What is an ML Model?
A machine learning model is an intelligent file that has been conditioned with an algorithm to learn specific patterns in datasets and give insights and predictions from those patterns. When creating an ML model, you define the answer that you would like to capture and set parameters for the model to work within and learn from.
Once an ML model begins working with new data, you can gain actionable insights. They are also used for broad ranges of data with no known target—with the ability to utilize a pattern, they can address randomized data and still pull insights from it.
How does an ML model work?
ML models work to find solutions, patterns, and insights within all types of data. When an ML model is enabled through a given algorithm, it can begin effectively learning the dataset and discovering insights. The more insights that are gained, the more the model can utilize the knowledge to discover in a quicker, more efficient manner.
Essentially, the method by which ML models work and learn is through the lens of human experience. While computers do not have the innate ability to reason and learn through experience, the algorithms that empower ML models function to simulate as close to that experience as possible. With the parameters and presets of algorithms, ML models can replicate experiential learning. This empowers a deep level of analysis and predictions that would not otherwise be possible.
The algorithm that ML models use to learn has been created with training data for the model to learn from. This enables compounded experiences within the dataset, allowing for exponentially increasing abilities of the ML model to learn, study, gain insights, and produce resulting predictions that benefit the organization.
What are the types of ML models?
The key types of ML models operate by two methods. Both methods utilize algorithms to approach the learning process within the given data. The key difference lies in a structured versus randomized approach. The way that machine learning models work is through lived experience. In other words, this method empowers computers to evaluate data with a human experience-based approach.
The supervised learning method involves making predictions through a constant or stable variable. Algorithms in this format are able to take known data and its subsequent answers to set parameters for predictions within new datasets. This method allows for accurate predictions of new data because of the constructive parameters assembled in previously studied data.
The unsupervised learning method involves rough exploration of data to create a foundational understanding of patterns and constructs that are intrinsically buried in the data. In order to draw insights and inferences out of data with no predetermined parameters, such as in the supervised learning method, this algorithm explores datasets with the goal of discovering discrete or even hidden patterns. This method is often used across industries and a popular choice for market research and studies of genetic sequencing.
How do you build an ML model?
Building an ML model consists of various steps, and will effectively lead to deployment. The ability to build, train, deploy, and monitor ML models is possible through the following process:
- Analysis: Your organization must analyze problems and goals that you would like to draw insights from. Not all organizations have the proper foundation for ML to operate at capacity. Establishing context within your organization is critical.
- Choosing an algorithm: Exploration of your organization’s data is essential, as it will help you choose the appropriate algorithm to run within your model. By choosing the correct algorithm, you will have guaranteed insights and actionable results that can directly benefit your organization.
- Data preparation: The dataset of choice must be ready to be run through the process of the model. With the dataset prepped, you can then initiate the ML model to begin gathering insights and making predictions.
- Deployment: Now that the problem is defined, algorithm is determined, and data is cleaned, your organization is ready to deploy your custom ML model. Building an effective model with intentional objectives for problem-solving is key to the foundation of machine learning.
HPE and ML models
HPE Ezmeral Runtime Enterprise is the industry’s first enterprise-grade container platform that supports cloud-native and non-cloud-native applications on one-hundred percent open source Kubernetes. This ensures faster time-to-value for your organizations and provides configurations for intensive workloads including AI, DL, and ML. HPE Ezmeral Runtime Enterprise allows you to enjoy lower costs and less complexities by running containers on bare-metal.
Experience better performance for applications that require direct access to hardware with HPE Ezmeral Runtime Enterprise in conjunction with BlueData and MapR that provide incredible control and persistent storage within a unified data fabric.
Build your own ML model with Apache Spark on HPE Ezmeral. The HPE Ezmeral software portfolio enables complete build-to-deployment architecture for your organization. Building, training, and optimizing models is now possible with the HPE Machine Learning Development System that accelerates insight while simplifying development.
Collaborate and train ML models at enterprise scale with HPE Machine Learning Development Environment, run on our open source Determined Training Platform. This platform accelerates time-to-production and simplifies set-up, management, and security of your AI compute clusters.
And with HPE’s acquisition of Determined AI, there’s a suite of services that accelerate your artificial intelligence innovation. Determined AI provides a range of capabilities, and enables users to train their models significantly faster. This promotes data optimization of advanced hyperparameters, along with neural architecture searches. You can realize the full potential of artificial and machine learning with an incredible portfolio of HPE services.