Technology experts predict the use of machine learning (ML) in business operations will double (opens in new tab) towards the end of 2018 as more companies recognize the true value of intelligent technology. Machine learning can solve problems independently without humans at the helm providing routine instructions. The adaptive nature of ML allows the machines to learn consistently and prevent system anomalies.
Machine learning is no longer bound to the concepts of productivity and cost reduction. ML is now gathering large sets of data to provide valuable insights to companies as they refine the customer experience and tweak their business operations to gain a competitive advantage in the market.
The complexity of ML development requires expert knowledge and laser focus. Therefore, outsourcing machine learning development is the better choice when compared to working with an in-house staff that often lacks the resources and contextual knowledge needed for accuracy in ML networks.
Evaluating what machines rely on for learning lends to a deeper understanding of the need for outsourcing when building a fully functional machine.
Consider these key points before hiring in-house employees instead of outsourcing.
Augmenting Staff Can Develop A Machine’s Neural Networks With More Efficiency
Inputting large datasets teaches machines to learn and interact like humans. From the machine’s neural networks, bots can speak in full sentences, understand emotions, and generate emotional responses. But training machines must be comprehensive and consistent. Extensive amounts of data must be fed to robots daily before they can mimic the human thought pattern. By outsourcing this process, machines can understand problems and analyze additional data without sacrificing speed and error-free processes for current and upcoming projects.
Neural networks categorize information, enable sound decision-making, and give machines the ability to make future predictions. Function approximations are used to create the algorithm system that powers neural networks. Because the approximations are general, they can be used with multiple machine learning systems to execute various tasks.
A team of outsourced developers can speed up the process of neural network development. By outsourcing the tasks related to neural networking, businesses will save time and money as the team focuses on machine learning while in-house staff concentrates on other primary objectives. Contract workers will often have their own equipment, which further saves businesses the expenses of providing additional software and on-site materials.
Outsourced Developers Know ML Accuracy is Not Always Guaranteed
Some projects require 100% accuracy, but it is a mistake when in-house staff expects machines to make accurate predictions at all times without considering the accuracy paradox. Part of the machine learning process is allowing room for error as new information is put into the system. During this time, outsourced teams will work closely with the artificial intelligence, creating the predictive algorithms that calculate the best decisions.
Among the most common issues in-house staff faces when working with artificial intelligence is the lack of time to input the large amounts of data that machines must have for learning and performing multiple tasks.
Without full and precise data sets, the machine will produce inconclusive results. Outsourcing ML development ensures that your company will work with teams who have the time to enter the vast amounts of specific data required for teaching machine learners to think with a higher level of reasoning.
Outsourcing ML Development Enhances the Customer Experience
Machine learning functions as an analyzation tool that predicts the future behavior of customers based on their past habits. Customers witness these tools daily on the front-end when using major platforms, such as Amazon and Netflix.
Intelligent machine processes use large data sets to teach software how to make suggestions and cater to individual traits. For example, dictation software identifies the speech patterns of an individual and customizes itself to respond to a person’s words by translating the spoken words into written text. Facial recognition software has also been taught to machines, helping the machine analyze unique facial features to verify user identity.
When companies hire in-house staff to develop ML processes, they must acknowledge testing as a necessary step to confirm that the machine is making sense of the data being fed into the network. Unit testing and testing in real-time as a front-end user are some of the various testing methods for ML technology. Regardless of a company’s preferred testing methods, considerable effort is put into performing the tests, which will prolong the time it takes for products to reach the market. Companies should outsource ML development to avoid the time constraints that often lead to testing errors.
Outsourcing Helps Narrow Decision Trees
Classification and Regression Trees (CART) are simplified algorithmic systems for machines during the decision-making process. Data scientists refer to these trees as decision trees, and they often use two primary methods of sorting the data. Trees can organize and categorize information (Classification) or predictively determine future values (Regression).
Decision trees begin upside down at the root and branches out until it reaches the final decision at the bottom. The state of the internal node is the deciding factor for the number of branches on the tree, but the tree will require trimming to lead to the most logical decisions quickly. An outsourced team can focus on limiting branch splits and test for accuracy after each alteration. Many data experts set a maximum depth for the tree, which determines where the decision process should stop.
The pruning process begins as the decision tree is near completion. Having additional staff allows additional time for both reduced-error pruning and cost complexity pruning (also known as weakest link pruning). Reduced-error pruning starts the removal process from the root and immediately measures machine accuracy after each removal. Weakest link pruning judges the size of the condition’s sub-branches and removes the nodes and branches of lesser importance. Once the process is complete, machines become more reliable because they are learning to focus on the most significant details to make the best choices.
Outsourcing ML Development Creates More Intuitive Analytics
Contracted workers can teach the machine to capture behavior analytics by analyzing user information, web page views, the duration of time spent on a website, and search queries.
Because machine algorithms can notice habitual behavior, its systems further help machines spot abnormal behavior. Once development teams receive alerts for suspicious activity, they will block the source to prevent fraudulent behavior – or the machine can be taught to block intrusive sources immediately upon discovery.
As a result, the front-end experience is more intuitive, sensitive customer data won’t be put at risk, and companies are less susceptible to hacking.
Today, organizations ranging from government agencies to entertainment companies use machine learning to drive business and predict upcoming trends. Outsourcing ML development is the best solution for assessing the scalability and data cleansing of these machines, which notes how the technology computes big data and allows machines to operate intuitively with more powerful algorithms.
Position your in-house staff to work more efficiently on primary objectives as outsourced teams teach machines. The return on investment for expert machine input goes beyond customer satisfaction because smart machines will learn to work with staff to prevent malfunctions, increase security, and foster an innovative environment for development teams.
Nacho De Marco, CEO of BairesDev (opens in new tab)
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