AI (artificial intelligence) and training data are inseparable. They are like night and day, heads and tails, and yin and yang. One cannot exist without the other. Because they have a cause-and-effect relationship, your job as a business operator is to provide as much high-quality training data for your AI modules so they can return accurate information.
There is no such thing as enough data. Reinforcement learning only improves with more datasets. Specifically, if you intend to launch a unique solution to your market, you need to ensure your product and its output live up to expectations. To produce profitable models, you need a perennial source of AI training data.
If you’ve been following our blog, you know that we’ve discussed free, in-house, and other data sources. In this post, we decided to narrow our focus to one aspect and discuss how end-to-end training data service providers can offer you immense benefits in data collection and annotation.
When you want your machine learning modules to process data and learn autonomously, end-to-end vendors are your ideal choices.
Why?
Let’s explore in detail.
Who are End to End Training Data Service Providers?
End-to-end training data vendors are your one-stop solution providers who consistently offer optimized datasets based on your requirements. Regardless of your market niche, demographics, product type, or other factors, they take responsibility for collecting the appropriate datasets for your modules. End-to-end data vendors then annotate the data making it machine-ready, ensuring the datasets are of the highest quality for your systems and deliver precise results.
A premium end-to-end vendor takes full charge of all the processes involved in sourcing and providing AI training data.
How do they operate and what’s their Process?
Data collection and delivery is a complex process that demands countless hours of intricate manual labor. Dedicated teams work in tandem to ensure collection, labeling, quality assurance, and data delivery happens one time without compromising value. Their sole aim is to keep your machine learning modules busy with autonomous learning until the desired results are achieved.
We’ve divided end-to-end vendor responsibilities into three categories, they include:
Data Collection
The first step is identifying the type of data you need. Datasets are dependent on your product, the intended results, the type of datasets you need, and other essential factors. Based on these, your training data service provider could retrieve your data in the form of images, audio, video, text, and/or a combination of these.
Data Labeling
Data generated or procured at this stage is usually raw. Meaning, datasets contain tons of irrelevant information, misinformation, poorly formatted details, and more. They are also devoid of the format in which AI systems can understand their contents. Service providers work on cleaning and then manually annotating the data to be used in your ML models.
Data De-identification
Due to privacy and data interoperability concerns, there are several standards, protocols, and compliances that businesses have to follow. Standards like HIPAA and GDPR guidelines dictate strict conditions with respect to data confidentiality, and failure to adhere to these could be detrimental to businesses.
Training data providers work on processes like data de-identification, where they de-associate the contents of data making it as objective and vague as possible. This is where keeping the dataset functional for machine learning is beneficial. Adding an additional layer of work for data providers ensures you have the safest quality data in hand for your project.
End to End Data Service Providers Vs. Multiple Data Vendors
When operating a business, you will need to decide if you need a single end-to-end data provider or allocate to multiple vendors. While the latter may seem more plausible and profitable in your budgeting requirements, only a comprehensive analysis can lead you to the most beneficial solution.
| Multiple Vendors | End To End Data Providers |
| Too many vendors will work on delivering one single type of dataset for your project. | Only one dedicated team works on acquiring, annotating, and delivering your required datasets. |
| There are inconsistencies among the final datasets. Meaning, you will have to rework on compiling data to your in-house standards and then feed it to your systems. | Your datasets are neatly compiled and delivered to you in batches as required. You could directly feed it into your systems to initiate processes. |
| Higher chances of data bias as multiple hands are working on datasets. | Bias is removed or conditions are specified to avoid them during processing. |
| Data repetition seeps in as every vendor doesn’t know from what source the other vendors are acquiring data. | Datasets are new and fresh as they have reports of how data was generated and acquired. |
| You will have to issue guidelines and requirements individually to different vendors and maintain distinct rapport and workflows. | The final quality is impeccable and you have a rewarding collaborative experience. |
The real benefits of End to End Training Data Providers nobody tells you about
Now that we have a basic understanding of end-to-end providers and how they differentiate from other sources, let’s go over the benefits they offer:
- One of the ways end-to-end training data providers stand out is that they don’t crowdsource data to multiple vendors. Instead, they have dedicated teams and workforces to source data from specific sources manually. This means no geography or demographics is challenging as they have regional associates who work on curating and compiling data.
- Feedback and changes are easier to incorporate into the process as you consistently deliver datasets in batches. Any feedback you have would be paid attention to in subsequent batches of delivery.
- All datasets are licensed and devoid of legal obligations.
- Domain experts and specialists guide data annotation and labeling. For instance, healthcare data is annotated by veterans in the industry for accurate processing and results.
- The collaboration is as transparent as it gets with consistent reports, updates, insights into data collection sources, and more.
- End-to-end data service providers can fetch your data regardless of the niche or complexities involved because of their vast networks around the world.
Collaborating with Shaip adds additional value to your project apart from the advantages regarding end-to-end service providers. Being a premier data annotation provider for years, we have managed to build and maintain three priceless assets in our portfolio:
- People – we have over 700 contributors and collaborators in our team to get you the most precise and relevant datasets for your projects. We also have the best project managers, SMEs, and product developers in our arsenal.
- Process – mastering efficiency is an art form. Our years of experience in the industry have allowed us to deliver massive quantities of quality data to our clients seamlessly. Rigorous quality checks, 6 Stigma Gate processes, and more ensure impeccable data quality.
- Platform – our in-house data annotation tool is the best in the industry ensuring swift TAT and high quality.
Wrapping Up
As a business owner, you need to take unnecessary burdens and responsibilities off your shoulders to scale your company. You will significantly benefit from leaving data collection up to the experts at Shaip. Work on optimizing your product while we optimize its capabilities through our AI training data.
Make the practical decision, reach out to us today.
