How to Get Started with Artificial Intelligence – A Guide to Set AI Projects Up for Success

#1 Understanding the Core Value and Objectives

First and foremost, question yourself about the ultimate goal as well as the value you wish to achieve from your AI project. Would you like to simply build a basic solution with artificial intelligence or create a complete AI-driven tool that serves your business goal? What benefits does your company hope to bring out by investing in such technology? How will this affect or change your business objectives?

Ask yourself all the questions that come up in your mind about the core value you wish to reap from artificial intelligence technology, try to analyze your AI initiatives from different angles and be clear about the final objectives of your AI projects. Once the management and executives have a basic understanding of the core value and objectives of what they are about to do, they will have a clearer picture of what they should do next and how much they should invest in this.

This step sounds theoretical, isn’t it? But a majority of businesses that are initiating AI projects have no idea of what they are expecting to achieve or where the project will head, eventually ending up failing or wasting time and resources for nothing. They simply chase after artificial intelligence development as everyone else is doing it or because they would like to make their businesses appear as if it is up-to-date with the latest technology trends. This driver is known as FOMO (fear of missing out), and it is avoidable with a little more critical business thinking. So, do not follow in their footsteps if you wish your AI project to come to fruition.

Instead, try to make clear the objectives of your AI project from the very beginning. Remember to identify the pain points of your business, and AI solutions should be born to resolve them for you. Moreover, the goal you set should be realistic. Of course, you can dream high with your project, but in the end, the anticipated outcome must be feasible in the context of your business. All of these things must be well-documented before mapping out a project plan.

#2 Figuring Out the Right Tech

There is a wide array of technologies and software solutions available in the market that you can make use of for your AI project, ranging from the programming language for AI, development tools, and testing tools to cloud services as well as big data solutions. For example, if you are planning to build a computer vision application, you will need to consider TensorFlow, Keras, Caffe, Torch, and so on, while in natural language processing, common choices include NLTK, spaCy, Gensim, and others. Therefore, it can be really overwhelming for businesses to make the decision on which tech stack is the most suitable for their project.

The key here is first to understand your project requirements and then match them with the right tools and technologies. Consider what your project is trying to achieve, as we emphasized previously and what data you have available; only then can you start narrowing down your options and select the good resources for your project.

#3 Finding the Right Talent

Two recent reports by Gartner revealed that the percentage of AI and machine learning projects that fail to deliver accounts for 85%, while only 53% of projects make their way from prototypes to production. Unfortunately, the key to the success or otherwise failure of AI projects lies within the people factor more than the technology itself. Therefore, the next step to getting started with AI is to have the right talent by your side to run and manage your AI project. Or it means building an AI development team with the necessary skills and experience that are required to see your project through till the end.

Similar to how to hire software developers, you need to consider a lot of factors before you can hand-pick the right talent for your team. Select those who have actual experience in developing AI applications or at least have a good understanding of machine learning concepts. You can start by finding data scientists or data engineers with a background in mathematics, statistics, and algorithms, as they will be able to apply their knowledge to understand and solve various issues related to data. Make sure you also have people who are good at critical thinking and problem-solving so that they can find creative solutions to difficult challenges.

You can always start by looking internally first, see if there are any employees with the relevant skillsets and experience, and then move on from there. Alternatively, outsourcing a dedicated development team or hiring IT team augmentation services is a better choice in case you have no or do not have enough in-house resources available for the upcoming AI project. Developing AI talent within your organization or hunting them is such a daunting task. Instead of struggling with the recruiting, training, and management processes, you can enjoy having experts working for you on an as-needed basis. Not to mention, this will also save you time and money in the long run. But whatever route you choose to take, make sure that you do not forget the importance of having the right talent on your side, as they will be the ones spearheading your initiatives.

#4 Optimize Your Data

Data is the core of any AI project, as it is used to train machine learning models so that their algorithms can learn how to make predictions. For this reason, you will not be able to train your machine learning models or get any valuable insights from data if you do not have enough high-quality data to work with. In other words, your data should be vetted to ensure it is clean, well-labeled, and accurate for you to get the most out of it. Accordingly, it is significant to prepare and optimize data before feeding it into the machine learning models.

According to the AI Creation Hierarchy Of Needs, the data normally goes through 5 main stages before making its way to artificial intelligence or deep learning development: Collect, move/store, explore/transform, aggregate/label, and learn/optimize. But we can simply wrap them all up into two main phases – data preparation and optimization – that cover all the stages above:

• Come to the first phase – data preparation, as it helps to clean and organize your data so that you can use it for training machine learning models. There are various data preparation techniques that you can use, such as data imputation, data normalization, and data standardization. Data imputation is used to replace missing values with plausible ones, while data normalization is used to rescale values so that they fall within a specific range. Data standardization, on the other hand, is used to transform values so that they have a mean of zero and a unit variance.
• In the second phase – data optimization is used to reduce the amount of data that you need to work with. Data optimization techniques include feature selection and dimensionality reduction. Feature selection, or variable selection, is used to select the most relevant features from a dataset, while dimensionality reduction is used to reduce the number of features in a dataset. These techniques play a vital role as they can help to improve the efficiency of machine learning algorithms.

Once you have optimized your data and got it in the ready position, you will be one step closer to developing high-quality machine learning models. What’s next?

#5 Build a Lasting Foundation

No skyscraper stands tall without a solid foundation, and neither does an AI project. Now that you get the two most fundamental things – people and data - in place, it is time to roll up your sleeves and get started with AI by building its foundation. Normally, projects involving AI-driven technologies can be a longer venture than we think, so any business seeking the power of AI should approach it as something that can be scalable and long-lasting over time. Unlike any basic application, you complete and launch it, and done.

Whether it is supervised or unsupervised learning, there is always a need for a system that can be continually optimized as new data comes in. This means the AI model needs to be nurtured continuously. Artificial intelligence development is no small feat since the AI model needs to be nurtured continuously. If not, it soon ends up being outdated and no longer utile. The data scientists have to constantly conduct ML experiments and retrain the models to not only make good predictions but also refine the models over time as the data varies and evolves.

• One thing you must do is set up an AI center of excellence (CoE) - a key organizational structure for implementing and managing an AI initiative. The AI CoE will serve as the foundation where you can experiment with new ideas, define processes and best practices, and develop reusable assets. It acts as a single source of truth for your organization and helps to ensure that everyone is on the same page. By having a centralized AI CoE, you will be able to avoid duplication of effort, increase transparency, and improve collaboration across the organization. The AI CoE should be composed of three key teams: a business team, a data team, and an engineering team. The business team is responsible for identifying opportunities and use cases for AI, the data team is responsible for preparing and optimizing data, and the engineering team is responsible for building and deploying machine learning models. These three teams need to work closely together to ensure the project runs smoothly and secure the outcomes.
• Apart from setting up an AI CoE, you should also define processes and best practices for your organization. This includes creating guidelines on how to develop, deploy, and operate machine learning models. By streamlining your workflow in the first place, it is viable to ensure that your AI project is scalable and sustainable in the long run.
• Last but not least, you should also focus on developing reusable assets. These assets can be used by different teams across the organization and are profitable in cost and resource saving and utilization. Some examples of reusable assets include data pipelines, machine learning libraries, and model templates.