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A machine learning engineer is a programmer proficient in building and designing software to automate predictive models. They have a deeper focus on computer science, compared to data scientists.

Machine Learning Engineering has grown in great popularity and is surpassing Data Science. The job title is high in demand with many people from Data Science careers transitioning to become Machine Learning Engineers. It is currently #6 in the top 50 Best Jobs in America, according to glassdoor.

A Machine Learning (ML) Engineer is a programmer proficient in building and designing software to automate predictive models. They have a deeper focus on computer science, in comparison to Data Scientists.

The majority of ML Engineers come from one of two backgrounds. The first is those with a Ph.D. in Data Science, Software Engineering, Computer Science, and/or Artificial Intelligence. The other is people who have prior experience as either a Data Scientist or Software Engineer who has transitioned into the role.

What Does an ML Engineer Do?

A Data Scientist and ML Engineer both work with dynamic data sets, carry out complex modelling, and have exceptional data management skills.

The main role of an ML engineer is to design software to automate predictive models which help carry out future predictions. This is how the ‘machine’ ‘learns’ from ‘engineering’.

The sub-tasks included in doing this include:

  • Researching ML algorithms and tools and how they can be implemented.
  • Selecting the appropriate data sets
  • Selecting data representation methods
  • Verifying the quality of the data
  • Identifying the distribution in the data and how it affects model performance.
  • Iterating training on ML systems and models
  • Perform statistical analysis
  • Fine-tuning the model
  • Improving existing ML frameworks and libraries

What Skills Do You Need To Be A Successful ML Engineer?

There are a variety of skills required to become an ML Engineer.

Programming Skills

You need to have knowledge in multiple programming languages such as C++, Python, and Java with other programming languages such as R and Prolog which have become important elements in Machine Learning. The more programming languages you know, the better; however that can require a lot of studying.

Statistics

Machine Learning has a heavier focus on computer science, using probability and other statistical tools to help build and validate models. Machine learning algorithms are an extension of statistical modelling procedures therefore having a good understanding of the foundations of statistics and maths is important.

Problem Solvers

There are going to be times when models fail and it can become very complicated, therefore ML Engineers need to be good problem solvers. Instead of giving up, solving the problem efficiently by understanding the issue at hand and developing these approaches to help you save time and reach your goal faster.

Understand Data

ML Engineers quickly gander through large data sets being able to identify patterns to help them understand what next steps to take to produce meaningful outcomes. Using tools such as Excel, Tableau, and Plotly can also be used to provide greater insight into the data.

How To Start Your Career as an ML Engineer

How to start your career as an ML Engineer
David Iskander via Unsplash

Traditional route: University

Desirable degrees for ML engineers include Mathematics, Data Science, Computer Science, Statistics, and Physics. These degrees provide ML Engineers with the foundations, aswell as skills in programming, statistical tools, and analysis.

If you would like to get a better insight on the type of content you will learn at University, have a read of this article: Free University Data Science Resources.

Once you have completed a degree, you will need to build your skills and experience in fields such as Software Engineering, Data Scientist, etc. ML Engineers require a few years of experience with a high level of proficiency in programming to be successful.

You can further increase your knowledge by getting a Master’s degree in Data Science, Software Engineering, and/or a Ph.D. in Machine Learning.

Modern tech route: e-Learning

With the demand for tech experts in this day and age, another possibility is independent and/or e-learning. This can be done through BootCamps, online courses, Youtube, and more.

If you are looking to learn through YouTube, there are a variety of YouTube channels that can help you get there. There are YouTubers such as John Starmer, Krish Naik, and more. If you would like to know more, have a read of this article: Top YouTube Channels for Learning Data Science.

There are also a variety of online courses, some of which are provided by Universities. This shows the demand for tech experts as Universities have taken the time to create courses to help meet this demand. With the new remote lifestyle, online courses are becoming more and more popular to help accelerate people’s careers.

An excellent platform that has recently interested me is Great Learning, which provides courses in Data Science & Business Analytics, AI & Machine Learning, Cloud Computing, Software Development, and more. One of their most popular Machine Learning courses is: Data Science and Machine Learning: Making Data-Driven Decisions Program.

