Unstructured data, such as text, images, audio, and video, can provide valuable insights for machine learning (ML) projects, but they also pose challenges for collaboration. How can you and your team work effectively on unstructured data in ML? Here are some tips to help you.
Before you dive into the data, you need to have a clear vision of what you want to achieve and how you will measure your success. Define your goals, scope, and deliverables with your team and stakeholders, and document them in a shared platform. This will help you align your expectations, prioritize your tasks, and avoid scope creep.
Depending on the type and size of your unstructured data, you may need different tools and methods to store, process, analyze, and visualize it. For example, you may use cloud storage, databases, frameworks, libraries, APIs, or software applications to handle your data. Choose the tools and methods that best suit your needs, budget, and skills, and make sure your team is familiar with them. Also, establish a common workflow and coding style for your team, and use version control and documentation to track your changes and progress.
Collaborative EDA tools comes very handy, especially when each team member is somehow orthogonal to rest in terms of knowledge for the use case. For instance, for intuitive and useful interactive visualization, I enjoy Microsoft's sanddance framework.
Establish clear standards for data formatting, labeling, and documentation. Create a data dictionary or schema to explain the structure and meaning of different data elements. Consistent data formatting aids in seamless collaboration.
Unstructured data often requires more cleaning and preprocessing than structured data, such as removing noise, duplicates, outliers, missing values, or irrelevant information. You may also need to transform, normalize, or standardize your data to make it suitable for ML algorithms. To collaborate effectively on this stage, you should define and follow a consistent data quality criteria and process, and use tools that automate or simplify your tasks. For example, you may use natural language processing (NLP) or computer vision techniques to extract features from text or images, or use data augmentation to increase your data diversity and volume.
If there is one thing being underrated in EDA preprocessing, it is the discretization of continous features. Its benefits may be multifaceted : feasible interpretation, tractable combination, new groupings, N/A value assessment etc.
Exploring and visualizing your unstructured data can help you gain insights, identify patterns, test hypotheses, and communicate your findings. You can use descriptive statistics, charts, graphs, plots, or dashboards to summarize and display your data. To collaborate effectively on this stage, you should share your exploratory and visual analysis with your team and stakeholders, and solicit feedback and suggestions. You should also use tools that enable interactive and collaborative data exploration and visualization, such as Jupyter notebooks, Google Colab, or Tableau.
Building and evaluating your models is the core of your ML project, where you apply different algorithms, techniques, and parameters to train and test your models on your unstructured data. To collaborate effectively on this stage, you should define and follow a common performance metric and evaluation method for your models, and compare and contrast your results. You should also use tools that facilitate and automate your model building and evaluation process, such as scikit-learn, TensorFlow, Keras, or PyTorch.
Deploying and monitoring your models is the final stage of your ML project, where you put your models into production and check their performance and accuracy on new data. To collaborate effectively on this stage, you should ensure that your models are scalable, reliable, and secure, and that they meet the requirements and expectations of your users and stakeholders. You should also use tools that enable and simplify your model deployment and monitoring, such as AWS, Azure, Google Cloud, or Flask.
Artigo genérico demais. Basicamente descreveu uma rotina comum em ciência de dados e implementa??o de modelos, mas direcionando o interlocutor para o tema do título. Quando um leitor busca tal tema, geralmente quer encontrar técnicas precisas e exemplos práticos.