Top challenges faced in machine learning consulting

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Machine learning consulting is a very useful service if you want to enter this niche. The fact is that a good machine learning consulting company will allow you to avoid costly mistakes and wasted time at the start. Moreover, even if you have already taken the first steps in this industry, mentoring more experienced colleagues will allow your business to grow faster.

A bit of theory

Machine learning is a highly specialized area of ​​knowledge that is part of the main sources of technologies and methods used in the fields of big data and the Internet of Things, which studies and develops algorithms for the automated extraction of knowledge from a raw dataset, training software systems based on the received data, generating predictive and / or prescriptive recommendations, pattern recognition, etc. What are the problems with machine learning? In this article, we are going to tell you about the most popular issues.

Image processing and computer vision

Applandeo engineers research real-time and offline imaging and video processing technologies from vulnerability scanners and sports trackers to intelligent object monitoring. A trusting approach inspires ML engineers to develop intelligent ML solutions to automate complex image and video-based decision-making processes. More often than others, business owners order the development and improvement of technology, and the design of the system:

  • Face recognition;
  • Detection and extraction of objects;
  • Tracking objects;
  • Replacement of faces / face swap;
  • Biometric identification.

Natural language processing

ML engineers conduct innovative research and apply innovative methods to create software for the recognition and processing of unstructured written and spoken language based on ML models and algorithms. Laboratory projects are aimed at finding solutions for:

  • Optimization of search queries;
  • Referral services;
  • Forecasting sales volumes;
  • Forecasting customer churn;
  • Checking the source code for vulnerabilities;
  • Converting text to speech;
  • Convert speech to text;
  • Custom search engines.

Machine learning in industries

The following niches are automating more and more every year. However, in each of these niches there are certain issues that need to be somehow circumvented. Let’s show with examples.

Healthcare:

  • Individual online consultation and treatment;
  • The use of robotics in the training of medical professionals;
  • Ease of storage of personal data;
  • Effective prevention and diagnosis of diseases;
  • Integration of ML into the process of developing new drugs and their production.

The main problem in this case was the lack of human empathy in computer mechanisms. Simply put, artificial intelligence doesn’t know how to empathize. Currently, only real doctors make decisions about treatment, and machine algorithms are used only for the purpose of collecting statistics and forecasting.

Education:

  • Optimization of scheduling classes;
  • Simplification of the educational process;
  • Virtual training;
  • Personalization of the educational process;
  • Creation of smart content.

Planning has already been successfully automated in this area. Currently, active development is underway to improve virtual learning (for example, 3D games). But so far there have not been any major project launches.

Marketing:

  • Deep understanding of customer needs;
  • Content creation based on analytics;
  • Accurate forecasting of sales and market behavior;
  • Optimization of marketing campaigns;
  • New insights to improve product positioning;
  • Decrease in customer churn;
  • Smart-systems of point-of-sale terminals;
  • Reduced marketing costs to increase ROI.

Automating the collection of target audience data has ethical issues. Not all people want to provide information about themselves. Currently, it is allowed to collect information only after the user has filled in special forms (which they are also not very welcome).

Electronic commerce:

  • Retargeting of potential clients;
  • Integration of chat bots and virtual assistants;
  • Customization of the shopping process;
  • Filtering fake reviews;
  • Automation of sales processes;
  • Improved product search;
  • Innovative ML implementation strategy;
  • Data analysis.

This is probably one of the most automated areas. The only issue is the speed of data processing, and it increases many times every year.

How to avoid problems?

An experienced team first studies the client’s business and, using data mining techniques, provides datasets to create ML models. By classifying and analyzing sequences using programming tools, data scientists extract information from a large array of raw data and prepare quality data to compose algorithms for training ML models.

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