Artificial Intelligence (AI) is a topic that we have heard almost everywhere, regardless of the place or occasion. It became such a popular concept that it saturated almost everything from the corporate sector to the film industry.
However, this technology is not just a buzzword, but an important tool for companies to improve their operational efficiency.
With numerous use cases from around the world on how AI improved certain processes, more and more companies began to realize that this innovative technology is the new competitive battleground.
You may be one of those who understand the many benefits of AI and would like to strengthen your business by incorporating it into your daily operations.
However, before getting started with this technology, it is important to understand all the steps your organization will need to take to implement and take advantage of AI.
Importance of knowing the stages of AI implementation
Knowing the steps required for project execution is important regardless of what you do, but it is especially vital when it comes to Artificial Intelligence solutions.
Understanding the AI project life cycle will help you identify the details that need attention, such as more information about the deliverables and what job roles need to be assigned at each stage. Ultimately, this translates into gaining control of the project more effectively.
AI project cycle and stages
Generally, the AI project consists of three main stages:
- Stage I – Project planning and data collection
- Stage II – Design and training of the Machine Learning (ML) model
- Stage III- Deployment and maintenance
However, the specificity of an AI project is that it does not stop at the implementation stage, but rather follows a cyclical process.
Now that we understand the importance and nature of the AI project cycle, let’s dive into each stage in more detail.
Stage I – Project planning and data collection
This is an initial stage, although very important and crucial as it explores the reasons why you decided to implement artificial intelligence solutions in your operations, as well as anticipating if the solution can be tangible and profitable.
What’s your problem?
When it comes to AI solutions, we are its main promoters. However, we also always emphasize the importance of implementing AI only when there is a real need.
The main driver of your AI project should be a real problem or an objective. This is very important, as not all cases require the implementation of AI to achieve desired results.
You should think, analyze and evaluate if your problem can be solved with simpler solutions (like simple automation) or if it really requires more complex resources like Artificial Intelligence.
However, if based on AI expert consultation, your assessment, user feedback, and real case studies, your team decides to start the AI journey, this means that you have identified the problem/objective and are ready to move on to the next step.
For every problem there is a solution
Now that you identified your problem you can focus on the solution. There are many AI solutions on the market that have worked for different businesses, but it is important to understand that your use case is unique and therefore requires a solution that is tailored to the needs of your business.
There may be various solutions that can benefit your business, from energy demand forecasting to automated data extraction. Therefore, it is vital to discuss possible solutions with AI experts to ensure access to both the important business information from your side and the technical knowledge of the experts, resulting in more accurate planning.
P.S. We know how challenging or even costly it can be to dedicate time and resources to the initial stage of problem and solution identification.
That is why we offer our potential clients a free consultation call with our Artificial Intelligence experts so that together we can brainstorm and discuss whether your company can gain real benefits from our custom AI solutions.
Do you trust your data?
From self driving cars to facial emotion recognition, data is at the core of all AI projects. Artificial Intelligence systems are capable of recognizing patterns and making decisions thanks to statistical models; For this to happen, data is required for the system to learn the correct patterns.
However, when creating effective AI solutions, the challenge is not simply the availability of data, but rather the availability of large amounts of varied and high-quality data.
Poor quality data not only prolongs projects, but also leads to more costly results, such as Machine Learning models not working properly and not delivering the desired results.
Therefore, it is important that the data for your AI solution is not only of high quality but also relevant to the problem you are facing.
So where can you get this data from?
- Primary sources – Primary sources are sources that provide data that originates from your own company. You can acquire such data from tools like CRM or IoT devices.
- Secondary sources – Secondary sources refer to external sources that have relevant data of interest to you. Those can be Third-Party data providers or government publications.
Since AI algorithms require a large amount of data to get accurate results, secondary sources can be a great way to fill that gap and enrich the data by adding more attributes if data from the primary source isn’t enough.
Once you’ve collected and cleaned your data, it’s time to tag it. Data labeling refers to the process of detecting and tagging data samples. It helps your ML models identify and understand the meaning of digital data.
Data labeling provides a learning foundation for your models to later recognize the same patterns in the new unstructured data.
The process can be done manually and also with the help of special software. This can be a time-consuming and tiring process, however, it is very important. Correctly labeled data is vital to the success of your AI project.
Too busy to dedicate your team to data labeling? That is where MaxinAI services come in handy. Partner with us and let us handle the entire process (we’ve done it many times, we know what we’re doing) so you and your team can focus on more important tasks. Schedule a free consultation with our experts right now!
Stage II – Design and training of the Machine Learning (ML) model
Choosing the right ML model depends on a number of factors, such as the type of challenge your business is facing, the type of result you want to achieve, the size of your data, etc.
Some of the types of ML models are:
- Binary classification model – As the name implies, ML models for binary classification problems predict a binary outcome. For example:
- Is this email spam or not (yes or no)?
- Is this app review written by a real person or a bot?
- Multiclass classification model – In this case, models predict an outcome into one of three or more classes. For example:
- Is a child holding a toy, a book, or a pen?
- Is this genre of music rock, jazz or hip hop?
- Regression model – ML models for regression problems predict the relationship between a single dependent variable and one or more independent variables.
- What will energy use be in California tomorrow?
- How much of product A will be sold this month?
Training your model
In this important step, we will feed our data into our ML algorithms. This process gradually improves the ability of the models to produce the desired result (identify the object, predict the outcome, etc.)
Like everything in life, practice makes better. This means that during the initial training process your model’s output will have low accuracy, however this is normal and there is nothing to worry about, as with more and more training processes your model will improve.
After training your model, as well as evaluating and testing its performance, it is time to move on to the next final stage.
Stage III- Deployment and maintenance
Finally, we come to the model deployment stage. This means that we must implement it in an environment with a web interface or some kind of application where the new data can flow and our ML models can show the analysis in the new interface.
An artificial intelligence solution that predicts energy consumption for energy providers would take related data, analyze it, and send its prediction to a web portal or app for companies to view and act on. Such tools simplify the decision-making process for end-users.
However, just because you’ve launched your AI solution live doesn’t mean the project is done. As in the previous steps, an equally important part is monitoring, reviewing, and making sure that your solution continues to deliver the desired results.
Most likely some adjustments and alterations will be required. This will depend on your customer and staff feedback or on trial and error. New data may be entered into the model to ensure that the results are accurate and up-to-date.
A report showed that for 40% of companies the process of deploying an ML model took more than a month, for 28% of companies it required 8 to 30 days and for 14% it took less than a week.
Now you have a clear understanding of each stage of the Artificial Intelligence project. This way you can better prepare and plan your operations to ensure a successful implementation of AI solutions.
You might have also noticed that some of the stages and steps are more complex than others. If your team does not have enough time or lacks the expertise in Artificial Intelligence, our services would be perfect for your project.
MaxinAI’s team of more than 60 experts has helped companies around the world with customized machine learning solutions. We can help you with your AI project by taking on the more complex tasks or we can take care of the entire project, the choice is yours.
Schedule a free consultation with us and let’s kick start your AI journey!