Organizations are constantly weighing the cost and benefit of investing in Artificial Intelligence (AI) solutions. Introducing advanced predictive analytics to a company can push them to the bleeding edge of innovation and past their competitors, however, the hurdle is often difficult to get over. But why?
First, understand how we define AI
The term AI can mean several different things; however, the most commonly used definition refers to the idea of intelligent machines, which is in slight contrast to the aspirational machine with human-level intelligence. There are endless ways to implement Artificial Intelligence, but the primary ways are via machine learning and deep learning.
Machine learning uses labeled historical data to train a model to understand patterns and make accurate predictions on new and unlabeled data points.
Deep learning is a subcategory of machine learning revolving around neural networks. While neural networks have been around for decades, they have truly exploded in research and use in the last five to ten years. Deep learning is used to solve problems that require a human such as image recognition, text/speech analytics, and decision making (i.e. game playing).
For the sake of this article, we will use AI as a synonym for machine learning and deep learning, even though AI in general may refer to software and hardware having human-level intelligence which is not achieved using current methodologies.
Let’s focus on 3 key areas to get the most from AI
At 10th Magnitude, our data intelligence community focuses on bringing analytics to solve our customers’ problems through data science, reporting, and big data pipeline projects.
Outside of developing solutions we encourage organizations to focus on the following cultural- and process-oriented areas to truly get the most out of their AI solutions.
Know Your Business Use Case(s) and Collaborate
The majority of AI applications are powered by machine learning, which is used to solve a very specific problem using data.The first thing that I do with a customer who is new to machine learning is to understand and identify all of their business problems. These problems often turn into new use cases for machine learning or deep learning.
Since the use cases are derived from the business itself, the key to creating a successful solution is collaboration between the data science team and business stakeholders. Additionally, stakeholders are likely the individuals who will need to approve the completion or evaluate the success of the developed application
Therefore, understanding what is needed to solve the problem and then relating the problem back to the data is crucial. Keeping the stakeholder aware of the development cycle allows them to understand the challenges data scientists encounter when creating new predictive analytics workflows.
Additionally, this enables the organization to develop a data-driven culture. Involving business users in the development room gives non-technical folks insight into what is possible, allowing them to spot other areas where AI can be of use.
10th Magnitude believes in the idea of data-driven design, where we use data to solve problems, power applications, and change the way an organization thinks about their business.
Don’t Stop After Development
Developing a machine learning solution is difficult. It is an iterative cycle where individuals go back and forth from the business to understand the problem, gather data, and train models. Developing these solutions takes time, however, once development is done you are only partially completed with the project.More often than not we see customers give up on a solution after the development portion because the model did not perform as well as hoped or the cost to put it all in production is simply too high. It takes a lot of work to move a solution from a development environment to a production one.For example, we recently worked with an organization to build and develop a model to detect anomalies for their different pieces of equipment. It took 2-3 weeks to develop the solution and an additional two weeks to set it up in production; we had to move the code to a production workload, build and release pipelines for two environments, model consumption, model monitoring, and more.
As data scientists, we often forget the difficulty and the amount of time it takes to move a solution to a production so that the organization is able to see the true benefit.
It is important to keep in mind that empowering an application or workflow with machine learning is about more than just the application. It also gives people the ability to see what is possible with the data.
Automation is Your Friend
Usually, data scientists are not familiar with automated build and release pipelines, but it is a skill that is quickly becoming a requisite in order to properly participate in the predictive analytics space. DevOps is the process and culture of delivering value to customers in a sustainable manner. As predictive insights grow organically within an organization, individuals need to be available to develop new solutions. and not maintaining existing ones. Automation is extremely useful in data science projects, specifically for: deploying changes to production with automated tests, retraining of existing model with new data, and monitoring the performance of the model.No data scientists should bring “right-click and deploy” predictive solutions to production; unfortunately, that happens more often than one would hope. Using Visual Studio Team Services (VSTS), we enable our customers to version control their code for team collaboration and set them up with automated build and release pipelines to train, test, and deploy their code.
As more data is collected, the solution will need to be retrained on a cadence to keep the model up to date so that it continues to make good predictions. While this task may seem like a trivial manual task, the time it takes a data scientist to update a model could be used to create new solutions or enhance old ones.
Often clients will only focus on surfacing results to their end users via reports, applications, or workflows; they forget that they need to build an interface to their solution for themselves.
Data scientists are responsible for maintaining the quality of a solution over time, therefore, the metadata gathered from testing the solution (success criteria, training time etc.) should be stored and visualized to understand the current and historical performance of a solution.
Developing machine learning and deep learning applications is far from easy. However, clients often struggle with the amount of effort it takes to create custom solutions, or they get so bogged down in technical details that they forget the why their business started on the path to AI in the first place.
So, what are the keys to successfully incorporate AI into your organization? To start, collaboration between the data science team and business stakeholders, understanding the data science process, and deploying solutions using DevOps. This process makes predictive analytics possible for data science teams of all sizes even as it changes the mindset of the organization as a whole.
If you’re ready to bring AI into your day-to-day, 10thMagnitude has the solutions to incorporate it seamlessly and painlessly, ensuring that you get the benefits without missing a beat.