Best Practice Guide for Computer Vision Data Operations Teams
In most cases, successful outcomes from training computer vision models, and producing the results that project leaders want, comes down to a lot of problem-solving, trial-and-error, and the unsung heroes of the profession, data operations teams.
Data operations play an integral role in creating computer vision artificial intelligence (AI) models that are used to analyze and interpret image or video-based datasets. And the work of data ops teams is very distinct from that of machine learning operations (MLOps).
Without high-quality data, ML models won’t generate results, and it’s data operations and annotation teams that ensure the right data is being fed into CV models and the process for doing so runs smoothly and efficiently.
In this article, we review the importance of data operations in computer vision projects; the role data ops teams play, and 10 best practice guidelines and principles for effective data operations teams.
What’s the Role of Data Operations in Computer Vision Projects?
Data operations for computer vision projects oversee and are responsible for a wide range of roles and responsibilities. Every team is configured differently, of course, and some of these tasks could be outsourced with an in-house team member to manage them.
However, generally speaking, we can sum up the work of data operations teams in several ways:
Dataset sourcing. Depending on the project and sector, these could be free, open-source datasets or proprietary data that is purchased or sourced specifically for the organization.
Data cleaning tasks. Although this might be done by a sub-team or an outsourced provider for the data ops team, data ops are ultimately responsible for ensuring the datasets are “clean” for computer vision models. Clean visual data must be available before annotation and labeling work can start. Data cleaning involves removing corrupted or duplicate images and fixing numerous problems with video datasets, such as corrupted files, duplicate frames, ghost frames, variable frame rates, and other sometimes unknown and unexpected problems.
Implementing and overseeing the annotation and labeling of large-scale image or video datasets. For this task, most organizations either have an in-house team or outsource data labeling for creating machine learning models. It often involves large amounts of data, so is time-consuming and labor-intensive. As a result, making this as cost-effective as possible is essential, and this is usually achieved through automation, using AI-powered tools, or strategies such as semi-supervised or self-supervised learning.
Once the basic frameworks of a data pipeline are established (sourcing the data, data cleaning, annotation, data label ontologies, and labeling), a data operations team manages this pipeline. Ensuring the right quality control (QC), quality assurance (QA), and compliance processes are in place is vital to maintaining the highest data quality levels and optimizing the experimentation and training stages of building a CV model.
During the training stage, maintaining a high-quality, clean, and efficient data pipeline is essential. Data ops teams also need to ensure the right tools are being used (e.g., open-source annotation software, or proprietary platforms, ideally with API access), and that storage solutions are scalable to handle the volume of data the project requires.
Data operations teams also check models for bias, bugs, and errors, and see which perform in line with or above expectations, use data approximation or augmentation where needed, and help prepare a model to go from the training stage into production mode.
Does Our Computer Vision Project Need a Data Operations Team?
In most cases, commercial computer vision projects need and would benefit from creating a data operations team.
Any project that’s going to handle large volumes of data, and will involve an extensive amount of cleaning, annotation, and testing, would benefit from a data ops team handling everything ML engineers and data scientists can’t manage. Remember, data scientists and ML engineers are specialists.
Project managers don’t want highly-trained specialists invoicing their time (or requesting overtime) because you’ve not got the resources to take care of everything that should be done before data scientists and ML engineers get involved.
High-performance computer vision (and other AI or ML-based) models that data science teams are training and putting into production are only as effective as the quality and quantity of the data, labels, and annotations that it’s being given. Without a team to manage, clean, annotate, automate, and perfect it, the data will be of poorer quality, impacting a model’s performance and outputs.
Managing the pipeline and processes to ensure new training data can be sourced and fed into the model is essential to the smooth running of a computer vision project, and for that, you need a data operations team.
How Data Operations Improve & Accelerate CV Model Development and Training
Data operations play a mission-critical role in model development because they manage labor-intensive, manual, and semi-automatic tasks between the ML/data science and the annotation teams.
DataOps perform a cross-functional bridge, handling everything that makes a project run smoothly, including managing data sourcing (either open-source or proprietary image and video datasets, as required for the CV project), cleaning, annotation, and labeling.
Otherwise, data admin and operations would fall on the shoulders of the machine learning team. In turn, that would reduce the efficiency and bandwidth of that team because they’d be too busy with data admin, cleaning, annotations, and operational tasks.
10 Data Operations Principles & Best Practices for Computer Vision
Here are 10 practical data operations principles and best practice guidelines for computer vision.
Build Data Workflows & Active Learning Pipelines before a Project Starts
Implementing effective data workflow processes before a project starts, not during, is mission-critical.
Otherwise, you risk having a data pipeline that falls apart as soon as data starts flowing through it. Have clear processes in place. Leverage the right tools. Ensure you’ve got the team(s), assets, budget, senior leadership support, and resources ready to handle the project starting.
DataOps, Workflow, Labeling, and Annotation Tools: Buy Don’t Build
When it comes to data operations and annotation tools, the cost of developing an in-house solution compared to buying is massive. It can also take anywhere from 6 to 12 months or more, and this would have to be factored in before a project could start.
