Achieving A.I. Business Outcomes with Techolution’s AutoAI

AI has become somewhat of an imperative for businesses to remain competitive and innovative. AI projects hold a great deal of promise for businesses across varied sizes and industries with big bottom-line stakes. Of the companies that have rolled out AI enterprise-wide, 25 percent of those surveyed expect to increase revenue, according to a PwC AI Predictions survey.

The business possibilities around the combination of A.I., IoT, AppDev, and Cloud are nearly limitless. Nearly half of 5000 global IT professionals say their companies are exploring A.I., according to the IBM Global AI Adoption Index 2021. But that exploration is exposing the challenges of AI in terms of how to implement and effectively use it for real business outcomes.

Overcoming Challenges of Harnessing AI for Business Outcomes

Many of the challenges around AI initiative implementation come in the form of poor data quality and governance, where your data is highly distributed and riddled with redundancies and inaccuracies. Some problems require data scientists, which are extremely expensive and difficult to find.

Statistics compiled from just three major job services show that there was a data scientist shortage of 250,000 in 2020, according to consulting firm Quanthub. Nearly 75% of surveyed adopters agree all enterprise applications will integrate A.I. within three years, according to a Deloitte Survey. Making that reality holds some barriers for a significant number of businesses.

The IBM Global AI Adoption survey shows only 47% say they have a high level of skill around technologies, suppliers, and implementation. But this need for data science skills and AI methodologies and tools that can deliver proven business results pales compared to the question of how to maintain an AI initiative after it has begun.

The AI model you create on day one will not be the ideal model needed for the future. A.I. models are iterative by nature, so businesses will have multiple iterations after their initial model. This will deliver multiple performance capabilities as these models integrate fresh data and get smarter.

The challenge of how to operationalize AI and get it into production is something most businesses have yet to encounter. The Techolution team has developed a methodology and the tools that businesses need to overcome these challenges. Our approach is focused on helping your business develop an iterative AI model that delivers business outcomes called AutoAI.

The Business Outcome Benefits of Techolution AutoAI

Businesses are often starting with highly distributed data consisting of many types of data from CSV files, XML data, data lakes, or Snowflake to many other data types and repositories. Effectively using data to create data sets for AI when the data lives in so many locations mean a lot of integration points. Our approach is to help businesses to bring it all into a centralized place for better access.

The next step is to develop an intuitive user interface (UI) that allows you to map out the data sources based on your A.I. business objective so it’s easier to apply it to data sets. At this point, Techolution works with your team to leverage open-source frameworks.

There are many open-source frameworks available with the needed capabilities built into them, such as framework platforms like H20.ai. This is a fully open source, distributed in-memory machine learning platform with linear scalability

This and other frameworks offer a free open-source model alongside its paid product. While the paid solution does a lot of the heavy lifting, the ideal scenario is to have the capability to use several AI framework platforms to develop the best model.

Creating Ideal, Iterative A.I. Models with Open Source and AutoAI

The ideal scenario for any business is to create the best model possible and continually refine it with new data. That iterative approach ensures the model continually improves and gets smarter at the function for which it’s designed. Techolution’s Auto AI approach does the following for your business:

• Get to market faster with a simplified deployment model
• Go from zero to AI at twice the normal speed
• Enables you to avoid the need for leveraging a lot of data scientists’ knowledge by using our existing staff of experts in a fractional CoE model
• Deliver the capabilities to leverage a wide variety of open-source libraries to deliver the MLOps of operational A.I. right out of the box.

Nearly 75 percent of surveyed adopters agree that AI will be integrated into all enterprise applications within three years, according to a Deloitte Survey. The same survey shows only 47% of the same respondents say they have a high level of skill around technologies, suppliers, and implementation.

The Techolution AutoAI approach speaks to these needs for data science expertise and having a variety of the right tools to create the best model. These are things most businesses lack for harnessing the business power of AI across applications in the cloud.

Our approach at Techolution is to help you leverage open source and give you the power of access to an ensemble of different frameworks from which to choose. You can then let these open-source frameworks come up with the best approach and the best model for whatever your business is trying to do.

The ability to use a variety of open-source AI platform frameworks gives your business the ability to create a binary of these different models. Techolution will then take this binary and post it into a Git repository, which enables the following process steps:

1. Once placed in a Git repository, your binary frameworks will now be capable of satisfying the iterative process so it accommodates the need for your AI model to constantly learn from new data. This means it’s constantly learning, constantly upgrading it, constantly creating binaries, and constantly uploading them into a repository.
2. We then create a system that monitors these processes.
3. This starts what is essentially an RPA process, which enables us to create a flask API around the binary and the A.I. models. We do this because having a binary doesn’t provide any business outcome value if you’re interacting with a variety of sources, let’s say a web application, for example. You need a way to create an API capable of interacting with the application.
4. Once we have the API, we can build a container around it and throw it into something like cloud run or some sort of serverless profile capable of executing on that, and then deploy it into a container.

So now you have your API, which gives you endpoints for the AI model, so you now have your AI model for executing your decision on whatever you’re trying to figure out. You now have the capability to plug your model into your Web API, application, users, or business analytics. Essentially, whatever you want to enable for those decisions you’re trying to make.

This iterative process becomes automatic so that you can then create these models to have a great process for operationalizing your AI. To learn more about AutoAI, download our AutoAI solutions brief here. Then, contact us to see how we can harness AutoAI's power to drive your business outcomes today and tomorrow.