Nearshoring for machine learning software
Nearshoring involves sending work for the development of information technology to a foreign country that is relatively close to the base of the company. The outsourcing of the region is the practice of doing jobs or services performed by people in neighboring countries and not in their own country.
Many companies in Spain, for example, outsource work to neighboring countries. Geographic proximity means that travel and communications are easier and less expensive, there is likely to be at least some cultural resemblance, and people are more likely to speak the same language.
Machine learning is a type of artificial intelligence that gives computers the ability to learn, without being programmed explicitly. Automatic learning focuses on the development of software that can change when it was exposed to a new data.
Nearshoring in the development of automatic learning can certainly be of benefit to businesses as long as it is used wisely. It can enable them to take on more work, assigning their internal team important projects while getting a cheaper deal on more tedious tasks.
Koukio: Auto-learning engineers
As an Automatic Learning Engineer, Koukio will devise, design, implement and optimize statistical models to drive business decisions for customer relationship management, marketing campaigns and virtual vendors.
It will collect business problems and data from various stakeholders in Sales, Marketing, IT and Operations and will offer solutions based on automated learning.
Koukio’s auto-learning engineers build products / solutions, which provide descriptive, diagnostic, predictive or descriptive models based on data.
It uses or develops automatic learning algorithms, such as supervised and unsupervised learning, deep learning, learning reinforcement, Bayesian analysis and others, to solve problems applied in various disciplines such as data analytics, computer vision, robotics, etc.
Interact with users to define the requirements of innovative products / solutions. In both research environments and product-specific environments, it uses current programming methodologies to translate machine learning models and data processing methods into software.
Complete the programming, testing, debugging, documentation and / or deployment of the solution / products. Engineers large frameworks for data computation, data modeling and other relevant software tools.
Learning algorithms for industries
With the development of open source, free-learning artificial intelligence tools such as Google TensorFlow and sci-kit learn, as well as “ML-as-a-service” products like Google Cloud Prediction API and Microsoft Azure, Easier for industries of all sizes to harness the power of data.
Often times the current landscape of automatic learning algorithms and their functioning, provide examples of applications, uses and provide more resources to learn about them. Your summaries provide the first step in learning to apply automatic learning algorithms to make your business more efficient, more effective, and more profitable.
The purpose of classification algorithms is to place the elements into specific categories and answer questions such as: Is this tumor cancerous? Is this email spam? Will this loan applicant default? What category of article is this? What demographic population does this online customer belong to?
Bayesian classifiers are a simple but highly effective classification algorithm. They are based on Bayes’ theorem, which succinctly defines the probability of an event given that another related event has occurred.
A Bayesian classifier categorizes data by keeping track of the probabilities that the specific characteristics are characteristics of a set of data that you think might impact your classification.
Although Bayesian classifiers can be used for any classification task, they are particularly useful with document classification, especially spam filters.
For example, computer scientist and famed startup investor Paul Graham developed a simple Bayesian spam filter that captured more than 99.5 percent of its spam without having false positives (e-mails that are not spam mistakenly labeled as spam).