Data-savvy businesses increasingly rely on images and videos for critical functions, and yet are challenged by the sheer mass of information—more than 3.2 billion images and 720,000 hours of video are created daily. This explosion in visual data has paved the way for the growth of computer vision, a form of artificial intelligence (AI) that enables computers to “see” the world similarly to the way people do, but with unblinking consistency, and greater accuracy. 

The transformational impact and value of computer vision solutions are significant and has been a guiding objective for companies and AI developers. And yet, even as the applications for computer vision increase dramatically, architecting and implementing vision AI solutions remain highly complex. Visual data, such as images and video, are made up of thousands of pixels of information that represent millions of different patterns and meanings, which can make interpreting even a single image overwhelming from a computational perspective.

Many organizations struggle with deployments and fail to operationalize vision AI solutions due to development delays, machine learning and data science hiring challenges, inaccurate output, a lack of integration with existing infrastructure, difficulty of use, and high cost. Plainsight, with the power of Google Cloud resources, is addressing all these challenges and helping businesses by enabling the deployment of vision AI within enterprise private networks that can be managed easily and scaled economically.

Plainsight has announced availability of its vision AI platform on Google Cloud Marketplace. Businesses can now easily deploy end-to-end vision AI to private clouds to realize the full value of their video and other visual data for accurate, actionable insights across diverse use cases.

Delivering on the Promise of AI: Seeing What’s Hiding In Plain Sight

For organizations to integrate AI and machine learning into their businesses successfully, the technology must be powerful enough to solve real challenges, yet fast, easy, and accessible enough to ensure the innovation potential is realized. Plainsight on Google Cloud delivers the power of enterprise vision AI that’s quick and easy to use with Google Cloud resources that enable global scale, increased security, bolstered privacy, unified billing, and cost savings. 

To streamline vision AI workflows, Plainsight facilitates the entire pipeline, from visual data ingestion and annotation, through continuous model training, deployment, and monitoring for easier innovation and faster time-to-production. Our platform accelerates vision AI development in a manner that is complete, accurate, and accessible to non-technical business leaders. We believe that AI should be available and accessible to anyone and everyone—so that teams across entire organizations can reap the benefits. 

By integrating Plainsight into their private networks, companies worldwide can now leverage one intuitive platform for centralized control of streamlined vision AI model creation and training with optimized visual data handling for diverse enterprise solutions. These use cases include: social distancing monitoring, medical imaging, drug compound screening, defect detection in manufacturing processes, identifying gas leaks, or even livestock counting and crop health monitoring for agriculture, to name a few.https://www.youtube.com/embed/A7U_0UkjvEg?enablejsapi=1&

We enable customers so they can create successful solutions that enable them to clearly see their business from all angles and to take advantage of the knowledge visual data can reveal by simply and quickly operationalizing practical vision AI applications.

AI-Powered Dataset Creation, Automated Model Training & Easy Deployment Without A Single Line Of Code 

For vision AI applications, success is inextricably dependent on the quality and quantity of the datasets required to train the relevant models. To aid enterprises in this vital stage, the Plainsight platform provides built-in data annotation for the fast and easy creation of datasets. This includes AI-powered features that accelerate the speed and quality of labeling such as SmartPoly, for the automated polygon masking of objects, TrackForward, to predict and automatically label objects from frame to frame in video annotations, and AutoLabel for automated object recognition and labeling based on pre-trained machine learning models, to highlight a few.

In addition, to ensure the success of AI integration, we significantly reduce time-intensive processes with Plainsight vision AI’s automated machine learning with continuous model training and easy deployment capabilities. In just a few clicks, users can leverage optimizations for the most reliable model training without endless experimentation cycles. And, models are easily deployed at scale all within one, easy-to-manage model operationalization process for the business.

Growing With Google Cloud

Plainsight is a vision AI innovation leader, developing solutions that address unmet needs for challenger brands and Fortune 500s across vertical markets. As a team recognized for succeeding where others have failed, our expanding partnership with Google Cloud provides a powerful combination that helps customers see and activate the value of their visual data with a suite of services in a secure and private manner.

