All Resources

Data science, Gen AI, and AI solutions experts discuss real-world AI implementation challenges and solutions. Learn about building secure AI systems, leveraging large language models, and accelerating time to market with model inference.

Model monitoring of real-world apps by CloudFactory helps GameChanger attain higher customer satisfaction and fan engagement across youth sports.

Data annotation from CloudFactory helps GameChanger reach new heights in data quality so it can continue to grow youth sports.

CloudFactory data labeling empowers aerial inspectors to drive efficiency of solar panels and wind turbines to help power plants and properties avoid million-dollar losses.

Accelerated Annotation helps Charles River Analytics scale high-quality data annotation to fuel stronger maritime AI initiatives.

Accelerated Annotation helps an AgTech leader deliver optimized crop yields through faster, more accurate ML models.

Outsourcing data labeling work can free up your teams to focus on accelerating AI/ML development. This guide will help you choose the right provider, process, and technology.

Unlock top computer vision model building, annotation acceleration, and automated labeling approaches to enhance the efficiency and accuracy of your automation processes.

Humans in the loop can make or break AI success. Here’s how to apply people strategically to improve data quality and accelerate the AI lifecycle.

Uncover how to get the most value from your AV data through accurate annotation in this on-demand webinar.

Ocuvera meets its human-centric mission faster with CloudFactory, resulting in an 89% reduction in unassisted bed exits.

How a combination of AI, automation, and humans in the loop enables you to scale quickly while maintaining data quality.

Scaling your data labeling operation effectively and efficiently: Lessons from our 10M+ hours of labeling experience.

Interested in natural language processing? Empower yourself with knowledge about emerging use cases, development and annotation techniques, and managed data labeling workforces.

Spatial AI innovator Allvision meets its ambitious smart city GTM mission 8x faster thanks to our flexible partnership.

Taking ethics beyond algorithms: The real scope of conventional AI ethics and the critical role of humans in the loop.

How to leverage aerial inspection data with humans in the loop and use AI to cut asset inspection and management costs.

Discover how AI and machine learning are advancing the insurance industry from sales to underwriting to claims processing. You’ll also get a first-hand glimpse into ten insurers and insurtech companies that are digitizing, modernizing, and innovating at scale.

Selecting the right workforce partner is key to optimizing your data pipelines and executing safe, reliable AV models. Ask these 10 critical questions during your evaluations.

Expert annotations help an asset management platform open capacity on congested power lines and boost grid reliability.

Discover safer, faster, lower-cost, and innovative ways of working. We also share what we've learned from working with leading precision agtech companies, including scalable data prep and labeling.

CloudFactory image annotation helps Compound Eye release its disruptive, camera-based AV technology into the world.

There are five key areas where adding autonomy to drones will enable you to address regulatory hurdles, scale delivery operations, and conduct safe deliveries and landings.

Learn how Agritecture, Asahi, and Microsoft help Czech hops farmers fight climate change using computer vision and technology.

Automation doesn’t always mean AI when it comes to autonomous vehicles. Learn how the levels of driving automation are determined by the presence of automated driving systems (ADS) with AI technology or automated driving assistance systems (ADAS).

Data quality and workforce strategy can make or break the success of your autonomous vehicle project. This guide will show you how to optimize your AV data pipeline using multiple workforces to launch safe, reliable autonomous driving models.

Busy physicians shouldn't do tedious data labeling work for medical AI projects. Here’s how much this mistake might cost you.

Learn how image and video annotation helps retailers produce high-quality robotics and autonomous solutions for in-store, warehouse, and delivery.

Perception and data processing enhances drone inspections by powering automated navigation, digital twin creation, post-processing data, and real-time data processing.

Pathr.ai helps retailers understand shopper behavior to improve sales and operations.

In this webinar, a panel of drone tech pioneers talks about how AI and computer vision, backed by high-quality data, is shaping the future of drone inspections.

Retail AI is booming. Learn how computer vision and AI are revolutionizing in-store and fulfillment experiences for retail customers and employees.

