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How to Overcome the Top 4 Machine Learning Adoption Challenges?

Data scientists love the idea of machine learning and artificial intelligence, but many organizations haven’t adopted the technology just yet. The number one reason why? The top four machine learning adoption challenges: 1) Lack of understanding 2) Lack of talent 3) A complex technology stack 4) Failure to produce ROI With these challenges in mind, here are four tips to help you bring this powerful technology into your organization successfully. You can learn more by watching our on-demand webinar. 

1) Challenge #1: Lack of Data 

Data is a valuable commodity. With machine learning, you need data to build and train models. So, if you don’t have enough data, you may not be able to train a model successfully. Even if your data has some accuracy issues or other problems that make it challenging for machine learning models to work well with the data, it has lots of points (points = data) so you can still use machine learning techniques like clustering or dimensionality reduction. As long as you have enough points in your set of observations or other measurements (even if they’re not perfect), then there are ways for ML algorithms to extract useful information from them. 

2) Challenge #2: Lack of Skilled Resources 

Skilled resources can be difficult to come by, which is one of the major challenges for organizations looking to adopt machine learning. The demand for machine learning skills has outpaced the supply. For organizations that are struggling with this challenge, there are a few things they can do. First, they can train their employees using on-the-job training or put them through outside courses. Second, they can find and hire contractors who have these skill sets. Third, they can outsource tasks to specialists in places like India and China where outsourcing is more common and machine learning is becoming a bigger industry. 

3) Challenge #3: Inflexible Infrastructure 

Machine learning is an extremely valuable tool in any industry, but it also comes with its share of challenges. One of those challenges is security and privacy concerns. For example, if you’re using machine learning for marketing, your company may want to use information about a customer’s browsing habits to optimize their next advertisement. But what if the user objects and doesn’t want you tracking that data? Or worse, what if a hacker accesses your database and publishes all of your customer’s sensitive information online?  

To successfully adopt machine learning as part of your company’s strategy, you must be aware of these risks and how they could affect both your customers and business. 

4) Challenge #4: Security and Privacy Concerns 

Machine learning is a powerful technique that has been growing in popularity and use. The latest release of Python’s Keras library, for example, makes it easier than ever to build neural networks and train them on large datasets. That said, there are still some adoption challenges that people need to be aware of. Chief among these are security and privacy concerns. 

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