The Ultimate Guide to Data Labeling Outsourcing in 2023

Wondering if you should outsource your data labeling for machine learning in 2023? Looking for expert insights on choosing a data labeling partner? Eager to learn insider tips on ensuring high quality results?

You‘ve come to the right place!

In this comprehensive guide, I‘ll walk you through everything you need to know about outsourcing your data labeling work in 2023 – from weighing the pros and cons, to finding the right provider, to optimizing the process for success.

Whether you‘re doing computer vision, NLP, speech recognition, or other ML workloads, properly labeled training data is key. And for many companies, outsourcing that data labeling is the best approach.

Let‘s dive in!

Why Outsource Data Labeling?

Data labeling for machine learning is extremely labor intensive. Just ask the team at Vectorform, an AI consultancy who found that data prep comprised a whopping 80% of their ML project timelines.

Handling data labeling in-house requires hiring and managing teams of data annotators. For ad hoc labeling needs, this overhead doesn‘t make economic sense.

This is why most companies turn to external partners for data labeling. According to an Alation survey, 83% of organizations outsource at least part of their data labeling work.

The reasons for outsourcing data labeling typically include:

Speed – Specialized data labeling firms can annotate data 3x faster than in-house teams, per a Google study. These vendors have ready access to large pools of qualified data annotators, unlike hiring internally.

Cost – Outsourcing labeling through a third party is significantly more affordable than building an in-house team, with estimated savings of 50% or more.

Flexibility – Data labeling needs fluctuate substantially. Outsourcing provides flexibility to scale labelers up and down to match volumes.

Domain Experience – Labeling firms have experience across data types – images, video, text, audio – that in-house teams are unlikely to possess.

Quality – Leading vendors implement multi-stage quality assurance and control processes that result in greater labeling accuracy.

Data Security – Reputable labeling providers adhere to strict data security standards and modern security controls.

According to labelers like Alegion and Appen, large majorities of their clients choose to outsource labeling for these reasons – the benefits outweigh the downsides.

That said, outsourcing isn‘t perfect…

Potential Downsides to Outsourced Data Labeling

Outsourcing your data labeling work does come with some tradeoffs to be aware of:

  • Loss of control – You have less visibility into and control over the labeling process compared to in-house.
  • Communication overhead – More time spent communicating requirements to external labelers.
  • Data privacy risks – You must transfer data to the labeling vendor, which increases attack surface area.
  • Quality uncertainty – Quality can vary substantially between different labeling firms.
  • Lock-in risk – Switching data labeling vendors means moving data and processes.

Despite these drawbacks, many organizations decide the benefits still outweigh the downsides. For others, keeping labeling in-house or using a crowdsourcing approach makes more sense.

How do you decide? Let‘s compare outsourcing in more depth to these other approaches…

Outsourcing vs. In-House Data Labeling

Handling data labeling completely in-house avoids the downsides of outsourcing, but comes with its own challenges:

  • Slower speed – In-house teams lack the specialized resources of data labeling vendors.
  • Higher cost – Significant overhead to hire, train and manage data annotators and build labeling workflows.
  • Lower flexibility – Scaling an in-house team up or down is difficult and slow.
  • Steep learning curve – Developing the range of data labeling expertise that vendors possess takes substantial time.

Studies suggest outsourcing can cut data labeling costs by 50% or more compared to in-house teams. These savings result from not having to hire, train, manage, and build tooling for internal data annotators.

This is why most organizations outsource, while keeping some labeling capabilities in-house to train internal teams. A hybrid approach provides the best of both worlds.

Outsourcing vs. Crowdsourced Data Labeling

Crowdsourcing data labeling using public pools of workers online offers advantages like:

  • Ultra-low costs – Crowdsourced workers accept very low pay, as little as $1 per hour.
  • Limitless scale – Crowdsourcing platforms give access to potentially millions of workers.

However, crowdsourcing poses major downsides:

  • Low data quality – With anonymous workers, quality is difficult to control and validate.
  • Data privacy risks – Few restrictions on how crowdsourced workers can use data.
  • Lack of specialization – Crowd workers unlikely to have specific domain labeling expertise.

While crowdsourcing works for basic tasks like image tagging, most organizations use professional data annotation services requiring specialized expertise and quality standards.

The State of Data Labeling Outsourcing

As demand for AI and machine learning has boomed, so too has the data labeling outsourcing market.

Recent reports size the market at over $1.6 billion in 2022, with expectations it will reach $7.6 billion by 2030. That‘s a compound annual growth rate of 31%.

