5 tips for organizing machine learning projects

The digital age has prompted business entities to step up and automate their processes. Digitalization provides many benefits, from optimizing resources to saving operational time and costs.

Integrating artificial intelligence, particularly machine learning, is a complex and time-consuming task that needs careful planning.

Apart from high-quality and relevant datasets, you need to also choose a reliable annotation tool platform to label and interpret data used for machine learning efficiently.

A machine learning project may fail without a steady influx of data and accurate annotation, as algorithms may interpret mislabeled data inaccurately.       

Organizing a machine learning project properly is necessary to increase productivity, minimize time, and develop an efficient digital solution. Below are some tips to make your machine learning project better.    

1. Assess available data and create a strategy 

While machine learning (ML) is possible without complex algorithms, the success of a project requires having high-quality and reliable data. These factors form the pillar that all successful machine learning projects are built upon.   

At this stage, project managers must know the data they can access as well as what needs to be accessed.

Problematic and deficient data can cause delays in implementation. This may also leave the project team searching for the information they need or removing unnecessary data.             

2. Determine project viability through costs and impact analysis

Businesses, whether large or small, need to optimize resources. That’s why not all identified issues can be addressed by machine learning.

A good rule of thumb is to choose the most viable project that provides the highest returns. Ideally, it should also require little to moderate input. 

In identifying the most viable project, consider crucial factors like business impact, viability, and data availability. The best project would be one that ticks all the factors mentioned above.

A proposed activity with high impact and applicability but low data readiness will likely result in less favorable results. In addition, a project with high data availability but with less applicability may be a waste of resources.

In more concrete terms, considerations for project viability typically include expenses for data acquisition, data labeling, frequency of system use, available ML use template, resource requirements, and constraints, to name a few. 

Following a comprehensive data analysis, the team must set clear objectives and performance indicators to predict project success.

3. Resources identification and availability

Data availability may be one of the most essential requirements for a machine learning project, but it requires robust IT systems, too.

That being said, your organization must be capable of fulfilling these additional requirements:     

• Capacity: Different ML projects require varying capacities. But most enterprise AI applications will likely demand computer systems with advanced processing capacities.   
• Security: Because of the high volumes of data being handled, end-to-end encryption and other robust security systems are a must to protect data at all costs.      
• Storage: High-capacity storage may not always be necessary. But some projects, especially ones that need to process heavy files such as videos and images will require ample space. A project of this nature will work better as it can access high-quality data.   
• Network: Running a system requires fast connections and low latency levels. Make sure you have a strong network, so it’s easy to deploy, test, and scale your project.  

4. Proper documentation is vital  

Machine learning projects can’t proceed without proper data labeling and documentation.

It’s necessary to tag your resources properly, as changing your labeling system later can be challenging, if at all possible. The team must decide on the project’s file structure and codebase to ensure a seamless workflow.

Organizing file structure and codebase

File structure refers to the logical arrangement of data and objects within a file. There is no one-size-fits-all strategy to achieve this, but most data scientists label the folders based on their commonalities.

For instance, some prefer to tag different inputs such as notes and models. Apart from putting sources such as uniform resource locators and local webpages together, you can also arrange inputs based on their format extensions.

Proper codebase labeling is also essential, as it comprises the source code for a specific software application or program.

An organized ML codebase makes data processing better. Processes may differ depending on the programming platform you use.

 5. Ensure flawless communication and collaboration 

Apart from data availability, resource access is vital to the success of any business activity, machine learning project included.

Assembling a team of competent information technology professionals is key to proper implementation. So are collaboration and communication. 

The team must possess both the skills and the right attitude for teamwork. When all members are on board, it’ll be easier to work towards a single objective and proactively exchange inputs that improve workflow and, eventually, the output.

At the same time, it’s best to keep the upper management involved. It may be helpful to conduct a crash course on the basics of machine learning for business executives. Doing so provides them with the appreciation and right attitude towards the activity.  

In conclusion

Machine learning projects are often time-consuming and resource-intensive. And it’s only the initial step in adopting an advanced computing system.

Once deployed and used, organizations need to maintain, update and upgrade ML systems to keep up with the changing needs and data available. 

The points discussed above can help streamline the time and costs needed to implement a successful project.       

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