What is Machine Learning?
Machine Learning (ML) is a sub-category of artificial intelligence, which is the process of computers leveraging neural networks to recognize patterns and improve is ability to identify these patterns. With enough fine-tuning and data, a machine-learning algorithm can predict new patterns and information.
The parts of the Machine Learning process
Neural Networks
Neural networks are a type of computational model inspired by the structure and function of the human brain. It consists of interconnected artificial neurons (also known as nodes or units) that are organized in layers. Each neuron takes inputs, performs a computation, and produces an output, which is then passed to other neurons in subsequent layers. Neural networks are designed to learn and adapt from data, making them a fundamental component of machine learning and deep learning.
In machine learning, neural networks are used to analyze and recognize patterns in data. They can be trained on labeled datasets to perform tasks such as classification, regression, or clustering. By adjusting the weights and biases of the connections between neurons, neural networks learn to generalize from the training data and make predictions or decisions on unseen data.
Deep learning is a specific subset of machine learning that utilizes deep neural networks with multiple hidden layers. Deep neural networks are capable of automatically learning hierarchical representations of data, extracting progressively more abstract features at each layer. This ability empowers deep learning models to handle complex tasks such as image and speech recognition, natural language processing, and even game playing.
Deep Learning
Deep learning is a particular branch of machine learning that takes ML’s functionality and moves beyond its capabilities.
With machine learning in general, there is some human involvement in that engineers can review an algorithm’s results and make adjustments to it based on their accuracy. Deep learning doesn't rely on this review. Instead, a deep learning algorithm uses its own neural network to check the accuracy of its results and then learn from them.
A deep learning algorithm’s neural network is a structure of algorithms that are layered to replicate the structure of the human brain. Accordingly, the neural network learns how to get better at a task over time without engineers providing it with feedback.
The two major stages of a neural network’s development are training and inference. Training is the initial stage in which the deep learning algorithm is provided with a data set and tasked with interpreting what that data set represents. Engineers then provide the neural network with feedback about the accuracy of its interpretation, and it adjusts accordingly. There may be many iterations of this process. Inference is when the neural network is deployed and can take a data set it has never seen before and make accurate predictions about what it represents.
How does machine learning work?
The process of machine learning on large datasets typically involves several steps. Here are five key steps with a focus on an enterprise use case:
1. Data Collection and Preparation: The first step is to collect relevant data for the problem at hand. This could include sources such as customer records, sales data, website logs, website events, customer feedback or any other data that might be available within the enterprise. The collected data is then preprocessed, which involves tasks like cleaning up missing or erroneous data, handling outliers, and transforming data into a suitable format for analysis.
2. Feature Engineering: Once the data is prepared, the next step is to extract meaningful features from the dataset. This often involves transforming raw data into more representative features that capture patterns and relationships. In an enterprise use case, this could include creating features like customer demographics, purchase history, seasonality, hot spots on products, customer bugs, or any other relevant attributes that could impact the problem being solved.
3. Model Selection and Training: After feature engineering, a suitable machine learning model is chosen based on the problem and the available data. There are various types of models, such as decision trees, random forests, support vector machines, or neural networks. The selected model is then trained on the preprocessed data, using techniques like supervised or unsupervised learning, depending on the nature of the problem.
4. Model Evaluation and Validation: In this step, the trained model is evaluated using validation techniques such as cross-validation or hold-out validation. The model's performance metrics, such as accuracy, precision, recall, or F1 score, are analyzed to assess its effectiveness on the given problem. It is crucial to validate the model's performance to ensure its reliability and generalizability across the enterprise's data.
5. Deployment and Monitoring: Once a satisfactory model is obtained, it is deployed into the enterprise's production environment. This involves integrating the model into the existing business processes, systems, or applications. After deployment, it is important to continuously monitor the model's performance, detect any drifts, update the model periodically as new data becomes available, and ensure that it continues to deliver accurate and valuable insights.
These steps provide a high-level overview of the machine learning process on large datasets for an enterprise use case. However, it's essential to note that each step requires careful consideration, iteration, and refinement to achieve the best results.
What are the different types of machine learning models?
Depending on the situation, machine learning algorithms function using more or less human intervention/reinforcement. The four major machine learning models are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
With supervised learning, the computer is provided with a labeled set of data that enables it to learn how to do a human task. This is the least complex model, as it attempts to replicate human learning.
With unsupervised learning, the computer is provided with unlabeled data and extracts previously unknown patterns/insights from it. There are many different ways machine learning algorithms do this, including:
- Clustering, in which the computer finds similar data points within a data set and groups them accordingly (creating “clusters”).
- Density estimation, in which the computer discovers insights by looking at how a data set is distributed.
- Anomaly detection, in which the computer identifies data points within a data set that are significantly different from the rest of the data.