Our vision AI Platform simplifies building and operationalizing AI to solve business problems enterprises are facing every day—and the demand is increasing. To accelerate our journey to faster, more accessible AI for enterprises, we knew we needed strong support to grow Plainsight and scale our backend tools to match our vision. 

Google Cloud delivered everything, and more, in one program. The Startup Program by Google Cloud provided the technology and services for scale and the support we needed to maximize the value the Program provided us. The Startup Program has been a springboard for architecting Plainsight vision AI in the cloud, accelerating our goals and optimizing innovation, efficiency, and growth. The team also helped us optimize Google Ads campaigns, fueling adoption of Plainsight. 

After launching the SaaS version of Plainsight Data Annotation in November 2020, we grew our user base by nearly 110x in just three short months. Google Ads has also dramatically increased website traffic, growing new users by nearly 5.75X and page views by over 5X. The Google team helped us identify where Google Cloud offerings could be leveraged instead of developing in-house solutions and offered best practices that enabled us to deliver faster on our initiatives. 

Kubernetes was already the underlying component of our platform and leveraging Google Kubernetes Engine (GKE) as a managed service removed a layer of complexity. By combining GKE and Anthos, we were able to standardize our deployments, aligning to how our customers leverage Anthos for enterprise applications in their own organizations. In addition, as a fast-moving, customer-centric company we use Google Workspace to help us centralize and manage our day-to-day work internally. By leveraging multiple products across Google’s ecosystem, we take advantage of a holistic partnership that has helped our business tremendously as we scale. 

Leveraging Google’s Partners for Strategic Consultation

To facilitate this expansion of our partnership with Google and to maximize our use of Google Cloud services, we are working with DoiT International, a Google Managed Services Provider and 2020 Global Reseller Partner of the Year. DoiT provides us with ongoing technical consultation for cloud-native architecture, Google Cloud Marketplace integration, production-grade Kubernetes support, Google Cloud cost optimization, and technical support. The DoiT team has been invaluable in compiling best practices, tips, and strategies from their vast experience with various cloud customers to ease our Marketplace integration and is providing input for infrastructure strategy to support our continued rapid growth.

Plainsight Delivers Enterprise Vision AI Through The Google Cloud Platform Marketplace

Plainsight vision AI is now available to Google Cloud Customers on Google Cloud Marketplace enabling organizations across industries to deploy private Plainsight instances within their own environments. Marketplace customers will benefit from Google Cloud privacy, security, scalability and unified billing through their Google Cloud account. 

Combining the powerful benefits provided by Google Cloud resources with Plainsight’s vision AI Platform into private networks, enterprises worldwide can now leverage one intuitive platform for centralized control of streamlined vision AI model creation and training with optimized visual data handling for diverse enterprise solutions. 

Through our Google partnership, we’re able to leverage a powerful foundation that allows us to rapidly innovate, scale and accelerate delivery on our vision AI platform capabilities. By executing on our vision to make AI easier, faster and more accessible for all users across entire enterprises, we’re helping businesses see more and by seeing more, they’ll have the power to solve more. 

If you want to learn more about how Google Cloud can help your startup, visit our page here where you can apply for our Startup Program, and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.

Imagine ordering a “ready-to-assemble” table online, only to find that the delivery package did not include the assembly instructions. You know what the end product looks like, but have little to no clue how to start assembling the individual pieces to get there. A poorly designed API tends to create a similar experience for a consumer developer. Well designed APIs make it easy for consumer developers to find, explore, access, and use them. In some cases, good quality APIs even spark new ideas and open up new use cases for consumer developers.

There are methods to improve API design — like following RESTful practices. But time and again we are seeing customers unknowingly program minor inconveniences into their APIs. To help you avoid these pitfalls, here are six of the most common mistakes we have seen developers make while creating the API — and guidance on how to get it right.

