Predominant methodologies in developing artificial intelligence (AI) and machine learning (ML) models advocate the use of vast amounts of data. It often becomes challenging to roll back for fixes in algorithms after the implementation, which points to the significance of integrating quality datasets at the outset. Whether feeding off-the-shelf pre-built data or collecting on our own, an initially established error-and-bias-free dataset can help build better performing models. Be that as it may, we are not here to provoke deeper thinking about AI or reignite the endless debate on algorithms vs. data. We are data-driven. Experiencing the value of quality data firsthand made us almost impatient of sharing how to build better data to give your models a significant advantage in the long run. In the following, we’ll discuss:
- The art of collecting data
- Building a robust data pipeline
- Annotation and QA
- Data management and versioning
- Error finding and curations
- Building and versioning models
- Deployment and monitoring
- Key takeaways
The art of collecting data
As a primary step in developing a model, data collection requires a nuanced and all-around perspective. What is the data that we need? A question as simple as that opens up room for further planning on how to gather training data, how to warrant its quality, or make sure it is what the model will “see” throughout the deployment. Depending on the application, collecting data manually can take a significant cut of time and resources. The price of a single satellite image, for example, is in the range of hundreds to thousands of US dollars, and the exact impact it has on our model can only be known after training it on the new datapoint. However, to the best of our knowledge, the influence of new images can still be efficiently estimated. A few alternatives to data acquisition include using public datasets (COCO dataset, the Cityscapes, BDD100K, Pascal VOC, etc.) and synthetic datasets.
While collecting data consumes time and resources, it is vital to take precautions to analyze the dataset and ensure it corresponds with project requirements. Otherwise, issues such as dataset imbalance may prevail. A model might have difficulties detecting the road signs if the data introduced contains only daytime images, for instance. Now, how will that affect your output? Flawed predictions, in all likelihood.
Building a robust data pipeline
Data collection is usually followed by the next staple: An effective data pipeline glues the puzzle together, establishing a viable system to navigate through the data path, from raw inputs to predicted deliverables. This often allows typing, reviewing, and polishing your data much more efficiently to assure you can easily refer back and apply changes to the dataset or dataset version as per need. Instead of standardizing on a single data format, also consider building up and bolstering a format-agnostic pipeline that can generate data from a wide range of formats, including image, text, and video. Besides, it is paramount to estimate ahead of time the volume of your data to start developing a process for saving images and accessing them later on.
Spilling the beans just a tiny bit, one way to automate uploading massive volumes of data is through SuperAnnotate’s SDK function. Our users prefer SDK integration as it minimizes manual processes, reduces workflow complexity, and helps complete tasks way faster than existing alternatives. Let us know whether you agree once you check it out.
Annotation and QA (Quality Assurance)
The quality of raw data directly impacts your AI performance. This comes to the fore especially during training, where your goal is to end up with a model that is unbiased. Confidence in raw data and a rigorous pipeline will lead you to the next stage of building premium-quality data: annotation and QA, which requires special attention to detail. After all, you don’t want to annotate facial features with bounding boxes or a text body by semantic segmentation, correct?
If you want to develop robust and reusable training data, be cautious of the annotation techniques used. Make use of QAs for additional rounds of examination, but first, you have to be sure people in the loop understand the project’s end goal to generate appropriate instructions and annotations. Don’t forget to leave your email below to download more insights on how to build SuperData in a single checklist.
Data management and versioning
Leaning on the latest AI capabilities allows you to not only exclude data from your training set if not relevant but also to generate versions of data for prospective use. This way, you will build models on the most reliable datasets only while cutting down on the building process. In case anything crashes, you will always be able to smuggle back for changes in training data. You can also version different random splits of the same dataset in order to study the bias induced by partitioning training and test data. This is also known as cross-validation. By and large, data versioning helps keep track of different sets of data, compare model accuracies and analyze the pain points and emerging tradeoffs.
Keep in mind that depending on your use case, it may make sense for you to combine several datasets. This practice will serve best, especially if every dataset is too small for high-accuracy predictions or if it doesn’t provide you with balanced data.
Error finding and curation
One of the most common problems with data is that its quality is often uneven. In some places, it might be inaccurately labeled or low-resolution. If you're going to train AI, you need an easy way to see how the data you're feeding into the model is free from such impurities. A curation system can be a huge help in detecting these issues and performing comparative dataset analysis. Moreover, training data can be enormous, and it becomes even more gigantic if you're planning to test various versions of your model. With SuperAnnotate's curation system, you get a clear idea of your data quality through wider filtering options, data visualization, and team collaboration opportunities.
Building and versioning models
Implemented in a timely manner, the model versioning can be a boon to AI, cranking out necessary variations and updates and reducing pitfalls in the final output. When you are ready to roll out your work, though, testing and revision might limit the pace of development. Hence, it becomes useful to know why some model A is better than model B and where data stands within that comparison.
Be mindful of the fact that versioning should be implemented early on in the process. If you track changes to your models and document why you made them, it's easier to maintain consistency in delivery over time, even if it’s limited to a certain extent. By revising your model frequently, you can make sure you're using the best models you could develop, which in turn will guarantee maximum accuracy in predictions.
Deployment and monitoring
Rule of thumb: It’s not enough to deploy your model in production. You also have to keep an eye on how it’s performing. If the model doesn’t deliver the results as expected, there should be a way to revert quickly. Here is where knowing the influence of the data on the model becomes crucial, and curation plays a fundamental role in spotting data-related issues. Follow-up maintenance is essential to ensure the model is working as planned, that there are no run-time issues, and even if there are, fix them before they become significant. Creating triggers to notify you when there are changes in metrics can save a lot of back-and-forth revisions. Nonetheless, we can't always predict the consequences of deployment. Extra caution and preparation are necessary to manage any possible side effects. Deploying a failure detection algorithm together with partial human supervision is a very good idea; the supervision level can be reduced over time. On-time monitoring and fixation would have reduced the probability of unfortunate upshots.
Key takeaways
The process of building SuperData entails more questions than answers: progress here is being made by asking better questions that redefine and spruce up the data quality. A vast amount of similar questions will be drawn from the sections of the checklist above, which we recommend downloading and embedding into your agenda. We at SuperAnnotate will gladly offer a helping hand at any stage of your pipeline.