- Principal component analysis (PCA), in which the computer analyzes a data set and summarizes it so that it can be used to make accurate predictions.
With semi-supervised learning, the computer is provided with a set of partially labeled data and performs its task using the labeled data to understand the parameters for interpreting the unlabeled data.
With reinforcement learning, the computer observes its environment and uses that data to identify the ideal behavior that will minimize risk and/or maximize reward. This is an iterative approach that requires some kind of reinforcement signal to help the computer better identify its best action.
How are deep learning and machine learning related?
Machine learning is the broader category of algorithms that are able to take a data set and use it to identify patterns, discover insights, and/or make predictions. Deep learning is a particular branch of machine learning that takes ML’s functionality and moves beyond its capabilities.
With machine learning in general, there is some human involvement in that engineers are able to review an algorithm’s results and make adjustments to it based on their accuracy. Deep learning doesn't rely on this review. Instead, a deep learning algorithm uses its own neural network to check the accuracy of its results and then learn from them.
A deep learning algorithm’s neural network is a structure of algorithms that are layered to replicate the structure of the human brain. Accordingly, the neural network learns how to get better at a task over time without engineers providing it with feedback.
The two major stages of a neural network’s development are training and inference. Training is the initial stage in which the deep learning algorithm is provided with a data set and tasked with interpreting what that data set represents. Engineers then provide the neural network with feedback about the accuracy of its interpretation, and it adjusts accordingly. There may be many iterations of this process. Inference is when the neural network is deployed and is able to take a data set it has never seen before and make accurate predictions about what it represents.
What are the benefits of machine learning?
Machine learning is the catalyst for a strong, flexible, and resilient enterprise. Smart organizations choose ML to generate top-to-bottom growth, employee productivity, and customer satisfaction.
Many enterprises achieve success with a few ML use cases, but that’s really just the beginning of the journey. Experimenting with ML may come first, but what needs to follow is the integration of ML models into business applications and processes so it can be scaled across the enterprise.
Machine learning use cases
Across vertical industries, ML technologies and techniques are being deployed successfully, providing organizations with tangible, real-world results.
Financial services
In financial services for example, banks are using ML predictive models that look across a massive array of interrelated measures to better understand and meet customer needs. ML predictive models are also capable of uncovering and limiting exposure to risk. Banks can identify cyber threats, track and document fraudulent customer behavior, and better predict risk for new products. Top use cases for ML in banking include fraud detection and mitigation, personal financial advisor services, and credit scoring and loan analysis.
Manufacturing
In manufacturing, companies have embraced automation and are now instrumenting both equipment and processes. They use ML modeling to reorganize and optimize production in a way that is both responsive to current demand and conscious of future change. The end result is a manufacturing process that is at once agile and resilient. The top three ML use cases identified in manufacturing include yield improvements, root cause analysis, and supply chain and inventory management.
Why do enterprises use MLOps?
Many organizations lack the skills, processes, and tools to accomplish this level of enterprise-wide integration. In order to successfully achieve ML at scale, companies should consider investing in ML Ops, which includes the process, tools, and technology that streamline and standardize each stage of the ML lifecycle, from model development to operationalization. The emerging field of ML Ops aims to deliver agility and speed to the ML lifecycle. It can be compared to what DevOps has done for the software development lifecycle.
To progress from ML experimentation to ML operationalization, enterprises need strong ML Ops processes. ML Ops not only gives an organization a competitive edge but also makes it possible for the organization to implement other machine learning use cases. This results in other benefits, including the creation of stronger talent through increased skills and a more collaborative environment, plus increased profitability, better customer experiences, and increased revenue growth.
HPE and machine learning
HPE offers machine learning to untangle complexity and create end-to-end solutions—from the core enterprise data center to the intelligent edge.
HPE Apollo Gen10 systems offer an enterprise deep learning and machine learning platform with industry-leading accelerators that deliver exceptional performance for faster intelligence.
The HPE Ezmeral software platform is designed to help enterprises accelerate digital transformation across the organization. It enables them to increase agility and efficiency, unlock insights, and deliver business innovation. The complete portfolio spans artificial intelligence, machine learning, and data analytics, as well as container orchestration and management, cost control, IT automation, AI-driven operations, and security.
The HPE Ezmeral ML Ops software solution extends the capabilities of the HPE Ezmeral Container platform to support the entire machine learning lifecycle and implement DevOps-like processes to standardize machine learning workflows.
To help enterprises move rapidly beyond ML proofs-of-concepts to production, HPE Pointnext Advisory and Professional Services provides the expertise and services needed to deliver ML projects. With experience delivering hundreds of workshops and projects across the world, HPE Pointnext experts provide the skills and expertise to accelerate project deployments from years to months to weeks.