Organisations all over the world are now shifting its business strategy towards emerging technologies like artificial intelligence and machine learning. Currently, there is an acute need for developers this field and that’s why it’s the perfect time to delving into executing an AI project which will not only help you to make your resume look good but also it will shape your future into the path of emerging technologies.
Some of the most important factors for a successful project are:
- Its positive outcome
- The duration of time for completing the project
- The cost-effectiveness and quality of the project deliverables
However, there are few other common dilemmas of what tools should one use, how should one proceed, what should be the strategy, what about the data, among others while starting an AI project.
In this article, we jot down five steps which will help you to measure success in your first AI project.
1. Determine the Purpose
Before starting a project, it is very important to determine the purpose of the project. First, you need to have a clear concept of the algorithms such as supervised, unsupervised, reinforcement, semi-supervised learning algorithms. Then, one should consider every measure and analyse the big picture that will be visible after completion of the project.
2. Determine the Objectives
AI is an ever-evolving technology. There are plenty of objectives one can choose to build a successful project. Various sectors like banking, finance, healthcare, e-commerce, and many others are implementing AI algorithms in order to get real-time outcomes with accuracy. One needs to get a solid objective of how the specific problem will help in the near future such as quality of research, user satisfaction, etc.
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3. Data Building
Data is an integral part of an AI project. It is the data which will determine how your model will behave and what will be the quality of the outcomes. Building the right algorithm solely depends on using the right data. There is an abundance of data everywhere and one should be careful while choosing one. Not only there are a number of online sites from where you can download data but also there are popular organisations who are open-sourcing their data to the public. While digging for data, mixing and merging from various sources can provide you with a good set of data. After collecting the right data, there are various processes that one must consider to prepare good dataset such as data pre-processing, data cleaning, data manipulation, data evaluation, etc.
4. Efficiency Checking
Checking and measuring the outcome of a project will help in understanding how successful the initiative is. In such cases, iteration provides a crystal clear view of the algorithms that you have been working on and will be able to solve such complex problems. In the case of a beginner, it will help to understand and dig deeper into the mechanism of the algorithm.
5. Considering The Visualisation
In the current scenario, one has to deal with large volumes of data. As you are going to start a new journey, it is very important to get familiar with visualisation tools. For instance, Tableau is one of the most popular data visualisation tools that is used by a number of organisations to extract the hidden insights from the data. It is one of the best ways to explore what is hidden behind the large pile of data and develop interesting features using that.
Artificial Intelligence is still at the infancy stage and it can be said as the high time one can pursue his/her career into this field of evolving technology. Following these steps will not only lead you to complete an AI project successfully but also help you in the near future when you will deal with more complex problems.
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A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box. Contact: firstname.lastname@example.org