AI and machine learning are powerful tools for solving complex problems and creating innovative solutions. However, they also require careful planning, testing, and evaluation to ensure that the results are valid, reliable, and useful. In this article, you will learn how to conduct effective AI and machine learning experiments by following six steps.
Before you start any experiment, you need to have a clear and specific goal that you want to achieve. This will help you to design your experiment, choose your metrics, and interpret your results. Your goal should be aligned with your business objectives, your research questions, or your user needs. For example, your goal could be to improve the accuracy of a sentiment analysis model, to reduce the cost of a recommendation system, or to increase the engagement of a chatbot.
It can be helpful to collaborate with other researchers and practitioners in order to share ideas and learn from each other. This helps in trimming down the scope of the ML models to test. One needs to make sure, their goal is realistic, and the focus is on real-world problems.
To define goals, it will first be necessary to collaborate with experts in the respective field to recognize the depth of the problems.
The quality and quantity of your data are crucial for the success of your AI and machine learning experiments. You need to select the data that is relevant, representative, and reliable for your goal. You also need to ensure that your data is clean, consistent, and well-labeled. Depending on your goal, you may need to split your data into different subsets for training, validation, and testing. You may also need to augment, balance, or anonymize your data to avoid bias, overfitting, or privacy issues.
In the real world, data is rarely clean, consistent and well-labeled and it is impossible to fix these issues fully. I would not call the section "choose your data" but "know your data". Most of the time, you cannot choose your data anyhow.
La elección de los datos adecuados es esencial para lograr el resultado esperado. Debemos contar con un alto volumen de datos y con datos representativos. Utilizar el histórico de datos, generarlos/ completarlos mediante datos sintéticos y utilizar datasets públicos son buenas técnicas que pueden utilizarse de forma aislada o complementarse. De cualquier manera, hay que trabajar con estos datasets para evitar sesgos que distorsionen el modelo y hay que dividir el dataset para tener datos para entrenar y otros para validar que permitirán conseguir modelos más precisos.
Based on the objectives, the representative data must be cleaned, made consistent, and structured first, which are important for problem-solving.
The next step is to choose the methods that you will use to implement your AI and machine learning experiments. This includes the algorithms, frameworks, libraries, and tools that you will use to build, train, and evaluate your models. You need to consider the advantages and disadvantages of different methods, as well as their suitability and compatibility for your goal and data. For example, you may need to compare different neural network architectures, optimize different hyperparameters, or use different evaluation metrics.
In addition, finding a suitable method is a critical step, it is an iterative and sometimes time-consuming process. Other factors to consider when selecting the right approach: 1. Not every problem is solved by fitting a neural network on input data, it's also wise to consider non-neural ML algorithms (depending on data like size, or features) 2. Consider data-centric approaches beyond model-centric ones 3. The choice of method should align with the resources at one's disposal e.g., to fine-tune LLM to use openai's API or open-sourced model at hugginface? Or use a tool like Cleanlab to clean data automatically or use domain experts/labelers to clean it?
Prior to these tasks you should define a baseline (previous method/current state of the art/human level performance/...) which serves as a reference against which you compare your experiments results.
It would be important to conduct a method benchmark based on the provided method results. Depending on the objective, a preference for discriminative or generative methods may exist.
Once you have your data and methods ready, you can run your AI and machine learning experiments. You need to follow a systematic and reproducible process that allows you to track and document your steps, inputs, outputs, and results. You also need to ensure that your experiments are fair, ethical, and compliant with the relevant standards and regulations. You may need to use tools such as TensorBoard , MLflow , or Weights & Biases to monitor and manage your experiments.
After you run your experiments, you need to analyze your results and draw meaningful insights from them. You need to use appropriate methods and tools to visualize, summarize, and interpret your results. You also need to compare your results with your baseline, your expectations, and your benchmarks. You need to identify the strengths and weaknesses of your models, as well as the sources of errors, uncertainties, and biases. You need to use your results to answer your research questions, validate your hypotheses, or support your decisions.
The final step is to communicate your findings and recommendations to your stakeholders, peers, or audience. You need to use clear, concise, and compelling language and visuals to present your goal, data, methods, results, and analysis. You also need to explain the implications, limitations, and applications of your findings. You need to tailor your communication to the level of understanding, interest, and feedback of your audience. You need to use your communication to demonstrate your value, credibility, and impact.
Although this article focuses on ML experiments, you should also begin with deployment in mind just as a good practice. Is your model going to be deployed on someone's Linux box under their desk for a small, internal service? Or is it going to be deployed on millions of edge devices globally? Deployment should shape and inform development.