AI product engineering is one of the most intriguing development methodologies in the present times. It involves a user-defined interface, data analyst teams as well as business inputs to create a product that is technologically viable and commercially successful. In the domain of product engineering, it is important to identify the challenges that we may face during the entire journey of product development. More importantly, it is advised to come up with a plan so that the business journey becomes successful and is devoid of pitfalls. It has been witnessed that businesses proceed for product development without adequate knowledge of tools and techniques of artificial intelligence. It is in this case that an artificial intelligence course is highly advisable before embarking on the technical journey. The best artificial intelligence course is the one that gives a bird’s eye view of various technical aspects of AI like analytics, data management, and product engineering.
A clear roadmap
Let us take a look at various steps that clearly set the roadmap for successfully conceiving an AI product.
Value assessment
The first step for conceiving an AI product involves value assessment taking into consideration factors like input cost, value enhancement as well as commercial output. For instance, let us consider one of the most important products of Google, that is, Google Analytics. When this product was launched for the first time, the role of the analyst teams was to assess the value of a product on the basis of various factors. Business analysts working with Google predicted that Google Analytics would be a game-changer for small and big businesses alike. It was predicted that Google Analytics would increase the revenue of companies by more than 25% within the first quarter itself. At the end of the first quarter, when the results were compared, they were found to be related to the ones that had been predicted by the analyst teams.
Drafting the problem statement
In the problem statement, the questions related to various business metrics are addressed. We also take stock of the methods that we need to use to solve a particular problem. This is done by identifying the inputs as well as the outputs. Machine learning techniques have been very successful in solving problem statements and predicting the outcome of an event with a high degree of accuracy. The choice of machine learning technique to be used depends on the training data as well as the test data set. Accordingly, supervised, unsupervised or reinforcement learning techniques may be used to counter the problem statement.
Resource coordination
It is extremely important to coordinate all the resources including people and data to make sure that an innovative AI product comes into existence. An AI product involves a highly skilled team that consists of machine learning engineers, data scientists, data Architects, ML Ops professionals, and other experts in the field of artificial intelligence and machine learning. Their inputs are extremely crucial while the product is being conceived. They provide input to the model and help in deriving solutions to the problem statement. They also help in coordinating data sets that may be segregated across the databases. They help in deriving insights from these segregated data sets that help in design modification and decision sciences. Finally, they are also specialized in frameworks like TensorFlow and PyTorch and help in the development of a model as well as its internal architecture.
Integration stage
The main task that we perform in the integration stage is related to microservices. The integration of microservices determines the final shape of the AI product. Even after the final product has been conceived, the process of monitoring needs to continue to track the development of the project as well as its commercial potential. This is done with the help of peer review as well as feedback surveys. We also lay a lot of focus on the architecture of the model, the hyperparameters, and the data versioning. If a new tuning needs to be done in the model, it is subsequently taken up in the next versions. This ensures that continuous development of a product takes place along with periodical revisions.
Concluding remarks
In the future, we may see the development of new products and services that are inspired by artificial intelligence techniques and methodologies. The commercial potential of AI product engineering is slated to increase in the future given the new technological dimensions and business ventures that we are exploring.