What‘s driving this exponential growth? A few key trends:

  • Skyrocketing demand for labeled data to train computer vision and NLP systems
  • Shortages of in-house data annotators at enterprises
  • Expanding use of semi-supervised learning requiring some labeled data
  • New self-service labeling platforms democratizing access

North America leads demand, but Europe and APAC are also seeing surging interest. The APAC data labeling market alone could be worth $1.6 billion by 2024.

With so many organizations turning to outsourcing to meet growing labeling demands, choosing the right data annotation partner is crucial…

Choosing The Right Data Labeling Provider

When evaluating and selecting an external data labeling provider, some key criteria include:

Proven Track Record – Look for demonstrated experience within your required data domains – e.g. computer vision, NLP, speech, medical, etc.

Turnaround Time – Assess ability to deliver within your required timeframes and flexibility to scale up/down.

Data Security – Review security standards, certifications, controls and processes to ensure adequate data protections.

Quality Processes – Examine their training processes, quality assurance practices and tools to measure and ensure labeling quality.

Team Expertise – Consider experience level of their labelers, project managers, and solution architects that you‘ll interact with.

Technical Capabilities – Assess compatibility with your data formats, ML tooling, and API/integration options.

Communication – Gauge responsiveness and clarity during the sales and evaluation process.

Pricing – Compare base pricing models and discounts offered across shortlisted vendors.

I‘d recommend starting with a pilot project to directly evaluate vendors against these criteria before making a full commitment.

Some leading data labeling service providers include:

  • Alegion – Specialized in privacy-safe enterprise data labeling
  • Appen – Massive scale data labeling across geographies
  • CloudFactory – Ethical labor data labeling based in developing world
  • Playment – Optimized for computer vision data labeling
  • iMerit – On-demand data labeling service for machine learning

There are also more niche players focusing on specific verticals – like medical imaging, sentiment analysis, or conversational AI.

And traditional business process outsourcing firms offer data labeling services complementary to their broader offerings.

The right partner for your needs depends on your data types, volumes, timelines, budgets, and quality expectations.

Data Labeling Pricing Models

Pricing for outsourced data labeling services varies based on:

Data complexity – Simple images are cheaper than complex 3D point clouds or lengthy audio.

Provider capabilities – Pricing scales with provider quality and rigor.

Country – Labor costs differ, with developing world cheapest.

Pricing model – Per hour, per unit labeled, monthly subscription plans, etc.

Some typical price ranges:

  • Image annotation – $0.01 to $0.08 per image
  • Text annotation – $40 to $100 per hour
  • Video annotation – $0.10 to $1.50 per minute
  • Speech annotation – $0.70 to $2.50 per minute
  • Point cloud annotation – $4.00+ per minute

Pricing model options each have pros and cons:

Per hourSimplest modelHard to estimate costs
Per unitPredictable costsComplex to plan
SubscriptionFixed monthly feeOverage fees
On-demandHighly flexiblePremium pricing

Be sure to ask potential vendors for quotes tailored to your specific dataset needs. Most will provide free pilots to demonstrate capability and enable accurate pricing estimates.

Ensuring High Quality Labeling

With outsourced data labeling, you relinquish some control – so extra diligence is required to ensure labeling quality.

Issues that can arise include:

  • Inconsistent label quality
  • Errors from labeler fatigue
  • Ambiguous edge cases labeled incorrectly
  • Guideline misinterpretations by annotators
  • Missed retraining as team scales up

Here are some best practices vendors use to maximize quality:

  • Detailed guidelines and training on labeling schemas
  • Qualification testing to onboard labelers
  • Multiple labelers per data item with consensus
  • Multi-stage review and quality control
  • Technology assisted quality checks
  • Bonuses and gamification incentives
  • Continuous retraining with real examples

As the client though, you retain responsibility for clearly communicating requirements, including edge cases, reviewing results, and providing ongoing feedback. Treat outsourcers as an extension of your team.

Spot checks on samples labeled externally versus internally also help catch issues early. This lets you re-align with vendors quickly.

Key Takeaways on Data Labeling Outsourcing

Let‘s recap the key insights from our guide on outsourced data labeling:

  • For speed, cost, flexibility and quality reasons, most companies outsource at least some of their machine learning data labeling work.
  • Choose data labeling partners carefully based on proven expertise, security, quality rigor, communication practices, and integration with your tech stack.
  • Start with a pilot before committing fully to validate quality and capabilities relative to your needs.
  • Treat external labelers like members of your team through clear requirements, tight feedback loops, and continuous process refinement.
  • Implement spot checks, audits, and other QA practices to ensure the quality you need for effective ML model training.
  • Consider a hybrid approach of outsourcing the bulk of labeling while keeping some capability in-house.

I hope this guide provides a comprehensive yet accessible overview of everything you need to know to maximize the value of outsourced data labeling in 2023. Reach out if any questions!

Similar Posts