6 of the Most Promising Data Annotation Companies to Watch out for in 2022 and Beyond
As the world moves avidly towards smart systems and intelligent machines, the focus on Artificial Intelligence to spruce up things becomes the most appropriate point of discussion. Starting from smaller industries to large-scale firms, projects based on machine learning, NLP, Computer Vision, anomaly detection, and more are being taken up, in an effort to envision structures that would be able to automate repetitive tasks, least of all.
But then, envisaging smart and intelligent machines isn’t only about writing lines of boilerplate code. Instead, it all pans down to datasets and insights that need to be fed into the systems during the formative phase, as a part of training and contextual evolution.
Anyways, data alone isn’t going to cut it for the machine learning algorithms. Companies brainstorming research and project development must analyze data quality and information stance before contemplating AI-powered projects. As self-explanatory as it might be, ensuring data quality of the highest grade requires accurate labeling of the datasets, post-collection.
Despite the importance of labeling or rather annotating data, organizations often find themselves at sea when it comes to managing the accruing bandwidth, associated with voluminous data annotation chores. Plus, annotating data is a skill and software-driven specialization, which is anything but economical when approached with in-house team members.
The Importance of Data Annotation
As a firm working on machine learning projects, you might have set things in motion by collecting data or arranging a collection in the first place. Still, the data by your side needs to be modeled in a certain way to be able to make sense to the intelligent machines. This is where Data Annotation comes into the mix, playing a salient role in developing some of the more perceptive autonomous vehicles, drones, robotics applications, and other vertical-specific resources.
Are you more into specifics? Well, here is something more justifiable for you to look at.
As per research and credible assumptions, the data annotation market, worldwide, is expected to assume a $2.57 billion valuation by the end of 2027.
Therefore, if you are unsure if your company needs to consider outsourcing data annotation as a service, well, let the higher costs of in-house annotation and the importance of labeling data for training multiple iterations to increase predictive accuracy be the torchbearers.
Which Company to Consider for your Data Annotation Project?
Not every data annotation company is adept enough to handle a case-specific annotation workload. As a matter of fact, you should consider providers that aren’t only comfortable with the extent of training veracity but have the manpower and technical acumen to endure concurrent projects and highly specific requirements.
Long-story-short, you can consider on-boarding any of the following 6 companies for getting a data annotation project underway. Also, you can take a closer look at their specializations for making informed decisions.
If you seek training data of the highest quality for designing and developing intelligent and state-of-art ML solutions, Append can be a name to consider. What makes Appen a reliable name in the data annotation industry is its vast range of annotators. Most importantly, Appen boasts one the most seasoned and diverse clientele in the concerned arena, aiding auto players, tech firms dealing in bespoke projects, government agencies, and more.
- Eye-on-Data Accuracy
- Let’s you scale services and setups, both horizontally and vertically
- Globally experience
- Top-drawer quality of annotations, involving images, videos, audios, and texts
- Hive: Best for Small Projects
Trust me, there is hardly a provider better than Hive when end-to-end data annotation is concerned. Despite serving a limited clientele and a set number of use cases, Hive is the name to consider if you plan on ideating something major with a primary focus on accuracy. The costs can be somewhat overwhelming for startups, but the quality of services cannot be questioned.
- Let’s you ride the benefits of a full-stack Artificial Intelligent platform
- Supports explorative product development
- Extensive service range, provided Hive supports your use case in the first place
- Shaip: Best Overall
If you want to rely on a data annotation company with industry-grade, hands-on data annotation experience, you can hardly go wrong with Shaip. With over 15 years of strategic experience in collecting and annotating data using a wide range of techniques, Shaip is arguably the most comprehensive service provider in the picture. Plus, Shaip offers a multi-faceted service set with solutions catering to healthcare setups, Conversational AI models, self-driving cars, and more.
Shaip eliminates the bottlenecks exhibited by the likes of Appen, Hive, and others by offering equal weightage to small and large projects. Plus, they have the vision to handle both avant-garde and conventional training requirements with comparable precision and accuracy.
Finally, if you are looking to launch an AI at the earliest, grabbing the first mover’s advantage, Shaip is the name to consider.
- Focus on data security and privacy
- Competitive pricing ( NLP, computer vision, images, texts, videos, and audio)
- Multi-lingual expertise, with support for 40+languages and counting
- Covers a wide range of use-cases
- Has Google, Microsoft, Amazon, and other reputed names on the client list
Note: Shaip doesn’t only offer data annotation but even excels if you want the data to be collected, transcribed, cleaned, or even cataloged as per highly specific labels. Plus, Shaip offers the best annotation prices in the industry without compromising on the quality, accuracy, and delivery of the projects.
- TELUS International: Best for Inventive Setups
Nothing feels better than connecting with a service provider capable of annotating data for avant-garde digital setups. This is where TELUS International takes the cake by letting enterprises work on disruptive and global AI solutions, without worrying about data quality and training. At TELUS, no use-case is far-fetched as the company is capable of working on millions of data points, regardless of the type of annotation the prototype seeks.
- Ability to annotate geo-datasets along with images, texts, videos, and audio snippets
- A more diversified AI platform with a focus on data quality
- Ability to label and segregate data in the most granular way
- Mindy Support: Best for Standard Data Tagging
If your data annotation requirements rival that of the biggest AI-players in the technical space, like Microsoft and Apple, you might just be in luck with Mindy Support by your side. Backed by indubitable credibility and the ability to help scale your ideas beyond the lines of possibility, Mindy Support is a force to reckon with in the data annotation realm.
- 7+ years of relevant experience
- One of the best service providers for building accurate setups
- One of the largest and hottest customer portfolios
- Cloudfactory: Best for Minimally Insightful Models
For each of your several accelerated data labeling needs, there is a Cloudfactory service plan to revel in. The best thing about this service provider is its ability to simplify data annotation for the companies that would be using the insights in the first place. Plus, there is a team of data analysts working alongside the human annotators to minimize goof-ups and accuracy concerns.
- Flexible pricing plans
- Offers a complete tech stack that scales beyond annotation
- Support for conversational AI, computer vision, anomaly detection, and other possibilities
Comprehensive annotation isn’t as fleeting as we make it to be. With any of the 6 featured data annotation companies by your side, depending on the specifics of your purported product, it becomes possible to access top-of-the-line data labeling features for creating enterprise-grade AI solutions, with elevated accuracy and predictive precision.
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