FAQ

Why is a cheat sheet required to complete an AI project?

A cheat sheet is essential for completing an AI project because it provides a concise roadmap through the complex process, ensuring that crucial steps and best practices are not overlooked. It simplifies communication and coordination among team members, helping to maintain focus and consistency throughout the project.

Are all cheat sheets for AI projects similar or different?

Cheat sheets for AI projects can differ because they are tailored to the specific AI type, such as generative AI versus traditional predictive models. While they share common elements, such as data preparation and model evaluation, the techniques, metrics, and considerations can vary significantly based on the project's focus.

What is the main difference between regular and Generative AI project cheat sheets?

The main difference between cheat sheets for regular and Generative AI projects is that Gen AI cheat sheets focus on techniques like GANs and VAEs for creating new content, whereas regular AI cheat sheets concentrate on predictive modeling and classification tasks. Generative AI projects emphasize managing data augmentation and model creativity, while regular AI projects focus more on accuracy, reliability, and avoiding overfitting.