In theory most of the challenges we face today can be solved with ML. There has been amazing progress recently with OpenAI, but Machine learning is sill very young. In many fields the mindset of if it ain’t broke don’t fix it is persistent. A perfectly reasonable approach if the job is getting done with non ML methods. But progress is inevitable, to harness the powerful problem solving capacity of ML, we need to overcome the following roadblocks:

  1. Data Availability: Machine learning algorithms need large amounts of data to be effective, but it may not be available due to privacy concerns or cost.

  2. Data Quality: Machine learning algorithms require high-quality data to be successful, but this is often difficult to achieve due to noise, errors and other issues in the data.

  3. Lack of Expertise: Applying machine learning in the real world requires a wide range of skills and experience that are often not available or easily acquired.

  4. Computational Resources: Running machine learning algorithms can require a lot of computing power and specialized hardware, which can be expensive and difficult to access.

  5. Interpretability: Machine learning models often produce predictions that are difficult for humans to understand and explain, making them difficult to trust and use in decision making.

This is not an exhaustive list but it covers the bulk of the potential roadblocks faced in developing a ML project for the real world

Let’s explore a few of the roadblocks in the health industry:

  1. Data Privacy and Security: The health industry involves sensitive patient data which needs to be protected from misuse or unauthorised access. Machine learning algorithms require large datasets for training, so securing this data is a challenge.

  2. Data Collection: Gathering the right data for training is a significant challenge for machine learning in health care. Different medical sources need to be integrated, such as electronic health records, clinical trial results, and lab tests.

  3. Model Interpretability: Machine learning models can be complex and difficult to interpret, making it difficult for medical professionals to trust the predictions made by the model. This can lead to errors if the model is not understood properly or if incorrect assumptions are made about how it works.

  4. Regulatory Compliance: The health industry is highly regulated and any machine learning technology used must adhere to the relevant regulations or face potential sanctions or penalties.

When developing a ML project for real world deployment, considering these roadblocks from the start will give you the best chance of building a robust product.