Four common obstacles in AI/ML projects

​But the unfortunate reality is that 85% of AI and ML projects cannot be fully delivered, and only 53% of projects can go from prototype to production. Still, U.S. spending on artificial intelligence will grow to $120 billion by 2025, an increase of 20% or more, according to recent IDC spending guidance.

Therefore, it’s important to avoid five common mistakes that often cause AI and ML projects to fail.

1. Understand the resources required to train ML algorithms, especially data resources

While it sounds great to say that AI and ML are being used to revolutionize company processes, the reality is that 80% of companies Finding these items is harder than expected.

In order for these projects to be successful, a clear understanding of what is needed in terms of resources and people is needed. One of the most common mistakes is not understanding how to obtain the right training data - not only is this critical to the success of such a program, but it also requires a lot of effort and expertise to complete successfully. Most companies looking to adopt AI/ML projects do not have access to the amount or diversity of data needed to ensure high-quality, unbiased results.

However, failure to do this often creates huge obstacles to success, causing project costs to soar and project confidence to plummet.

There is no shortage of training data for companies to purchase, and many third-party data companies can provide services. The problem is that just because a company can easily buy large amounts of data cheaply doesn't mean it's high-quality training data, which is what successful AI and ML projects require. Rather than simply buying one-size-fits-all data, companies need data that is project-specific.

Therefore, in order to reduce bias, it is important to ensure that the data is representative of a broad and diverse audience. Data also needs to be accurately annotated for your algorithm, and data should always be checked for compliance with data standards, data privacy laws, and security measures.

2. Don’t expect that the development of artificial intelligence will be smooth sailing

The training of ML algorithms is not a strange process. Once training begins and the data model is better understood, changes must continue to be made to the data collected. It's not easy to know what data you actually need before the algorithm training process begins. For example, you might realize there's a problem with the training set or the way the data was collected.

Like traditional software development, artificial intelligence is essentially composed of software and requires continuous and stable investment to gradually generate benefits. And in this process, never take it lightly.

3. Always Integrate Quality Assurance (QA) Testing

Often, QA testing is considered an add-on or form of ensuring that the product works correctly, rather than being viewed as optimizing the product across all iterations essential tool. In fact, QA testing is an important part of successful AI development. Results validation should be integrated into every stage of the AI ​​development process to reduce costs, accelerate development timelines, and ensure efficient allocation of resources.

4. Schedule frequent application feedback

While it may be daunting to imagine, the reality is that AI projects are never truly finished. Even if a project exceeds accuracy and performance expectations, you still have room for improvement and improvement. Additionally, algorithms make decisions based on constantly changing things (opinions, conversations, images, etc.). For an AI experience to be successful now and in the future, it must be retrained on a rolling basis to adapt to new social circumstances, technological developments, and other changes that impact data.

In fact, companies that see the most positive impacts from AI adoption follow core and AI best practices and invest in AI more efficiently and effectively than their peers. This includes testing the performance of AI models before deployment, tracking performance to see if results improve over time, and developing good protocols to ensure data quality.

By developing a robust approach to developing AI programs, companies can avoid these common mistakes and ensure the long-term success of their AI and ML initiatives. ​

The above is the detailed content of Four common obstacles in AI/ML projects. For more information, please follow other related articles on the PHP Chinese website!