You can easily lose focus while developing and working on a machine learning project, from getting lost in your team’s directory files to repeatedly running the same script from previous work. In other words, inefficiency at work can cause unnecessary delays for you and your team.
Worry not, as this article will tips and guidelines to help you in your machine learning project journey—from hiring the right manpower to choosing a dependable image annotation tool for correctly interpreting and labeling data.
How to organize machine learning projects
These guidelines are intended to provide machine learning amateurs and pros with a general overview of what to follow and consider when working on a project.
Choose and prepare resources
When developing a highly advanced machine learning project, having the correct tools helps save time and improves work quality. Get the correct computer system with sufficient specifications, including storage and a reliable network. This is to ensure that it’ll improve the overall process and latency will not be an issue. Remember that having the proper hardware can make a huge difference.
You must also strike a balance between the software side of things, including investing in a robust image annotation tool. Having dependable and efficient tools early on won’t just reduce inefficiency but also reduce the team’s workload. Imagine requiring only one member of your team to handle image labeling with a powerful image annotation tool, rather than having to employ additional personnel to speed up the labeling for your project.
Focus on labeling data
Simply put, data labeling is the process of adding identifiers (labels) to information in order to make it accessible and useful using automated systems. It’s possible to train machine learning algorithms to accurately recognize comparable data based on previously labeled data. Data labeling is essential to the success of modern business processes, which rely on machine learning and artificial intelligence to carry out complex tasks. (1)
Labeling and documenting data correctly is a prerequisite in moving forward with machine learning projects. It is crucial to accurately label your resources because altering your labeling method in the future can be challenging, if not impossible. The team must establish the project’s file structure and codebase to facilitate efficient collaboration. If you lack manpower, you may also hire a specialized team of freelancers to handle the data labeling for you. It would also lessen the chances of mistakes, given they are experienced in the field.
Hire the right individuals
Having a great selection of talent is one of the critical factors in achieving the two significant benefits—efficiency and replicability. The 2022 Global Leadership Monitor Survey from Russell Reynolds showed that 72% of business leaders believe that the lack of skilled talent is the top concern for their organizations—superseding other challenges like uncertain economic conditions, health concerns, etc.
With this in mind, having a skilled analyst must be a priority. A data analyst’s duties include:
- Utilizing their methods and resources for collecting data.
- Assessing the quality of the dataset.
- Drawing conclusions to help the team.
Check project feasibility
When choosing the best project, it’s crucial to consider its potential, its impact on the business, and the information and dataset you have access to and are readily available to the team. By having these datasets, you can evaluate if what you have is enough for it to be considered the best project from your choices.
If a proposed activity has a significant impact and may be employed in the project, but the data is not yet ready, the outcomes are likely to fall short of expectations.
Also, a project with a lot of data that isn’t viable may just be a waste of time and money. Project viability is determined by several factors, including but not limited to:
- Data acquisition costs
- Data labeling efforts
- System usage patterns
- The availability of a machine learning (ML) use template
- Resource requirements and limitations
A project’s success can be projected if the team establishes concrete goals and key performance indicators (KPI) after conducting an in-depth analysis of the available data. This is vital as the KPIs would be the benchmark from the preliminary stages to maintaining the completed product.
Thoroughly plan and strategize
As with any systematic strategy, properly outlining the workload, process, and goals is critical. It should be done on the first day, to avoid inefficiency and ensure everyone is on the same page and working on the main model objectives.
Your team’s participants and structure must be chosen based on their skills and aptitude for their roles. Throughout the process, they should be well-informed and properly instructed on what they need to focus on, such as high-quality and relevant datasets.
According to recent studies, a large part of machine learning programs fail, resulting in an inadequate return on investment or disappointing outcomes. The majority of research on machine learning focuses on technical factors as opposed to project management issues. Over 80% of machine learning engineers believe that methods for project management would enhance the execution of their projects.
Determine the file structure
Although it is very subjective, a file’s structure is an arrangement or organization of data within the file. Still, there has to be a common theme behind the structure for a more effortless experience in terms of navigation for the whole team. This is vital, as efficiency is directly tied to your file’s structure.
Codebase labeling is also important because it contains the source code for a specific part of an application or software. A systematized ML codebase improves data processing. Depending on the programming platform you use, the processes may differ.
Benefits of organizing your machine learning projects
There are three significant advantages to organizing your machine learning project: (2)
You won’t waste time looking for files, datasets, codes, and models if your project is well-organized and everything is in the same directory.
Many data science projects will feel repetitive. If your data is organized correctly, you can easily reuse the same script later.
This is to ensure that other data scientists can easily reference your project on Github or similar platforms.
To ensure a smooth project, it’s essential to put together a team of capable experts. The team needs to have the right skills, experience, and mindset to work well together. When everyone is on the same page, it’s easier to collaborate toward a common goal. Information should be exchanged in ways that can increase productivity and, ultimately, achieve a successful project.
That said, you shouldn’t forget that even after accomplishing the project, there’s still a need to keep an eye on the results to see if they’re in line with the performance metrics set for the model. Performance metrics that were used for model evaluation can also serve as valuable feedback source.
Remember that as long as you take note of the tips mentioned above, you may be able to reduce the time and money necessary to carry out a successful machine-learning project.
- “Training Data: The Overlooked Problem Of Modern AI”, Source: https://www.forbes.com/sites/forbestechcouncil/2022/06/27/training-data-the-overlooked-problem-of-modern-ai/?sh=1a4c88bb218b
- “Benefits of machine learning for your business”, Source: https://medium.com/hackernoon/benefits-of-machine-learning-for-your-business-624c7297a3af