We’ll look at what problem AI solves for their company, specifically the problems highlighted in our AI Software of Choice 2022 report.

The type of machine learning algorithms was highlighted as the most important factor to consider when choosing an AI platform, having 40.4% of votes. Workload reduction at 26.8% and machine learning times at 22.4% were indicated as the second and third most important factors, respectively.

We will now take a better look at what’s most important to AI experts when choosing an AI platform:

What’s more important to you when choosing an AI platform?

1. Type of machine learning algorithms

Highlighted as the most important factor in choosing an AI platform, there are four types of machine learning algorithms to choose from.

a. Supervised learning

Machines learn by example, with operators giving the algorithm a known dataset with desired inputs and outputs. The algorithm will then identify patterns in the data, learn from observations, and make predictions. The operator will correct these predictions until the algorithm has a very high level of performance and accuracy. Supervised learning includes:

  • Classification. The machine learning program draws conclusions from observed values and determines what categories new observations belong to.
  • Regression. The machine learning program estimates and understands relationships among variables, focusing on one dependent variable and various other changing variables for prediction and forecasting.
  • Forecasting. Typically used to analyze trends, forecasting makes predictions about the future based on historical data.

b. Semi-supervised learning

Similar to supervised learning, semi-supervised learning uses labeled and unlabeled data. By combining data with meaningful information and data that lacks information, machine learning algorithms can then learn how to label unlabelled data.

c. Unsupervised learning

The algorithm studies data to identify patterns, determining correlations and relationships through available data analysis. The algorithm interprets large datasets and addresses data accordingly, trying to organize the data to describe its structure. Unsupervised learning includes:

  • Clustering. The grouping of similar datasets, based on defined criteria, is often useful for data segmentation into different groups and analysis to find patterns.
  • Dimension reduction. Reduces the variable number that is being considered in order to find the exact required information.

d. Reinforcement learning

Algorithms are provided a set of actions, end values, and parameters, which teaches the machine by trial and error. By learning from past experiences, it can adapt its approach according to the situation.

2. Workload reduction

AI platforms should enhance human employees’ work so that it’s smarter and more efficient. This includes workload reduction so that human employees can focus on other aspects of operations. To do this, you need to provide human-readable output from machine learning algorithms, which also includes guidance on the meaning of detection and what steps human employees need to take to verify and respond.

3. Machine learning times

Knowing the amount of time it takes for algorithms to trigger detections in new environments, how many algorithms need a learning period, and how long that would take is vital when choosing an AI platform.

If machine learning algorithms need an extended period of time for learning, unsupervised learning is the only option, which is limited because it just detects anomalies and produces higher alert volumes than need manual triage.

4. Machine learning algorithms volume

Machine learning algorithms are a combination of both logic and math that automatically adjusts to perform more progressively when there’s a variation in the input data. Python, for example, is a general-purpose and easy-to-learn and understand language, which makes it ideal for use in a wide variety of development tasks.

Python can do a number of machine learning tasks, which means that, when investing in this platform, there’s a chance you won’t need several tools and can save on costs. Taking Python as an example, it has the machine learning algorithms such as:

  • Linear regression
  • Logistic regression
  • Decision tree
  • Support Vector Machines (SVM)
  • Naive Bayes
  • kNN (k-Nearest Neighbors)
  • k-Means
  • Random forest

5. Time taken for integration with other systems

When looking into an AI platform, it’s vital to hit the ground running. Integration should be both straightforward and simple, with platforms providing intelligence to your existing infrastructure to reduce the time to respond. Integration with other systems can happen through APIs, automation platforms that offer product standardization, or outbound events.

It can be challenging to bridge the gap between data scientists who develop a model, for example, and developers who deploy the model. Whether you develop in-house or invest in a well-known platform, it’s important to consider how it works with your other tools.

Machine learning deployment can be complex, however, there are often four common steps involved:

  1. Development and creation of a model in a training environment.
  2. Testing and cleaning code so it’s ready for deployment.
  3. Container deployment preparation.
  4. Planning for continuous maintenance and monitoring after deployment.