Over the last eighteen months, logging into your LinkedIn account has felt nearly impossible without being inundated with AI posts on your feed. Artificial Intelligence (AI) is far from a new concept, but we are now seeing some incredible developments in the field starting to come to fruition.
Even with the recent boom of new AI products, there is still some uncertainty around what and how businesses should leverage AI within their organizations. With those identical LinkedIn posts suggesting to “adopt or get left behind,” some version of the question, “How can my business leverage AI to become more productive?” lingers for many organizations, and ultimately, it needs answering.
Plenty of tools, platforms, and companies offer AI services and solutions to help companies implement artificial intelligence into their daily lives. This can feel like a lot of technical noise that needs deciphering to find the correct fit for your organization. As an AI and data consulting firm, we at phData help clients succeed by simplifying the adoption of AI tools from AI companies that genuinely add business value.
In this blog, we will unpack everything you need to know about LandingAI, including an overview of the LandingAI platform and MLOps philosophy, the use cases where LandingAI has been applied, and its cost.
What is LandingAI?
LandingAI is a software company that develops state-of-the-art, data-centric artificial intelligence computer vision applications tailored to specific industry needs. The platform allows organizations to build custom large vision models (LVMs) or utilize their domain-specific LVMs to extract AI image insights with tremendous accuracy, significantly accelerating the time-to-insight for image analysis.
Even with limited data sets, companies have built computer vision models and started realizing AI value while seamlessly moving computer vision projects from proof-of-concept to full-scale production without prior AI knowledge.
Data-Centric AI
A few challenges of AI adoption are the barrier of entry, lengthy and ambiguous development periods, and inconsistent data for training models. Landing AI’s data-centric approach to their product enables businesses to start and scale their computer vision project reliably, efficiently, and systematically.
So, what is data-centric AI? Simply put, it is a change in focus from the code in a model-centric approach to focusing on the data in their data-centric approach. With this shift in focus from arduous, time-consuming coding to collaborating on quality image data on Landing AI’s intuitive platform, companies have seen significant improvements in how they build models and the success of the output:
10x faster build times for computer vision applications
Reduced time to deployment of CV applications
Improved yield and accuracy with less data
The beauty of this approach is that it is collaborative. AI model development is no longer reserved for data teams. Still, it allows subject matter experts and developers to work together to reach a consensus on how AI should understand image data. By focusing on ensuring clean image data that clearly conveys what the AI must be learning, companies can garner greater model accuracy with less data on a platform that allows the entire team to train, analyze, and monitor their model.
Effectively, data-centric AI is more than just about producing better results from the model. It’s about instilling a more consistent, efficient, and cooperative AI development process to produce better results faster. This data-centric approach is fully realized with Landing AI’s flagship product, LandingLens.
LandingLens
LandingLens is the industry-leading AI Computer Vision software platform from Landing AI. It is an end-to-end software platform designed for domain professionals and AI experts to quickly develop and deploy visual classification systems. With the ability to create and test computer vision AI projects in minutes with low/no code, the most impressive part of LandingLens is how accessible and intuitive the platform is for users. There is no need for complex programming or AI knowledge to begin building a custom computer vision model.
LandingLens requires no setup as a cloud-based solution, and a new user can begin building AI computer vision models in less than a day. LandingLens is comprised of a unique suite of tools to automate machine learning for computer vision. The key advantages to using the interactive platform include:
Data Management: LandingLens includes various tools and features that help a team align on how their data needs to be labeled. Additionally, the platform allows your team to test how well the labels align and overlap automatically. Having a living electronic document of labels as the single source of truth enables teams to forego speculation on how image data needs to be classified. Additionally, LandingLens includes data management features to easily split your data into training, validation, and test sets.
Model Iteration: LandingLens offers the flexibility of a professional ML operations team while removing the complexity of training models in a traditional coding environment. Codeless model training, automatic model tuning, and live model training progress are some features that make model refinement in LandingLens simple and iterable. With built-in performance metrics on the interface, showing where your model is strong and where it needs improvement is easily understandable. With out-of-the-box model architectures available in LandingLens, many options exist for computer vision use cases, including object detection, semantic segmentation, and image classification.
Deployment: Deployment is certainly where the value of your training efforts comes to realization – namely, getting production-ready models to make decisions on your behalf. With device fleet management, environmental checking & change detection, dashboards, and a data flow that integrates with the rest of the platform, you can retrain models on newly captured data. With seamless integration, regardless of the hardware you use, your team can deploy and manage an infinite number of AI edge devices. LandingAI offers several ways for your organization to deploy models to edge devices, including a Windows application, the LandingLens Docker app, a cloud instance (EC2 Wavelength in AWS), and more.
With the entire MLOps workflow in one system, LandingLens gives confidence to organizations looking to implement computer vision into their operations regardless of the data size, changing environments and requirements, and users’ AI expertise.
What Are the Top Use Cases for LandingAI?
If a picture is worth a thousand words (and a video is thirty pictures per second), then the breadth of practical applications for computer vision is quite vast. Metaphors aside, LandingAI provides solutions to improve quality, efficiency, and productivity across all industries through AI and deep learning.
Here are a few of the solutions where LandingAI is helping resolve critical challenges unique to their respective industries:
Automotive
The complexity of auto parts presents particular challenges with expected defects, especially at the automotive industry’s scale and velocity. When safety and reliability are imminent parts of your product, the automotive industry needs equally reliable and consistent solutions. Computer Vision and artificial intelligence solutions can help.