Given all the variables at play, choosing a managed workforce that meets your criteria can be a daunting task. Use this fillable Scorecard as you research potential partners.

When it comes to your data processing practice, quality is NOT optional. It affects accuracy, speed, and value—in big ways. For the highest possible data quality, follow our proven 4-Part Quality Management Framework.

With CloudFactory image annotation, Matterport can focus on helping customers create interactive 3-D digital twins

Medical advancements depend on data and artificial intelligence. How do you prepare image and video data for diagnostics, robotics, research, and more? Find out in this guide.

With CloudFactory data enrichment, agtech company Tractor Zoom has grown from advertising $2B to $5B in assets annually.

Explore the challenges and opportunities in developing computer vision solutions for healthcare including annotation tools, data labeling workforces, and medical AI use cases.

This paper details a four-part quality management framework to ensure high-quality processing of financial data and documents, whether you outsource or do the work in-house.

Adding AI and computer vision backed by high-quality data will improve drone safety and reliability. This guide will show you how applying AI to drone delivery operations will address regulatory hurdles and help you scale quickly.

V7 Labs’ Alberto Rizzoli and CloudFactory’s Keith McCormick discuss overcoming data annotation challenges like scaling teams, labeling complex data, and handling edge cases.

Sartorius came to CloudFactory to speed the development of large training datasets for its machine learning models.

In this webinar, we explore the definition, benefits, and use cases for human in the loop (HITL) machine learning from the perspective of seasoned data science practitioners.

Sylvera came to CloudFactory for a customized annotation solution to make sure carbon-offsetting projects live up to their claims.

Keith McCormick and Jonathan Reichental discuss smart cities, the data they collect, and the impact of AI technology like autonomous vehicles.

Keith McCormick and Usama Fayyad discuss autonomous vehicles, deep learning, NLP, and the future of data science in this on-demand webinar.

Interested in humans in the loop machine learning? Watch a replay of Keith McCormick and Robert Monarch’s discussion about HITL, workforce options, ethics, and active learning.

A geospatial mapping company turned to CloudFactory for help to scale a new roof geometry business.

Recycling tech company turned to CloudFactory when its growth outpaced internal capacity to annotate quality data.

Now is the time to implement robotic process automation (RPA). We discuss maximizing ROI, workforce decisions, and exception processing with Ian Barkin of SYKES.

Geospatial mapping company turned to CloudFactory to take over its data labeling function and scale operations.

Web scraping technology company turned to CloudFactory to speed up development of a new scraping tool.

How should you combine your predictive models with existing business rules? Watch this discussion with James Taylor, CEO of Decision Management Solutions to learn how to optimize ML outcomes.

How can humans in the loop help optimize machine learning outcomes? Hear from ML experts CloudFactory's Keith McCormick and Dean Abbott, Chief Data Scientist at SmarterHQ.

FestiVote turned to CloudFactory to get the data they needed to launch their groundbreaking app.

Geckomatics turned to CloudFactory to help scale their cost-effective solution to more customers around the world.

Scholars App turned to CloudFactory to build and vet their scholarship database and expand their users.

Data annotation is seeing advances such as using AI and automation that seemingly promise to eliminate the need for human-powered labeling. This panel explores the future of humans-in-the-loop data annotation and what role your labeling workforce will play in the years to come.

True Lark turned to CloudFactory to help them scale data tagging for development of a robust customer communication solution.

Computer vision models require a massive amount of data to learn and stay current. It takes upwards of 800 hours to annotate one hour of video training data and you certainly don’t want to choose the wrong techniques or workforce to annotate that data. This guide will help you make good choices about data annotation for computer vision.

Gathering an initial data set for your machine learning project is the first hurdle on the path to a successful ML algorithm. CloudFactory and Keymakr discuss the attributes of an ideal data set, the pros and cons of using a pre-created data set, and best practices for building your own.

Some data annotation tools won't be a good fit for your AI and machine learning project. Keep these six important features in mind as you evaluate tool providers.

Learn how CloudFactory helped Hummingbird Technologies develop crop analytics for farmers around the world.

CloudFactory helped Luminar launch a product that dramatically increases autonomous vehicle perception.

