How is Machine Learning used in industry?

achine learning is a subset of artificial intelligence (AI) where computers independently learn to do something they were not programmed to do. They do this by learning from experience — leveraging algorithms and discovering patterns and insights from data. This means machines don’t need to be programmed to perform tasks on a repetitive basis.

As stated in the What is machine learning? article most common types of Machine Learning Algorithms are:

  • Supervised Learning — Supervised learning occurs when an algorithm learns from example data and associated target responses that can consist of numeric values or string labels, such as classes or tags, in order to later predict the correct response when posed with new examples.
  • Unsupervised Learning — Unsupervised learning occurs when an algorithm learns from plain examples without any associated response, leaving the algorithm to determine the data patterns on its own.
  • Reinforcement Learning — Using this algorithm, the machine is trained to make specific decisions. The machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.

Machine Learning is Widely Applicable

Most industries working with big data have recognized the value of Machine Learning technology. By collecting insights from this data, organizations and companies are able to work more efficiently or gain an advantage over competitors.

Which industries use machine learning?

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1- Healthcare
Machine Learning (ML) is already lending a hand in diverse situations in healthcare. ML in healthcare helps to analyze thousands of different data points and suggest outcomes, provide timely risk scores, precise resource allocation, and has many other applications.

2- Retail
The retail field consists of supermarkets, department stores, chain stores, specialty stores, variety stores, franchise stores, mail-order houses, online merchants, and door-to-door sellers. Retail stores buy their goods from wholesalers, stock the goods, and resell them to individual consumers in small quantities.

3- Financial Services
Process automation is one of the most common applications of machine learning in finance. The technology allows to replace manual work, automate repetitive tasks, and increase productivity. As a result, machine learning enables companies to optimize costs, improve customer experiences, and scale up services.

4- Automotive
In the automotive industry, machine learning (ML) is most often associated with product innovations, such as self-driving cars, parking and lane-change assists, and smart energy systems.

5- Government Agencies
AI can be used to assist members of the public to interact with government and access government services, for example by: Answering questions using virtual assistants or chatbots (see below) Directing requests to the appropriate area within government. Filling out forms.

6- Transportation
AI has the potential to make traffic more efficient, ease traffic congestion, free driver’s time, make parking easier, and encourage car- and ridesharing. As AI helps to keep road traffic flowing, it can also reduce fuel consumption caused by vehicles idling when stationary and improve air quality and urban planning.

7- Oil & Gas
Machine Learning has become an integral part of the operations of most oil and gas companies, allowing them to gather large volumes of information in real-time and translate data sets into actionable insights. They now need to view data as an extremely valuable resource, with huge upside for companies with innovative, robust Machine Learning strategies. Saving time, reducing costs, boosting efficiencies, and improving safety are all crucial outcomes that can be realized from using Machine Learning in oil and gas operations.

Skill Areas within Machine Learning

At the time of writing this article only 28% of companies have some experience with AI or Machine Learning, and more than 40% said their enterprise IT personnel don’t have the skills required to implement and support AI and/or Machine Learning.
Below are some key skill areas that are required to work in the field of Machine Learning:

  • Probability
  • Statistics
  • Data Modeling
  • Data Science
  • Software Engineering

Generally, Machine Learning teams are comprised of Engineers, Analysts, Scientists and Managers. Let’s take a look at each of the roles and their associated responsibilities.

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  • Data Engineers — Data Engineers are responsible for building and maintaining the technical infrastructure required for modeling, predictions, and analysis. These professionals create and maintain databases, machine learning pipelines, and production processes.
  • Data Analysts — Data Analysts monitor processes, evaluate data quality, and monitor production model performance. This allows for more senior roles to focus on innovation, not maintenance.
  • Data Scientists — Data Scientists own the modeling process. In general, they take input parameters from product or other team leads in order to understand the model’s business objective. They then work to articulate requirements to the engineers and other stakeholders. Once these criteria have been defined, the process of building tests, models, and evaluating performance begins.
  • Machine Learning Engineers — With backgrounds and skills in data science, applied research, and heavy-duty coding, these professionals run the operations of a machine learning project and are responsible for managing the infrastructure and data pipelines needed to bring code to production.

However, Machine Learning’s ability to automate, anticipate, and evolve is powerful, but that doesn’t mean computers will take over the world. Machine Learning still requires human operators 👩‍💻 to provide context, to set parameters of operation, and to continue to improve the algorithms.

Rampco Machine Learning Software develop a data-driven Machine Learning (ML) model that can use past actual production input parameters and their corresponding output parameters to estimate the system output based on its inputs.

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We develop a data-driven Machine Learning model that can use actual production input parameters and their output parameters to estimate the output.

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