ML Engineers have to know a lot of knowledge surrounding Machine Learning, and the different types of algorithms. If you would like to know more about the type of algorithms you will learn in Machine Learning, have a read of this article: Popular Machine Learning Algorithms.

Books

Although many things have moved online, fewer and fewer people read books. Books are a great way to learn, however, it can be difficult to know which book to choose. I would highly recommend the book Machine Learning for Absolute Beginners by Oliver Theobald.

If you would like more Machine Learning book recommendations for different levels of learning; beginners, intermediate, and experts, have a read of this article: Machine Learning Books You Need To Read In 2022

It’s Not An Easy Route, But It’s Worth It

Becoming an ML Engineer won’t happen overnight, but once you have obtained the correct qualifications, skills, and experience, you will be in a field that provides you with a solid future. It requires a lot of hard work and determination, all you need to do is put in the work.

Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.

Feature Image Credit: rawpixel

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Sourced from KDnuggets

By Julius Cerniauskas

The law of success in business states: the quicker you accept and welcome innovations, the further behind you leave your competition. That’s the reason why technical enhancements seem to be as frequent as coffee breaks. Here is why data scientists are becoming future marketers.

As big data is changing the status quo, the marketing sector is not lagging behind.

On the contrary, data scientists have shown that big data adapts data-driven methods to make smarter decisions.

Not to mention, the shift that is being caused by increasing accessibility to unprecedented amounts of publicly available information, which spurs data scientists to pick up the role of marketers.

Not surprisingly, in the face of the information revolution, marketers say that data is their company’s most under-utilized asset. The biggest challenge to data-driven marketing success is the lack of data quality and completeness.

Here is the bridge the knowledge gap between modern marketers and data scientists.

What type of data is actually useful and how do you use it? My goal is to make sure that after reading this article — you will be able to:

  • Understand why the ability to use big data in the marketing sector is essential for business survival.
  • Get a good grip on how to obtain the “right” high-quality information to craft an effective marketing strategy.
  • Learn 4 ways how company giants are using data gathering right now to stay in leadership positions.

Statistics predict the future of marketing

The value of using data science and analytics in marketing has been increasingly recognized. Currently, about 3 out of every 4 marketing leaders (76%) base decisions on data analytics. Globally, the budgets for data-driven marketing has also been increasing rapidly.

In 2019 reaching its highest point in six years. The overwhelming majority (73%) of marketing associations agree that this trend is expected to continue in the future.

This trend has been echoed by executives and CEOs worldwide. They confirm that data-driven marketing is crucial to success in a hyper-competitive global economy, as Forbes Insights and Turn report reveal.

Lessons from the best of the best

What do Disney, Apple, Uber, and Amazon have in common?

Apart from all being at the top of their game, these companies place customer experience as their top priority. Equally, findings from global market studies worldwide show that consumers are more likely to pay more for better customer experience. As a matter of fact, they even tend to prioritize trusting brand relationships over product quality.

Following this tendency, most successful businesses move from communication to conversation. And to catch this wave, I will tell you exactly how data-driven marketing can help companies get there.

Advantages of data-driven marketing

For someone who has been working in the business of information for over a decade, it is evident that data analytics is the backbone of marketing automation.

To develop any successful marketing strategy, there is a need for large amounts of information on the target audience. For example, their attitudes, behavior and experiences. Also, activities such as monitoring brand reputation, search engine ratings, and competitors’ behavior should be a part of marketing strategy development.

All of these goals are simplified ten-fold by automated data gathering. There are few data extraction tools available, most of which rely on web-crawling. It automizes long hours of manual labor spent on researching information. Also, it drastically reduces labor costs and expenses, while at the same time increasing productivity, quality, and the process itself.

How does web crawling work?

Web crawling, also known as web scraping, is the process of retrieving data from a website. It revolutionizes mundane, mind-numbing tasks of manually extracting data. Instead, web scraping uses well-calculated automation processes to retrieve valuable data from the internet.