Machine learning is often fueled by image data. In this guide, learn the basics about image annotation, common techniques, and key workforce considerations.

Selecting the right tool to annotate your data can save you time, money, and frustration. This guide will help you choose the best data annotation tools for your machine learning project.

The end goal for every data labeling project is quality data - but how do you get there? There are several QA workflow types but each has pros and cons when it comes to the quality and speed of data outputs. In this panel discussion, we will explore 5 quality assurance workflows for data labeling including tooling, staffing, and how each workflow affects throughput and data quality.

Are you training machine learning algorithms or planning to scale your team's training data operations? Here are some tips to ensure your people, processes, and tools produce quality training data.

Sensor-as-a-service start-up finally finds an annotation solution that can help them expand.

Heretik wanted to make legal contract review less tedious so they created an AI-fueled solution. Learn how CloudFactory helped bring their software to life.

Data quality is vital when creating reliable algorithms. For companies looking for a solution that equals the quality of their in-house team, it can seem as though outsourcing is an impossible option. Have no fear. This eBook will help you predict the level of quality you can expect from a data labeling provider.

Learn how CloudFactory helped Heretik train their AI models and disrupt the legal industry by streamlining the contract review process.

Data scientists at Hivemind created 3 identical tasks and hired two teams to complete them. How did crowdsourced workers and a managed workforce differ in terms of accuracy, speed, and effective cost?

Bizly found the perfect workforce to help it expand its event planning platform to over 18,000 venues.

Not all outsourced data labeling partners are a good fit for every machine learning project. Here are 5 things you need to consider before, during, and after vendor evaluations.

Your in-house data scientists shouldn't be doing tedious data labeling work for machine learning projects. They should be focusing on more important innovation. How much might it cost for a data scientist to annotate a one-hour video?

When you’re creating high-performing machine learning models, you need quality, labeled data...and lots of it. Getting it can be a challenge. A growing number of innovators are outsourcing data labeling operations so their teams can focus on strategy and innovation. Choosing a data labeling partner is an important decision that can affect your model performance and speed to market. Here’s what you need to ask when you’re looking for a data labeling service.

It takes a lot of time and resources to prepare and label data. Learn why outsourcing the data preparation to a managed workforce partner is a good business decision and how in-house responsibilities will shift when you outsource the work.

The people, processes, and tools used by outsourced data labeling partners make a big difference in final data quality. Here are 3 signs that you'll receive quality work from your data partners.

The level of data quality you'll receive from data labeling providers depends on several workforce, QA, and tooling factors. Here are 6 ways *some* data labeling providers put your data quality at risk.

Medical AI company stays ahead of the curve by labeling 24,000 images in 6 months.

An insurance company expanded their business with the help of a dedicated cloud team that handles upfront data processing needs.

It takes a mountain of data to train, test, and build machine learning algorithms and AI projects. You may be considering hiring a data labeling service to take the burden off your in-house data scientists and machine learning engineers. But what does that entail? Watch the webinar to learn what you need to know before hiring a data labeling service.

Financial services company leveraged a managed workforce to improve turnaround time from days to just minutes with 99% accuracy.

Data scientists at Hivemind tested CloudFactory’s managed workforce and a leading crowdsourcing platform’s anonymous workers to complete a series of the same tasks, ranging from basic to more complicated, to determine which team delivered the highest-quality structured datasets and at what relative cost.

Azavea interviewed a handful of leading data labeling firms, and studiously compared their pricing, approach, and cultural values. Learn why CloudFactory stood out.

If you need people to process some portion of the big data that feeds your artificial intelligence, you need a reliable workforce. You’re not alone: more businesses are using in-house staff, contractors, and crowdsourcing to get this kind of work done, and industry analysts expect that trend to increase significantly over the next two years.

Ibotta finds the people and technology to scale a data-verification process during the busiest retail season of the year.

Data is today’s gold, representing huge potential value for businesses. By analyzing data, you can discover patterns that can help you make smarter decisions, improve products, and disrupt entire industries. Download our guide to solve the dirty data problem.