Computer vision can solve automated inspection challenges at scale to a degree previously thought impossible. Landing AI’s deep learning workflow simplifies the development of automated assembly solutions that identify, classify, and categorize defects, reducing waste and improving production yield.
Landing AI’s Automotive AI/Deep Learning Use Cases Include:
EV Battery Inspection
Weld Inspections
Dispensing/Coating Verification
Glass/Scratch Inspection
Condition/Asset-Based Monitoring
Leak Detection
Healthcare & Life Sciences
If there were ever an industry that demanded precise accuracy and output of its information, HCLS, Biotech, and Pharmaceuticals would all be atop the list. Even the slightest deviation in cell count and structure or the smallest foreign contaminant in a vial can be problematic, leading to serious liability issues and costs.
Computer Vision and AI can assist medical professionals and manufacturers by identifying threats in medical devices, pharmaceutical drugs, and even human cells. With strict compliance standards in place, companies use Landing AI’s workflows to prevent compromised products from leaving their factory floor.
Landing AI’s Medical/Biotech/Pharma Use Cases Include:
Vial Contamination Inspection and Counting
Nano-scale Medical Device Inspection
Medical Seal Inspection
Disease Detection & Diagnosis
Cell Sorting
Tracking, CFR 2.11 Validation
Manufacturing
Like the automotive industry, manufacturers must detect even the most subtle defects during assembly operations—every defective or compromised product results in losses and potential liabilities for the company.
LandingLens provides a platform for OEMs, system integrators, and distributors to quickly develop & deploy their AI model and evaluate its production efficacy for a single application or as part of a hybrid solution combined with traditional 2D/3D machine vision and robotic control solutions.
Landing AI’s Manufacturing Use Cases Include:
Assembly Inspection/Validation
Complex Defect Detection & Classification
Process Monitoring and Yield Improvement
Cost Reduction
Root Cause Analysis
Predictive Maintenance
What Do We Love About LandingAI?
There’s a lot to be excited about regarding artificial intelligence applications. However, implementing AI into a company’s operations and decision-making has felt like a technological benchmark that is impossibly out of reach for most companies due to the endless coding, model refinement, and huge amounts of data required. Moreover, trusting a model you’ve built as a reliable decision engine can be a challenging threshold to cross for most organizations.
So, what do we love about LandingAI here at phData? Their product has resolved those barriers that traditional, code-centric AI presents. LandingLens is accessible to business users and data professionals; it doesn’t require vast amounts of data, and the development/deployment process is streamlined to scale quickly. Most importantly, LandingLens has shown the ability to produce precise results that can be trusted as a decision engine in many industries with seemingly limitless applications for computer vision analysis.
Finally, we are excited about LandingAI’s partnership with Snowflake, which offers native applications in the Snowflake AI Data Cloud Marketplace. phData is actively testing the integration and is very excited about the results.
LandingAI & Snowflake
The partnership between LandingAI and Snowflake enables organizations to harness the power of computer vision on unstructured data directly within their Snowflake account. Models can be trained within the LandingAI platform and deployed into Snowflake as User-Defined Functions. Landing AI’s pre-trained models are also available to deploy through the Snowflake Marketplace as Snowflake Native Applications.
So, why is this partnership important? By bringing the models and compute closer to the data, the integration allows for improved security and governance while offering more control over the infrastructure the model runs on. Through this integration, the engineering team incurs no additional operational overhead. The model interface is supported by a Streamlit UI, which allows non-technical users to engage with their unstructured data.
How Much Does LandingAI Cost?
LandingAI is free to try and includes 1000 credits/month, three user seats, and unlimited projects with one active project for model downloads. Here is the general breakdown of pricing plans for Landing AI:
LandingAI recommends the free tier as the best option for individuals, the visionary tier as the ideal choice for small businesses, and the enterprise plan as the best option for large organizations looking to scale securely.
What are Credits?
Credits are a form of currency on the LandingLens platform. They can train a model or run inferences (like the Predict functionality). A credit is equivalent to one image trained, one image inference, or an image deployed to a cloud endpoint.
Consider this example with the free tier of 1,000 credits/month:
Train a project with 200 images that will cost 200 credits, leaving you with 800.
Test 20 images by dragging and dropping them into the Predict Window. This will cost 20 credits, leaving you with a remaining balance of 780 credits.
We like the results and want to deploy the model to a cloud endpoint. You send the trained images to the cloud endpoint, which costs you a credit each.
Note that for custom training, changes in the settings will impact the cost of each image trained and inferences. Once you reach the 1,000 credit limit on the free tier, you cannot run any training or inferences on the pictures. On the contrary, if you exceed the 5,000 credit limit on the Visionary Tier, each additional credit will cost $0.01. Credits do not roll over each month for the free or Visionary plans.
Closing
Landing AI’s philosophy of data-centric AI gets fully actualized with a computer vision platform, LandingLens, that produces incredibly accurate results, even with limited data. Moreover, Landing AI’s product encourages a culture of efficient development practices typically reserved for the most mature AI teams. With a unique blend of accessibility, intuitiveness, and insightfulness, LandingAI is the gateway for commercialized computer vision AI.
We hope this introduction has helped you understand LandingAI’s philosophy, core functionality, and use cases in artificial intelligence and computer vision.
FAQs
I’m interested in LandingLens. How can I try it?
LandingLens offers a free trial, which costs 1,000 credits per month. Sign up here to start building models.