Considering all the advantages that web-scraping offers, it comes as no surprise that this practice is growing in popularity. In fact, marketing is not the only department reaping the benefits. For instance, cyber-security, retail, and travel industries, among others, have been taking advantage of what data can offer.

The fascinating aspect of web scraping is that it is continually evolving. Indeed, industry leaders continually come up with more and more ways to use big data to reach their business goals. I am certain that there are still unexplored ways of how web crawling can aid companies across the globe to automate their processes and gain valuable insights.

4 Fool-proof ways to use data collection

Real-time public data scraped from the web has multiple uses and goals to simplify the marketing strategy. Here is to name a few:

Make your clients happy

Listen, listen, listen. In general, the needs and desires of the customer are the guiding stars for every successful business. In truth, people are giving valuable feedback to companies everywhere on the web: social media, personal blogs, news articles, comment sections, or discussion boards.

Unfortunately, most of this information never reaches the marketers’ eyes. Uninformed decisions then lead to the creation of products or services that people don’t really care about.

By effectively tapping into a constant stream of publicly available information, businesses can continually shift their marketing strategy responding to the latest industry trends. This gives a chance to adjust communications appropriately, offer products and services which clients genuinely want, and improve the overall customer experience.

Strengthen your brand

What are the highest valued companies in the market? The answer? The same companies carrying best-recognized brands in the world. I hope it is evident for everyone that it is no coincidence.

Strong and well-regarded brands are the result of not only great promotional strategies but robust defense systems against such possible threats as counterfeiting or copyright infringement.

Brands suffering from counterfeiting globally.

In 2017 alone, the estimated losses the brands have suffered from counterfeiting globally have amounted to staggering 323 billion dollars. Sadly, this grim trend is rising, according to the Global Brand Counterfeiting Report.

Data-driven marketing can support the constant brand vigilance efforts to diminish the damages. As a response to the situation, luxury brands are employing proxies to crawl e-commerce websites, auction sites, and relevant marketplaces to spot the fraudsters.

Massive-scale web crawling also can support the prosecution process against these illegal activities by collecting all necessary data.

Know your competition

Being able to obtain and process large amounts of information about your market competition, gives an enormous competitive advantage. The traditional methods of doing market research, including the interviews, surveys, and focus groups, are quickly turning into a thing of the past, giving way for more time and cost-efficient web crawling.

Automating the market research for such routine processes as tracking changes in pricing, auditing the product line, observing presence online, public engagement, and other promotional activity through various communication channels gives an opportunity to react quicker and more accurately.

Be searchable

Visibility online is everything. Sadly, statistics are cruel on this matter. According to Protofuse, less than 10% of people advance to page 2 on search engines. This means that even the best products or services available will never reach the eyes and ears of the potential client. The regrettable fact is that the majority of businesses are struggling in this regard.

SEO (Search Engine Optimization) is the process that allows improving the visibility of the website among the search engine results. Not surprisingly, the web crawling tool is essential for monitoring changes in these ratings. Getting hands-on real-time and location customized data can serve the company to come up with the most effective strategies for increasing exposure instantly.

As the game is changing rapidly

The use of big data has been in the marketing world for a while now, and it is not going anywhere anytime. On the contrary, the data-driven approaches will continue to shape marketing, along with other industries. To emphasize this point, the global demand for data analytics has been continually increasing and is expected to rise in the future, as pointed out in the Oxylabs 2020 Trend Report.

In 2019, the market value has reached $49 billion. Concluding from the steady growth rate, it is expected to double in just seven years, reaching an impressive $103 billion by 2027.

It is fair to say that future data scientists will contribute their skills and technical knowledge in more diverse business sectors.

The trend is a serious signal for all market players to adapt and embrace the ongoing innovations. However, if they shy away from using the powerful insights from the big data, their competitors will outsmart them in every move, by betting on data-backed decisions.

By Julius Cerniauskas

CEO at Oxylabs. Julius Cerniauskas is Lithuania’s technology industry leader & the CEO of Oxylabs, covering topics on web scraping, big data, machine learning & tech trends.

Sourced from readwrite