Machine Learning Jobs

Find jobs in Machine Learning

In this article, we will tell you what a job in machine learning is and what artificial intelligence and machine learning engineers are needed for.

How does machine learning work?

When we make an appointment with a doctor via a chatbot or ask Alice to play rock music, we hardly think about how this happens: the actions seem very simple. In fact, behind each of them there is a complex process that includes elements of machine learning.

ML is a class of methods in which a machine, an algorithm, is taught to think and act like a human based on experience or data. Read, write, draw, distinguish rap from rock, and potatoes from carrots.

Machine learning is not the same as programming. The programmer creates an algorithm for the machine: he prescribes a clear sequence of actions that will lead to the desired result.

The machine learning engineer who trains the model does not write a program for the machine. He transmits the data and tries to explain what he wants to get as a result. The algorithm does not have a given answer to arrive at, it only knows how to build a model that answers the question posed. The goal of machine learning is to teach the model to find a solution itself.

Where is machine learning used?

Almost everywhere in the world, both in contract jobs, and in remote jobs. The ability of artificial intelligence to remember information, find and analyze and predict data is used in marketing, finance, medicine, demography and security.

Here are some examples of how and where machine learning models are used:

·      Banks. Bank scoring programs solve the problem of processing a huge number of credit questionnaires. Specialists create a model that automatically calculates a credit rating, assesses the client’s solvency and determines whether to approve or deny a loan.

·      Marketing. When Alice suggests a personalized playlist, this is a classic application of machine learning in a recommendation task. Another example is stores without cash registers and sales assistants, in which, through machine learning, algorithms learn to match a customer with his virtual basket and track the movement of goods on the shelves.

·      Medicine. One of the most high-profile examples is the fundamental discovery made by the AlphaFold algorithm in 2020. He modeled the process of protein folding, solving one of the most difficult biochemical problems of the century. Thanks to the model, scientists were able to prevent the development of infections, cognitive and neurodegenerative diseases - Parkinson's, Alzheimer's and others.

·      Agriculture. With the help of machine learning, models have been created that can analyze the composition of the soil compositions, calculate the required amount of fertilizers, predict crop yields, and even predict the milk yield of cows.

Artificial intelligence itself is not capable of assessing or predicting anything. In order for the model to understand that an online cinema client likes thrillers, or to calculate the amount of fertilizer per hectare of soil, it needs to be trained to work with data.

How does an ML specialist work?

Model training is divided into five stages.

Data collection

At this stage, you need to collect information that will be used to train the best model.

Data markup

If training a model involves working with labeled data, you need to do some preparatory work - highlight the areas or criteria that are needed to train the machine or give the correct answer for each case.

Exploratory Analysis

The control check stage, in which an ml engineer checks how the data is distributed, how various signs depend on each other, and whether there are any errors or atypical cases in them.

Model training

At this stage, the ai ml engineer selects suitable algorithms to solve the problem and trains several promising models.

Testing and Evaluation

The results of the training AI jobs need to be assessed and understood what to do next: collect the missing data and continue training, replace the model parameters or revise the algorithm.

Conclusion

As in any profession, in ML best companies there are also non-standard situations that do not fit into this work scheme. For example, when there are no suitable algorithms for a given task and you need to design a new one. Or create a new neural network architecture, train it and evaluate the result. Machine learning is a rapidly growing field. Computing power vacancy is growing, new tasks are emerging that require an atypical approach. This means that an ML specialist always has room for creativity and professional development!