Machine Learning – why isn’t it everywhere?

  

“If we have data, let’s look at data. If all we have are opinions, 

let’s go with mine.”

Jim Barksdale

 

Let start with a basic, ML is not AI. Yes, Machine Learning (ML) is a type of artificial intelligence (AI) that involves the use of algorithms and statistical models to enable computers to improve their performance on a particular task through experience.

 

In ML, a computer is fed a large dataset and uses statistical analysis to identify patterns and relationships within the data. The computer can then use this knowledge to make predictions or decisions without being explicitly programmed to do so. If this is the case – then the question may well be, well why isn’t it everywhere?

 

ML poses many challenges to it being conducted and implemented within businesses, some of these challenges are:

• Data Quality and Quantity: ML algorithms require large amounts of high-quality data to learn effectively. This can be a challenge because it is often difficult to obtain large amounts of clean, accurate, and relevant data for the question we are trying to answer.
• Overfitting: Overfitting occurs when a ML model is trained too well on the training data and does not generalize well to new, unseen data. This can be a problem because the model will perform poorly when deployed in the real world or to changes that happen over time.
• Feature Engineering: Is the process of selecting and creating the input features that will be used to train a ML model. The key to this is domain knowledge and expertise, which can be time-consuming and difficult to convey. 
• Hyperparameter Tuning: ML algorithms have several hyperparameters that control their behaviour and performance. Finding the best values for these hyperparameters can be a challenge because it requires experimentation and evaluation.
• Bias and Fairness: ML algorithms can sometimes perpetuate or amplify societal biases that are present in the training data. 
• Explainability: Many ML models are considered "black boxes" because it is difficult to understand how they arrived at a particular prediction. This lack of explainability can make it difficult to trust and deploy ML systems in certain contexts.

 

Based on the above there are several reasons why companies may not adopt and use ML on a regular basis:

• Lack of resources: Implementing ML can require a significant investment in terms of time, money, and personnel. 
• Lack of expertise: ML requires specialized knowledge and skills, which may not be present within a company. 
• Complexity: ML projects can be complex and require a significant amount of infrastructure and technical expertise to set up and maintain. 
• Concerns about bias and fairness: ML algorithms can sometimes perpetuate or amplify societal biases that are present in the training data. 
• Legal and regulatory issues: There may be legal or regulatory hurdles that a company must navigate to implement machine learning. For example, there may be concerns about data privacy or the ethical use of ML.
• Lack of clear ROI: In some cases, it may be difficult to quantify the potential benefits of a machine learning project, which can make it difficult for a company to justify the investment.

Over the coming blogs, we’ll investigate these areas of concerns and how we can address some of these within the Oracle ecosystem. Looking at the how to’s to solve these complexities and how you can become a ML superhero within Oracle technology. 

 

Oracle has several tools and features that support ML, including Oracle Machine Learning (OML), Oracle Cloud Infrastructure (OCI) Data Science, and Oracle Autonomous Database (ADW).

 

OML is a suite of tools and libraries that allows users to build, train, and deploy ML models within the Oracle Database. It includes several pre-built machine learning algorithms and supports integration with popular open-source machine learning libraries such as scikit-learn and TensorFlow.

 

These can also be controlled through Oracle Analytics Cloud (OAC):

 

OCI Data Science is a cloud-based platform that provides a range of tools and services for data science and ML, including data preparation, model training, and model deployment. It also includes support for popular ML libraries and frameworks.

 

ADW is a fully managed database service that uses machine learning to optimize and manage itself, eliminating the need for manual tuning and maintenance. It includes support for in-database ML using SQL and Python.

 

As you can see we’ve got a lot of exploring and learning to do in 2023, and I’m grateful I’m able to help some people get started, and maybe help those who have started, look at it in a different way.