#1 Thinking inside-out vs outside-in

Being everything for everybody often means that nothing you do is the best it could be, and that is just as true for APIs. When customers turn to APIs, they are looking for specific solutions to make their work easier and more productive. If there is an API that better works to their needs, they will choose that one over yours. This is why it’s so important to know what your customers need to do their work better, and then building to fill those needs. In other words, start thinking Outside-in as opposed to Inside-Out. Specifically,

• Inside-out refers to designing APIs around internal systems or services you would like to expose.
• Outside-in refers to designing APIs around customer experiences you want to create. Read more about the Outside-in perspective in the API product mindset.

The first step to this is learning from your customers — be it internal consumer developers or external customers — and their use cases. Ask them about the apps they are building, their pain points, and what would help streamline or simplify their development. Write down their most significant use cases and create a sample API response that only gives them the exact data they need for each case. As you test this, look for overlap between payloads and adapt your designs to genericize them across common or similar use cases.

If you can’t connect with your customers — because you don’t have direct access, they don’t have time, or they just don’t know what they want — the best approach is to imagine what you would build with your APIs. Think big and think creatively. While you don’t want to design your APIs for vaporware, thinking about the big picture can make it easier to build non-breaking changes in the future. For example the image below showcases APIs offered by Google Maps. Even without diving into the documentation, looking at the names like “Autocomplete” or “Address Validation” clearly outlines the purposes and potential fit for a customer’s use case.

#2 Making your APIs too complex for users

Customers turn to APIs to bypass complicated programming challenges so they can get to the part they know how to do well. If they feel like using your API means learning a whole new system or language, then it isn’t fitting their needs and they will likely look for something else. It’s up to your team to make an API that is strong and smart enough to do what your customer wants, but also simple enough to hide how complicated the tasks your API solves for really are. For example if you know your customers are using your APIs to present information about recently open restaurants and highly rated pizzeria to their consumers, providing them with a simple API call as below would be of great help:

GET /restaurants?location=Austin&category=Pizzeria&open=true&sort=-priority,created_at

To see if your API design is simple enough, pretend you are building the whole system from scratch — or if you have a trusted customer who is willing to help, ask them to test it and report their results. If you can complete the workflow without having to stop to figure something out, then you’re good to go. On the other hand, if you catch rough edges caused by trying to code around system complexity issues, then keep trying to refactor. The API will be ready when you can say that nothing is confusing and that it either meets your customers’ needs or can easily be updated as needs change.

#3 Creating “chatty” APIs with too many calls

Multiple network calls slow down the process and creates higher connection overhead — which means higher operational costs. This is why it’s so important to minimize the number of API calls.

The key to this is outside-in design: simplify. Look for ways to reduce the number of API calls a customer must make in their application’s workflow. If your customers are building mobile applications, for example, they often need to minimize their network traffic to reduce battery drain, and requiring a couple calls instead of a dozen can make a big difference.

Rather than deciding between building distinct, data-driven microservices and streamlining API usage, consider offering both: fine-grained APIs for specific data types, and “experience APIs” (APIs that are designed to power user experiences. Here is a further theoretical discussion on Experience APIs) around common or customer-specific user interfaces. These experience APIs compose multiple smaller domains into a single endpoint; making it much simpler for your customers — especially those building user interfaces — to render their screens easily and quickly.

Another option here is to use something like GraphQL to allow for this type of customizability. Generally you should avoid building a unique endpoint for every possible screen, but common screens like home pages and user account information can make a world of difference to your API consumers.

#4 Not allowing for flexibility

Even if you’ve followed all of the steps above, you may find that there are edge cases that do not fit under your beautifully designed payloads. Maybe your customer needs more data in a single page of results than usual, or the payload has way more data than their app requires. You can’t create a one-size-fits-all solution, but you also don’t want a reputation for building APIs that are limiting. Here are 3 simple options to make your endpoints more flexible.

Filter out response properties: You can either use query parameters for sorting and pagination, or use GraphQL which provides these types of details natively. By giving customers the option to request only the properties they need, it guarantees that they won’t have to sort through tons of unnecessary data to get what they need. For example, if some of your customers only need the title, author, and bestseller ranking, give them the ability to retrieve only that data with a query string parameter.

