Artificial intelligence (AI) is entering many diverse industries and business processes. The benefits of machine learning and other aspects of AI can be productively applied to maintaining, operating, and optimizing an IT environment. This post looks at some of the specific ways AI and machine learning can be used to improve your operational efficiency.
A modern and advanced infrastructure is a prerequisite for taking advantage of the power of AI. The cloud offers a streamlined method of incorporating AI processes into your IT environment. VAST can help your business find the right cloud platform to effectively leverage the power of machine learning and AI.
What is Machine Learning?
Machine learning (ML) is a subset of the field of artificial intelligence. ML concentrates on developing algorithms and statistical models that allow computers to perform complex tasks without explicit programming. Machine learning systems learn from exposure to data rather than a predefined set of rules.
The algorithms are designed to identify patterns in datasets with which they can make informed decisions. ML algorithms can improve their performance over time by repeatedly examining their results and taking corrective action. Typically, an ML model will continuously improve its decision-making capabilities as it evaluates larger volumes of data.
Multiple steps are involved to train machine learning algorithms. Some steps may be eliminated based on the training method selected for the ML model. For example, minimal preprocessing may be required when using unsupervised learning to train an ML algorithm.
Data collection and preprocessing
The first step in training ML models is to collect a large volume of data relevant to the problem it is intended to solve. The data can be structured or unstructured based on the training methodology utilized to teach the algorithm. In some cases, preprocessing is performed to clean the data and better prepare it for use in training.
Datasets may be split to address multiple uses. Some data can be used to train the model. Additional data may be employed to tune the algorithm’s parameters and evaluate its performance against defined baselines.
Selecting and training a model
Select an appropriate machine learning algorithm based on the problem to be solved and the type of data available. For instance, the model may be intended to perform classification or regression to address business objectives. Once a model is selected, training can begin using one of the three methods we will describe shortly.
Tuning and evaluation
During training, the model’s performance should be continuously evaluated for precision, accuracy, and other metrics relevant to its objectives. Testing may be a lengthy process to correctly tune the algorithm until it meets expectations and can be deployed into the production environment.
Deployment and maintenance
The model is deployed into the IT environment after ensuring its satisfactory performance. Monitoring should be conducted to verify the model’s effectiveness. Additional updates to tune the algorithm may be necessary to achieve the desired results. A model may need to be retrained to address evolving business conditions or new data sources.
Training Methods for Machine Learning Models
Machine learning algorithms and models can be trained using several different methods. The learning approach adopted by an organization is necessarily influenced by the type and intended use of the deployed ML algorithm.
Supervised learning
Supervised learning trains ML models using labeled data that pairs input with the correct output. The algorithm learns to map specific types of inputs to viable outputs. Supervised learning is used as the training method for many ML applications.
- Classification – ML algorithms can be deployed to perform classification activities such as identifying spam emails and recognizing specific objects in images.
- Regression – Regression problems such as forecasting future stock prices can be addressed with supervised learning.
- Credit scoring – ML algorithms can evaluate the probability of applicants defaulting on loans based on their financial history.
- Medical diagnosis – Patient data including medical history, current symptoms, and test results can be used to predict diseases accurately.
Unsupervised learning
This type of ML learning is done with unlabeled data. The algorithm independently identifies patterns in the data without being informed about its search objectives. Unsupervised learning is the appropriate training method for the following implementations of ML models.
- Clustering – Activities such as customer and image segmentation based on shared characteristics have many business applications.
- Anomaly detection – Detecting anomalies and unusual patterns in systems such as networks and customer transactions can prevent fraud and enhance security.
- Topic discovery – An ML model can discover related items in groups of documents without being provided with predefined categories utilizing unsupervised learning.
Reinforcement learning
Reinforcement learning involves a model receiving feedback as it interacts with an environment. Penalties and rewards are used to improve the model’s performance over time as in the following examples.
- Game playing – ML algorithms are used to create computer opponents to play complex games such as chess and Go.
- Autonomous vehicles – Reinforcement is employed when training self-driving cars to make navigation decisions.
- Resource management – Optimizing resource allocation and utilization can be accomplished with ML systems using reinforcement learning to understand how to address evolving conditions.
- Personalization – Reinforcement can be used to personalize the user experience by making suggestions based on previous interactions.
ML Models in an IT Environment
Machine learning can improve operational efficiency in an IT environment in multiple ways. The following are some of the applications of ML in an IT setting.
- Predictive analytics – System failures can be predicted by analyzing historical data so proactive measures can be taken before issues occur. Usage patterns can be used to optimize capacity planning and reduce waste.
- Anomaly detection – ML models can perform real-time monitoring and analyze logs to identify anomalies that may indicate potential security breaches. Teams can take the appropriate actions to minimize threats to the environment.
- Performance optimization – Network and application performance can be optimized by deploying ML models. The models can analyze traffic patterns and identify optimization techniques.
- Automating processes – ML algorithms can be deployed to resolve issues automatically by executing predefined actions to reduce downtime without manual intervention. Virtual assistants and chatbots can automate routine support tasks for internal and external customers.
How VAST Can Help You Take Advantage of Machine Learning
Taking advantage of the cloud is the most efficient way for most companies to access the computing power necessary for effective machine learning. VAST’s cloud experts are partners with the major providers that have portfolios of ML applications designed to address a range of business requirements. These providers also have the necessary infrastructure available for companies looking to develop ML models and algorithms from scratch.
We’ll work with you to identify the cloud platform that best addresses aligning machine learning models with your business objectives. Get in touch with us today and get started utilizing the power of machine learning to improve operational efficiency and grow your business.
