The machine learning framework moves beyond the traditional model of computation. Instead of arriving at a definite reproducible answer through a series of calculations, machine learning — a branch of artificial intelligence — works on a series of statistical probabilities to suggest new solutions to a problem. This work is useful for such jobs as designing new materials, medical diagnosis, advanced game graphics and so many other tasks.
There are four types of algorithms and several techniques used in machine learning. These types — supervised, unsupervised, semi-supervised, and reinforcement learning — and their respective techniques are examined below:
Much of the early success in machine learning has come from supervised learning, where a clearly defined data set is already available for analysis. The algorithm then determines how to arrive at given outputs and inputs. The machine learning algorithm makes predictions by analyzing the data and is corrected by the operator until its predictions are highly accurate.
In supervised machine learning, the algorithm learns through these three techniques:
Gathering recent momentum has been the Deep Learning technique, which doesn’t require all the intermediate steps that supervised learning does. Instead, the idea is to let the Deep Learning neural nets find the answers on their own.
Machine learning algorithms are provided with labeled and unlabeled data. Labeled data provides information about a given data set that helps the algorithm understand what the data is about. Unlabeled data has no descriptions. Through semi-supervised learning, machine learning algorithms learn to label unlabeled data.
In an unsupervised learning process, the machine learning program is left to observe and analyze large data sets by determining the relationship between variables. There is no operator to make corrections and the algorithm solely addresses data accordingly. As it assesses more data sets, its deductions improve and become refined.
In unsupervised machine learning, the algorithm learns through these techniques:
In reinforcement machine learning, algorithms are given specific actions, parameters, and results. The algorithm determines the rules and develops different processes for arriving at the given conclusion. The machine learning algorithm arrives at the result through trial and error. Through reinforcement, algorithms learn based on past experiences and adopt the optimal approach determined in new situations.
Artificial intelligence technologies are improving outputs in machine learning. Through technology, operators can develop automated machine learning algorithms and achieve more in less time. Some new technologies train machine learning models and others such as Google’s Auto ML Zero — a new kind of automated machine learning — use simple methods to create algorithms that can be adapted to a task at hand.
Machine learning has many potential applications and is growing rapidly. The exponential growth of data contributes to the expansion and use of machine learning. Not only can machine learning be applied to client-facing applications like product recommendation, customer service, and forecasts, but it can also be used by internal teams to speed up processes and time-consuming tasks.
Here are some machine learning use cases today:
At The New Stack, we have focused our coverage of this emerging field mostly around two areas of scalable architecture. We are keeping a close eye on the emerging field of AIOps, where machine learning can influence and drive IT operations. AIOps help to automate the path from development to production, predicting the effect of deployment on production and automatically responding to changes in how the production environment is performing. Companies such as New Relic, OpsRamp, and Moogsoft have all invested heavily in this area.
Another area of machine learning we are covering closely is how Kubernetes and related cloud native technologies can expedite the machine learning lifecycle. Machine learning involves an entire IT cycle of technologies that are very early on in terms of commercialization. Data must be harvested and cleansed, models must be tested, and the most useful approaches must be pressed into production, with a feedback loop of some sort to ensure the models can be updated. Emerging workflows such as Kubeflow and Anaconda can help streamline these processes.
Read our articles in this category to learn more about machine learning.