It’s several orders of magnitude more expensive to build data ops and annotation tools, especially when there are so many powerful and effective options on the market. Some of those are open-source; however, many don’t do everything that commercial data ops teams require.
Commercial tools are massively more cost-effective, scalable, and flexible than building your own in-house software while delivering what commercial data ops teams need better than open-source options.
It’s also worth noting that several are specifically tailored to achieve the needs of certain use cases, such as collaborative annotating tooling for clinical data ops teams and radiologists.
Having a computer vision platform that’s HIPAA and SOC 2 compliant is a distinct advantage, especially when you’re handling sensitive data. We go into more detail about selecting the right tool, software, or platform for the project further down this article.
Implement DataOps Using Software Development Lifecycle Strategies
One of the most effective ways to build a successful and highly-functional data operation is to use software development lifecycle strategies, such as:
- Continuous integration and delivery (CI/CD);
- Version control (e.g., using Git to track changes);
- Code reviews;
- Unit testing;
- Artifacts management;
- Release automation.
Plus, any other software development strategies and approaches that make sense for the project, the software/tools you’re using, and datasets. For data ops teams, using software development principles is a smart strategic and operational move, especially since data engineers, scientists, and analysts are used to code-intensive tasks.
Automate and Orchestrate Data Flows
The more a data ops team can do to automate and orchestrate data flows, annotation, and quality assurance workflows, the more effectively a computer vision project can be managed.
One of the best ways to achieve this is to automate deployments with a Continuous integration and delivery (CI/CD) pipeline. Numerous tools can help you do this while reducing the amount of manual data wrangling required.
Continuous Testing of Data Quality & Labels
Testing the accuracy and quality of image or video-based labels and annotations is essential throughout computer vision projects. Having a quality control/quality assurance workflow will ensure that projects run more smoothly and label outputs meet the project's quality metrics.
Data operations teams can put systems and processes in place, such as active learning pipelines and debugging tools, to continually assess the quality of the labels and annotations an annotation team creates.
Ensure Transparent Observability
As part of the quality control and assurance process, having transparent metrics and workflows is important for everyone involved in the project. This way, leaders can oversee everything they need, and other data stakeholders can observe and provide input as required.
One of the best ways to do that is with a powerful dashboard, giving data ops leaders the tools they need to implement an effective quality control process and active learning workflows.
Deliver Value through Data Label Semantics
For DataOps to drive value quickly, and to ensure that annotation teams (especially when they’re outsourced), it helps everyone involved to build a common and shared data, metadata, and c. In other words, make sure everyone is on the same page when it comes to the labels and annotations being applied to the datasets.
Providing this is done early into a computer vision project, you can even pre-label images and videos so that when batches of the datasets are assigned to annotation teams, they’re clearer on the direction they need to take.
Create Collaboration Between Data Stakeholders
Another valuable principle is to establish collaboration between cross-functional data stakeholders.
Similar to the agile principle in software development, when data and workflows are embedded throughout, it removes bottlenecks and ensures that everyone works together to solve problems more effectively.
This way, data operations can ensure the computer vision project is aligned with overall operational and business objectives while ensuring every team involved works well together.
Data quality summary in Encord
Treat Data as an Intellectual Property (IP) Asset
Data ops, machine learning, and computer vision teams need to treat datasets as an integral part of your organizations and project's intellectual property (IP). Rather than treating it as an afterthought or simply material that gets fed into an AI model.
The datasets you use, and annotations and labels applied to the images and videos, make them unique; integral to the success of your project. Take every step to protect this IP, safeguarding it from data theft and ensuring data integrity and compliance is maintained throughout.
Have a clear data audit trail so that you know who’s worked on every image or video, with timestamps and metadata. An audit trail also makes data regulation and compliance easier to achieve, especially in healthcare, if you’re aiming to achieve FDA compliance.
Pick the Most Powerful, Feature-rich, and Functional Labeling & Annotation Tools
Picking the most powerful labeling and annotation tools is integral to the success of data ops teams and, therefore, the whole project. There are open-source tools, low-code/no-code solutions, and powerful commercial platforms.
In some cases, the tool you use depends on the use case. However, in most cases, the best tools are use case agnostic and accelerate the success of projects with extensive and powerful automation features.
Encord and Encord Active are two such solutions. Encord improves the efficiency of labeling data and managing a team of annotators. Encord Active is an open-source active learning framework for computer vision: a test suite for your labels, data, and models.
Having the right tools is a big asset for data operations teams. It’s the best way to ensure everything runs more smoothly and the right results are achieved within the timescale that project leaders and senior stakeholders require.
Conclusion: Advantages of an Effective Data Operations Team
A data operations team that’s performing well is organized, operationally efficient, and focused on producing high-quality and accurate image or video-based datasets, labels, and annotations. Beyond overseeing the annotation workflows, quality control, assurance, and data integrity and compliance are usually within the remit of a data ops team.
To achieve the best results, data ops teams need to ensure those doing the annotation work have the right tools. Software that comes with a range of tools for applying labels and annotations, a collaborative dashboard to oversee the work, and an efficient data audit, security, and compliance framework are essential too.
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