GET /books?fields=title,author,ranking

• Ability to sort with pagination. Generally, you don’t want to guarantee the order of objects in an API response because minor changes in logic or peculiarities in your data source might change the sort order at some point. In some cases, however, your customers may want to sort by a particular field. Giving them that option, combined with a pagination option, will give them a highly efficient API when they only want the top few results. For example Spotify API utilizes a simple offset and limit parameter set to allow pagination. A sample endpoint as shown in the documentation would look like this

$ curl https://api.spotify.com/v1/artists/1vCWHaC5f2uS3yhpwWbIA6/albums?album_type=SINGLE&offset=20&limit=10

• Use mature compositions like GraphQL: Since customer data needs can differ, giving them on-the-fly composites lets them build to the combinations of data they need, rather than being restricted to a single data type or a pre-set combination of data fields. Using GraphQL can even bypass the need to build experience APIs, but when this isn’t an option, you can use query string parameter options like “expand” to create these more complex queries. Here is a sample response that demonstrates a collection of company resources with embedded properties included

"data": [
{
"CompanyUid": "27e9cf71-fca4",
"name": "ABCCo",
"status": "Active",
"_embedded": {
"organization": {
"CompanyUid": "27e9cf71-fca4",
"name": "ABCCo",
"type": "Company",
"taxId": "0123",
"city": "Portland",
"notes": ""
}
}
}
]

#5 Making design unreadable to humans

“K”eep “I”t “S”imply “S”tupid when you are designing your API. While APIs are meant for computer-to-computer interaction, the first client of an API is always a human, and the API contract is the first piece of documentation. Developers are more apt to study your payload design before they dig into your docs. Observation studies suggest that developers spend more than 51% of their time in editor and client as compared to ~18% on reference.

For example, if you skim through the payload below it takes some time to understand because instead of property names it includes an “id”. Even the property name “data” does not suggest anything meaningful aside from just being an artifact of the JSON design. A few extra bytes in the payload can save a lot of early confusion and accelerate adoption of your API. Notice how user-ids appearing on the left of the colon (in the position where other examples of JSON ideally have property names) creates confusion in reading the payload.

"{id-a}":
{ "data":
[
{
"AirportCode": "LAX",
"AirportName": "Los Angeles",
"From": "LAX",
"To": "Austin",
"departure": "2014-07-15T15:11:25+0000",
"arrival": "2014-07-15T16:31:25+0000"
}
… // More data
]
},

We think that JSON like this is more difficult to learn. If you want to eliminate any ambiguity in the words you choose to describe the data, keep the payload simple and if any of those labels could be interpreted in more than one way, adjust them to be more clear. Here is a sample response from Airlines endpoint of aviationstack API. Notice how the property names clearly explain the expected result while maintaining a simple JSON structure.

"data": [
{
"airline_name": "American Airlines",
"iata_code": "AA",
"iata_prefix_accounting": "1",
"icao_code": "AAL",
"callsign": "AMERICAN",
"type": "scheduled",
"status": "active",
"fleet_size": "963",
"fleet_average_age": "10.9",
"date_founded": "1934",
"hub_code": "DFW",
"country_name": "United States",
"country_iso2": "US"
},
[…]
]

#6 Know when you can break the RESTful rules

Being true to the RESTful basics — such as using the correct HTTP verbs, status codes, and stateless resource-based interfaces — can make your customers’ lives easier because they don’t need to learn an all new lexicon, but remember that the goal is just to help them get their job done. If you put RESTful design first over user experience, then it doesn’t really serve its purpose.

Your goal should be helping your customers be successful with your data, as quickly and easily as possible. Occasionally, that may mean breaking some “rules” of REST to offer simpler and more elegant interfaces. Just be consistent in your design choices across all of your APIs, and be very clear in your documentation about anything that might be peculiar or nonstandard.

Conclusion

Beyond these common pitfalls, we have also created a comprehensive guide packaging up our rich experience designing and managing APIs at incredible scale with Google Cloud’s API management product, Apigee.

Apigee — Google Cloud’s native API management platform — helps you build, manage, and secure APIs — for any use case, scale or environment. Get started with Apigee today or check out our documentation for additional information.