Working with large datasets can be challenging. You need to store the data, maintain its quality, and ensure that it's accessible to all of your team members. One way to solve this is by using an image dataset management platform. They are great for storing large batches of images, but they also offer a lot more. Here are some things you should consider before choosing one.
Key Features an Image Dataset Management Platform Should Have
You should start off answering this question: What's the best way to store and maintain your image datasets?
There are various features that one would need in order to properly store and maintain their datasets.
- Efficient storage of raw images
- Simple filtering and visualization of assets
- Integrated labeling tools
- Data analysis for health-check control of your training sets
- Versioning and freezing of datasets
- Permission and user roles to insure security
However, there are other elements to look for in a data management platform, that lead to high-level benefits and you must consider for your final choice. Below, we'll cover 4 of these key elements.
4 Key Elements to Look For in an Image Dataset Management Platform
At a macro level, there's a few things you need to bear in your diligence to make sure you purchase the dataset management platform that best fits your business needs.
1. Cost and pricing model. The management of image datasets can be expensive. A good dataset management platform can cost you from 1k$ up to 100k$ depending on their pricing models.
2. Onboarding process. Does the platform facilitates a seamless onboarding process for new team members? A robust onboarding process will ensure that all of your team is up-to-date on how the platform works when they're added. In addition, purchasing a platform that versions your work, will avoid valuable data loss when someone leaves your company, ultimately easing your onboarding process.
3. Collaboration. Make sure collaboration is available at all stages. If you're working in a team or need to collaborate with other teams, you'll want a solution that offers strong collaboration tools, and not just simple file-sharing services.
4. Performance and scalability. You'll have to ask yourself a couple of questions. How long does it take for your images to upload on average? Can they be uploaded in batches? Speed will impact how quickly your team can perform tasks on the platform.
What's the Difference Between Per Image or Per Gigabyte Pricing?
Data management platforms can be pricey, but depending on your business needs, it might be worth investing in one.
Now, you may be asking yourself, what's the best pricing model for my business?
If you want to store a lot of data for a single project and don't know the amount of images that will be involved, then per gigabyte pricing would be best. Data management platforms that use this pricing model typically allow you to purchase as much storage space as you need and the price depends on the size of your dataset.
However, if you know that your audience will consist of only a few images, then per image pricing may be more appropriate. Data management platforms with this pricing system charge based on the amount of resources used per image.
Onboarding on The Platform — Is it Simple ?
If you're importing a lot of images and annotations, you'll want to make sure the process is seamless. You don't want to lose any information or have your team members recreate data that has already been collected.
If you're going to be importing from existing storage like AWS S3 or GCP, look for a provider that offers an API or Python SDK. This will help the process go faster and with fewer errors.
Another element you'll want to consider is how easy it is to migrate your data onto the platform. If you're starting fresh and building your dataset from scratch, this probably won't matter as much as if you already have a lot of datasets, but it's still worth checking out.
Apart from raw data import, you should also definitely look into the annotation import process. Will you be forced to format your labels a different way than usual or will you be able to just push your raw annotations? This would definitely save you some time!
Last (but not least!) point to consider about onboarding on the platform is your time-to-efficiency for new collaborators. In other words, the time you will have to dedicate to your new organization's onboarding process on the data management platform. The simplest way to assess that is to check the quality of the documentation, walk-through, and tutorials available online.
Collaboration - One Platform for Your Whole Team
It's likely that a lot of people in your organization are involved in at the data level, so you should want to get a platform that can be managed and used by multi-disciplinary teams. This implies having multiple levels of features dedicated to different skill levels in data science.
Additionally, you should contemplate collaboration between ML engineers and fields experts, to build great quality datasets. This is especially important in computer vision projects, when working with highly industrial use-cases that require expertise in the annotation process.
Your dataset management platform needs to be collaborative to bring one single source of truth to your organization. You should have a collaborative labeling tool with proper permissions and chat features enabled. These tools enable tracking, tagging, sharing, and viewing datasets across multiple departments or even an entire company-wide ecosystem. They also provide powerful search functionality so users can find data easily by keywords or metadata value.
At Picsellia, we offer a complete MLOps platform where your teams can work on the same datasets and collaborate in your annotations and experiments by comments, feedback and notifications.
Performance and Scalability of The Platform
Depending on the size of your business, you might need a platform that's designed for millions of images. If you do, make sure it's architected to handle the demands of your company, and that it's able to scale up with you.
You should also consider if the platform is bug-free. When working with large datasets, bugs can become an issue. The last thing you want is to experience performance issues or bugs while using your dataset management platform.
Key Insights
Now you should have a good overview of the key elements that you need to consider about a data management platform. Most of the platforms share the same features, but the key differences are more about the philosophy behind it.
Starting with a huge, robust platform from the beginning might be a wise choice. However, these platforms tend to be rigid, so adding an extra feature that was not originally included in the platform might be difficult, if not impossible.
In conclusion, there is no one single answer to what dataset management platform works the best, since every organization has its own, specific needs. So you really need to identify your own needs and ask the right questions to find the best fit for your ML dataset management activities.
