We talk about it time and time again the dreaded challenge and secret to preparing high-quality data. How do you quickly label all of your unstructured data seamlessly and efficiently? Well, starting with the right tool can help you accelerate the production of datasets and uplift your annotation process for success in 2023. How do you achieve this? Read on, and we’ll let you in on 3 key components we feel are crucial to building a successful data strategy.
Key Components to a Successful Annotation Strategy
1.Collaboration Enabled Via the Cloud
Challenge: Low accuracy in data annotation is the leading cause of underperformance in AI models, meaning that high quality data is crucial for any deep learning project to succeed. As the saying goes, “garbage in, garbage out.”
Solution: What you need is a platform that provides a full QA methodology in the cloud, where data scientists and engineers can connect directly with annotators and provide real-time feedback on annotation progress and results. At Dataloop, we enable this kind of direct communication which employs a range of embedded quality assurance mechanisms, both manual and automated, to ensure that human errors are spotted down to the pixel level and fixed in real time. With tools such as “data bugs,” and machine validation annotation flagging at their service, data managers gain full transparency and analytics into their workforce performance, ensuring top accuracy for model developers.
2. Productivity and 10X Faster Annotations
Challenge: When it comes to the process of testing your model the biggest challenge is figuring out where your model is falling down. Human annotations are a crucial component of the supervised deep learning process, with the need for human-labeled data only growing as the AI models mature. Naturally, this means that human labor composes a major part of project costs.
Solution: To minimize costs, Dataloop introduces automations into traditional human workflows, allowing data teams to increase productivity by up to 90%. Using features like AI-assisted annotation automations, model integrations, one-click pre-trained classes and built-in trackers, Dataloop augments human intelligence to increasingly automate the data preparation process, to the point where human intervention is only needed for model validation. This allows data teams to reach production faster than ever before, while lowering operational costs to a minimum.
3. DATA PRIVACY & SECURITY IS CRITICAL
Challenge: Data privacy and security are a central concern for any enterprise and data organization; mishandled data could have detrimental consequences for the company and its customers.
Solution: Dataloop takes extensive and complex measures to ensure customers’ data always remains private and protected. The platform combines an advanced Kubernetes-powered infrastructure, dockerized deployment, industry-leading identity management and authentication systems, role permission enforcement and periodic system audits & penetration tests, among other measures. Datasets can be connected directly to customers’ external cloud storage solutions, including AWS S3, Google Cloud Storage, Azure, etc. so that data never leaves or gets copied from its original location. These ensure that customers’ data remains secure and private both inside and outside the organization, allowing cross-functional teams to build and deploy deep learning data pipelines with unlimited scalability and control.
Summed Up
Implementing AI-assisted applications, QA, and quality control applications will help you start 2023 out right. Embedding these applications into the annotation process will help ensure the quality and accuracy of your work is monitored while the work is taking place, and it will also allow you to manage and annotate at scale efficiently.
