Artificial intelligence systems usually learn by example and are likely to learn better with high-quality examples. Low quality or insufficient training data can lead to unreliable systems that make poor decisions, reach the wrong conclusions, introduce or perpetuate bias and cannot handle real-world variation among other issues. Besides, poor data is costly. According to IBM, poor data quality in the US costs the country about 3.1 trillion dollars each year.
To build a successful training data strategy, have a well-designed strategy that will collect and structure the data you need to tune, test, and train AI systems. If you don’t have such a strategy, your projects could delay, fail to scale properly, and you may run the risk of competitors outpacing you. Here are 6 tips to help you build a successful training data strategy:
1.Create a Budget for Training Data
The first thing you need to do when launching a new machine learning project is to determine what you want to achieve. Doing so will help you know the type of data you need and how many “training items”- data points that have been categorized- you will need to train your AI system.
For instance, training items for a pattern recognition or computer vision projects would be image data. Human annotators are the ones who label the image data to identify the contents (stop signs, trees, cars, people, and so on) of the image.
Besides, your model may require continuous or refresher training. Your solution may either need weekly, monthly or quarterly updates.
After the determination of the training items and refresh rates, evaluate your options for sourcing data and create a budget. It is very important to be clear-eyed about the time and money investment needed to start the initiate, maintain it and evolve the features and functionality – along with your business- so the solution stays useful and relevant to your clients. A machine learning program is a long-term investment. A long-term strategy will help you get a great return.
2. Source Appropriate Data
The type of data that will suit your needs often depends on the kind of solution you want to build. Some of the data sourcing options include survey data, real-world usage data, synthetic data, and public datasets. For instance, a speech recognition solution that understands spoken human commands should be trained on high-quality speech data (real-world data) that has been translated into text. A search solution requires text data annotated by human judges to know the most relevant results.
The most common types of data that machine learning uses include video, image, audio, speech, and text. Training data items should be labeled or annotated before being used for machine learning. Doing this will identify what they are. Annotation tells a model what it should do with every piece of data.
For instance, if a data item for a virtual home assistant is the recording of a person saying that more double-A batteries need to be ordered, the annotation may tell the system to order with a certain online retailer once it hears the word “order” and searches on “AA batteries” when it hears “double-A batteries.”
3. Ensure Data Quality
While data annotation is a simple task depending on the task, it is difficult to do it right consistently. The task is also time-consuming and repetitive. It needs a human touch.
It is very important to know that the stakes are high as training a model on inaccurate data will make the model do the wrong thing. For instance, a computer vision system trained for autonomous vehicles with images of sidewalks mislabeled as streets can lead to disastrous results.
Indeed, poor data quality is enemy number one to machine learning. Data quality is about the consistency and accuracy of those labels. Accuracy refers to how close a label is to the truth while consistency refers to the degree to which several annotations on many training items agree with each other.
4. Be Aware of and Mitigate Data Biases
It is important to emphasize data quality as it allows companies to reduce bias in their AI projects, which can only be seen when an AI-based solution reaches the market. It is difficult to fix the bias when the solution reaches the market.
Bias is caused by unconscious preferences or blind spots n the training data or project team from the start of the project. Bias in AI can be uneven facial or voice recognition for different accents, genders, or ethnicities. Since AI has become popular in today’s world, it is the right time to deal with built-in bias.
Bias can be avoided at the project level by actively building diversity into the teams defining the roadmaps, goals, metrics, and algorithms. Even though hiring a diverse team of data can be a daunting task, the stakes are high. It is good to ensure that your team represents the external makeup of potential customers. Otherwise, the end product may only appeal to a subset of people and miss a mass-market opportunity or cause real-world discrimination.
5. Implement Data Security Safeguards When Necessary
Some of the data projects do not use personally identifiable information (PII) or sensitive data. Data security is very important for solutions that do leverage that type of information particularly when you are working with the PII of your customers, government or financial records, or user-generated content.
Government regulations are increasingly dictating how companies should handle customer information. It is vital to secure this confidential data as it will protect your information and your customer’s information. Being ethical and transparent about your practices and sticking to your terms of service will help you gain a competitive advantage. If you do not do so, you could increase the risk of being hit by a scandal that will negatively affect your brand.
6. Choose the Right Technology
The more nuanced or intricate your training data, the better the results. Many organizations need large volumes of high-quality training data, fast and at scale. To achieve this, they have to build a data pipeline that delivers enough volume at the speed needed to refresh their models. That is why it is important to choose the right data annotation technology.
The tool or tools you choose should handle the appropriate types of data for your initiative, manage individual annotator quality and throughput, and offer machine learning system assisted data labeling to augment human annotators’ performance.