Trends in Technology
Businesses will find it increasingly difficult to deal with incoming data as the technology that captures, and processes large amounts of data gets more advanced. The massive amounts of data entering an organization wouldn’t be a problem if companies had the ability to extract meaningful insight from the new data as quickly as they received it. This is where machine-learning powered artificial intelligence (MLpAI) can play a defining role.
According to a 2017 study by Gartner, artificial intelligence (AI) employing machine-learning techniques could be the next disruptive game changer, not just in the technology changes, but also in the operational changes it enables.
However, it is crucial to manage data to support the processing complexity for MLpAI and its executable architecture; this is needed to accelerate automation and decision making at scale using the technology. Machine learning (ML) provides computationally and data-intensive processing and architecture to power artificial intelligence (AI).
To build an effective data strategy to support MLpAI, it is important to understand the role machine learning plays in data management and analytics. The best way to gain this understanding is by studying machine learning, its fundamentals, and sub-disciplines.
The Three Main Sub Disciplines of Machine Learning
A data analysis technology, machine learning extracts insights from data without being explicitly programmed to do so. The machine learning system is ingested with data from various sources including networks, sensors, applications, devices, and applications.With the help of algorithms, ML uses this data to arrive at a logical solution to a problem and derive some insight. Following are three main sub disciplines that machine learning can be categorized into.
1. Supervised Learning
Supervised learning is observations with input or output pairs. Using these sample pairs, the machine learning system is trained to identify specific rules for tallying inputs to outputs. An example of this would be using an ML system to identify a shape based on a series of shapes in images.
2. Unsupervised Learning
The second main sub-discipline of machine learning is unsupervised learning. In unsupervised learning, labels are omitted, and the machine learning system finds data structures and patterns by itself rather than being ‘trained’ for it using sample data. An example of this would be an ML system using input data to identify patterns in attributes to predict or categorize an object.
3. Reinforcement Learning
The third and last of the main sub-disciplines of machine learning is reinforcement learning. In reinforcement learning, a particular situation is analyzed to determine how good or bad it is. An example of this is an ML system enabling computers to learn to drive automobile or play games.
While the field has evolved from artificial intelligence, machine leaning is focused more on cognitive learning. In simpler terms, machine learning can be defined as the AI technology specific to the use of data to trigger human learning.
Machine learning is particularly appealing to businesses due to the fact that it does not have to be programmed explicitly in advance to gain intelligent insight. Instead, ML provides insight by using learning algorithms that trigger some capabilities of human learning.
These are the three main sub-disciplines that everyone at your business needs to understand thoroughly before you can start to prepare your IT team for machine learning. Once you’ve ensured this, you can start to prepare IT for machine learning. The key steps involved in this are discussed next.
How to Prepare IT for Machine Learning
There is a world of difference between traditional software engineering approaches and machine learning (ML) technologies. Compared to the former, ML is more probing; lots of trial and error is needed. Additionally, it is being applied in complex business problems with no exact solutions. Not only that, debugging and testing of ML systems is also quite different.
Enabling ML for enterprises is often the responsibility of a company’s IT department or team. So, how you prepare IT for ML implementation. The process typically involves several key steps including understanding the ML process, building the ML architecture, and acquiring the necessary skills. These steps are explained in detail below.
1. Understanding the ML Process
The key to preparing enterprises for machine learning is understanding how the ML process works. It is the ML process that determines the required architecture and personnel. The ML process typically involves the following three stages:
Identifying the Problem
This is generally how the ML process starts. Identifying the problem makes the ML objective clear for the enterprises and their IT departments.
Gathering and Processing Data
Once the problem has been identified, the next step for IT departments is to point out the data sources (CRM, ERP, IoT devices, legacy systems etc.) from where information can be acquired to solve the underlying problem. Once they have identified the data sources, IT departments must process and normalize data so they can be utilized for ML execution.
Development and Deployment
IT departments or teams must create an ML model after they’ve gathered and normalized the data required for ML execution. This can be done by developing algorithms used by ML programs to learn and solve the identified problems. Once developed, the algorithms are run by the IT teams; additionally, analysis and validation of the results of every cycle is carried out. Once the desired outcomes are consistently achieved by the ML, it is ready to be deployed.
2. Building the ML Architecture
Following are the key functions for which IT departments need to build architecture so they can execute the ML process.
Data Gathering–The components of ML architecture should allow it to gather data from various sources including enterprise systems, mainframes, databases, and even IoT devices.
Data Ingestion—The architecture of ML should be able to prepare the data ingested for integration and ultimately for ML execution. For this purpose, tool capable of supporting self-service integration of data are ideal.
Data Modeling—The ML architecture should make it possible for organizations to choose algorithms and adapt them to address the problems that need solving.
Execution—ML architecture components should be able to execute ML routines once data and modeling ML algorithms have been prepared for solving the pre-identified problems.
Deployment—Lastly, the ML architecture should enable organizations to utilize the outcomes of the ML process in their own data sources, applications, and enterprise systems.
3. Acquire the Necessary Skills
The last requirement to prepare IT for machine learning is acquiring the skills required to execute it. Following are two skills necessary for ML execution:
Data Engineering—data engineering skills are necessary to maintain data’s quality and integrity throughout the entire process of acquisition, transformation, execution, and deployment.
Data Science—data science is required for integration and modeling stages of the ML process. For many organizations, the implementation of machine learning is a complex and tedious task. However, they can make things simple for themselves and their IT teams by keeping the above key steps in mind. Keeping following us for more news and information on machine learning.
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