Welcome to part 3 of our series on how to outsource data annotation. We’ve covered a lot of ground in the last two articles of this series and have presented several ideas for ensuring the success of your outsourced data annotation project. If you missed the first two articles (you can read them here), some of the critical ideas we discussed included:

Choosing the Right Vendor

  • Understand Your Requirements
  • Evaluate Vendors on Experience
  • Take a Test Drive

Using Project Guidelines Effectively

  • Take Time to Align
  • Practice Makes Perfect
  • Plan for the Unexpected

Choosing the right annotation partner and working closely…


Welcome back to part 2 of our 3 part series on How to Successfully Outsource Data Annotation projects! For those that haven’t read part 1 of the series, I’d recommend reading that article before jumping into part 2. You can read the first article by clicking here.

As a quick recap, in part 1 we discussed the keys to choosing the right data labeling vendor to partner with. We focused on three main points:

  1. Understand Your Requirements
  2. Evaluate Vendors on Experience
  3. Take a Test Drive

While we did briefly touch on the importance of having well-documented project guidelines in the…


Outsourcing data annotation requires strong, productive, and transparent partnerships.

We all know that training data preparation is one of the least enjoyable chores in the machine learning process. While having humans-in-the-loop to execute tasks like labeling unstructured data is often an essential step in preparing training data for your model, its tedious and time-consuming nature makes it a task not ideally suited for small teams of highly skilled & well-paid data scientists or engineers. This is why many organizations choose to outsource their data annotation projects in order to leverage lower-cost labor at scale. …


Students at Mississippi State University are keeping their campus jobs labeling data for a cutting-edge off-road autonomous vehicle machine-learning program, thanks to BasicAI’s specialized and secure data labeling platform with 3D LiDAR segmentation tools and a flexible new pricing model, which eliminated charges for user seats allowing the University to keep student-workers employed from home during the COVID-19 pandemic.

Lalitha Dabbiru, Assistant Research Professor and Sensor Platform Lead at the MSU Center for Advanced Vehicular Systems (CAVS) says, “BasicAI is a kind of savior to us.” She explains that students are paid by CAVS to label image and LIDAR data…


Great news! Complex data collection projects just got a whole lot easier.

BasicAI is excited to announce the release of our proprietary mobile app for data collection projects, BasicAI Tasks. The BasicAI Tasks app — available to approved data collectors on both the Apple App Store & Google Play Store — is a revolutionary mobile application designed to help BasicAI manage your data collection projects with even greater efficiency & effectiveness. …


Use Case: Healthcare Automation
Use Case: Healthcare Automation

The world is connected today in more ways than it ever has been before, as billions of objects are now capable of connecting to the internet or interfacing with devices that are already online. The new “Internet of Everything” generates a deluge of data, which is increasingly directed to the cloud for processing and storage. Meanwhile, Artificial intelligence is increasingly utilized to analyze and derive value from these enormous stores of data. In industries such as healthcare, transportation, industrial manufacturing, and financial services, AI algorithms are now being applied to increasingly difficult tasks, including critical decision-making processes.

What differentiates human…


Robotic Process Automation + Machine Learning = Intelligent Automation
Robotic Process Automation + Machine Learning = Intelligent Automation

Robotic Process Automation has generated a lot of buzz across many different industries. As businesses focus on digital innovation, automation of repetitive tasks to increase efficiency while decreasing human errors is an attractive proposition.

Robots will not tire, will not get bored, and will perform tasks accurately to help their human counterparts improve productivity and free them up to focus on higher-order tasks.

Beyond simple RPA, Intelligent Automation can be achieved by integrating machine learning and artificial intelligence with Robotic Process Automation to achieve automation of repetitive tasks with an additional layer of human-like perception and prediction.

The Difference Between RPA and Artificial Intelligence

By design, RPA…

BasicAI

BasicAI has deep expertise in training data collection and annotation for Artificial Intelligence and Machine Learning applications.

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