A Roundup of Our Top Training Data Tips
According to Gartner, more than 50% of organizations have an average of four Artificial Intelligence projects in production, with plans to double the number of projects they roll out each year.
With the many changes of the last year, we thought it would be a good time to reflect. We’ve spent a lot of time talking about high-quality data and its importance in training AI models, so here’s a roundup of our top training data mistakes to avoid.
Poor Data Collection Planning
Data collection is a major challenge – whether you’re a big organization or small. Average spending has increased by 13% annually for data and analytics teams. Yet cost optimization is often an afterthought, meaning projects suffer from cost cutting at advanced stages. How is any project supposed to become anything more if it was set up for failure in the first stage?
To compound the problem, teams often forget support costs associated with keeping data current, and with sorting clean vs dirty data. Instead, organizations should be prepared to scrutinize their current data collection strategy to see what other opportunities and strategies may be more efficient, more cost effective in the long term, and offer the right quality.
Lacking Enough Data
Collecting data is a key first step, but the next biggest mistake to avoid when training AI models: Not collecting enough data. A lack of data can lead to serious issues with your model and is one of the biggest hitches AI and ML projects can experience. Why? Insufficient data will prevent most projects from making it to production as models fail to scale.
If you’re light on training data, ensure you have collected all relevant data from the sources you have available. If that’s still not enough, consider acquiring data from data providers, crowdsourcing, or data pooling. Even if you have your own dataset, consider utilizing external datasets to enrich what you already have. Just remember to think about data quality when turning to third-party sources – and if you need help turning that data into high-quality data annotations, Figure Eight Federal combines the best of human and machine intelligence to create the highest quality training data for your AI and ML projects.
Dirty Data
Data scientists often find that data cleansing is the most time-consuming part of their Machine Learning project. According to this Towards Data Science article, 60% of a machine learning project consists of cleaning data, and 20% is ingesting that data. If data isn’t properly cleaned, or if it is inconsistent, there may be too much conflicting and misleading information, leading to model failure, an inability to process common scenarios, or a biased system.
How do you avoid training your AI models with dirty data? Start by removing outliers, addressing missing variables, and normalizing the data spread. From there consider reducing dimensionality, as well as deciding if oversampling or up sampling is required.
Failure to Understand Your Data
Having enough (clean) data is paramount to start the training process for AI models, but to properly understand the data you have is critical to your AI model’s success. Without spending time properly understanding a dataset, assumptions will be made, which may prevent selecting the best modeling approach that suits your data or problem.
Take time to comprehend the spread of your data. It will help identify if all possible conditions, use cases, and scenarios are correctly represented within the data. Start with the finish in mind – does the dataset you have help you get to the AI initiative you’re trying to solve for.
Not Utilizing the Right Data Tools and Partners
One of the biggest mistakes when training AI models is the assumption that you must solve all the problems on your own. Gartner recommends integrating data management and AI/ML initiatives by acquiring self-service data quality tools or partners that include features such as ML, NLP, and advanced analytics. Even the top technology companies use tools and partners to get the right, high-quality training data to build their AI models.
Consider tools and partners that can support your data needs. Factor in the shelf-life of your data, if you’ll need help sourcing, cleaning, or annotating data, and think about how to best spend both your time and budget resources.
Schedule a demo to learn more about how Figure Eight Federal can support your Machine Learning initiatives of 2021.
