Hire Machine Learning Developer

Machine learning, including OpenCV development is becoming very popular. According to MarketsandMarket™ research report 2017, the market value of machine learning in the major industry verticals will reach $8.81 billion by 2022 at 44.1% compound annual growth rate (CAGR), from its existing market value of $1.03 billion in 2016.

What Methods and Tools Machine Leaning Developers Are Using

Machine learning is a modern concept of learning based on artificial intelligence executed by intelligent computers or machines. Developing an application that helps machines to learn from the past data, examples and function/activity patterns without any explicit programming is known as machine learning.

According to Arthur Samuel one of the legendary machine learning thinkers, “the field of learning in which a computer or intelligent machine is provided with the capability of learning from the data without explicitly programming them”. So, it is very clear from the above definitions that machine learning is the field of studies based on machine learning algorithms, where machines (not human) learn about how to understand the situation and respond appropriately. If you’re looking for GIS software development for your business, feel free to get in touch with the experts at Mobilunity. Different machine learning methods based on machine learning tools and techniques are adopted in this domain of studies. A few very important machine learning methods are listed below.

  • Reinforced machine learning algorithms
  • Supervised machine learning algorithms
  • Unsupervised machine learning algorithms
  • Semi-supervised machine learning algorithms

Machine learning engineers develop different kinds of machine learning tools that help machines learn through both with human intervention and without human intervention.

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Machine Learning Popularity & Uses

Numerous industries and businesses are directly influenced by the effective and productive use of artificial intelligence-based on machine learning. According to the Forrester research report, the corporations and enterprises unleashing the power of customer insight CI into their insight-driven businesses are going to capture as much as $1.2 trillion revenue by 2020. The report further reveals that investment in artificial intelligence (AI) increased by 300% in 2017 as compared to the investment made in 2016.

There are many domains of businesses, especially in telecommunication, healthcare, physical security, data prediction, information extraction, and data analysis and others, where machine learning software is being extensively used. The major machine learning software applications based on annotated data for ML include:

  • Speech recognition application
  • Image recognition application
  • Statistical strategies regarding finance and trading
  • Learning customer insights
  • Business behavior predictions
  • Customer behavior predictions
  • Extraction of information from unstructured raw data
  • Assessing output data based on multiple input variables by regression methodology
  • And many others

Let us find a skilled Machine Learning developer who’ll deliver quality results in line with your timeframe expectations!

What’s the Difference Between AI & Machine Learning Engineers?

As you may have noticed, we widely mentioned AI and machine learning engineers. But let’s clear up any confusion — are they the same, or are there any notable differences between these two specialists? We’ll explore the peculiarities of each in greater detail.

Core Differences Between AI & ML Engineering

To figure out the AI engineer vs machine learning engineer distinction, let’s first look at these two engineering domains and how they work.

  • AI engineering involves teaching computers to do tasks that typically require human intelligence, like understanding speech or making decisions. It uses various techniques like deep learning, computer vision, natural language processing, and, of course, machine learning to be human-like.
  • ML engineering, on the other hand, is a subfield of artificial intelligence that trains algorithms to make predictions or identify patterns based on data. Think of it like teaching a computer to recognize pictures of cats without telling it exactly what a cat looks like — it learns on its own. Here’s the overview of the ML models training process:

So what are the fundamental differences between an AI and machine learning engineer and their specializations? What comes to mind are the following:

  • Focus and scope. AI involves creating intelligent systems that perform a wide range of tasks, from NLP to robotics. In contrast, ML concentrates on developing algorithms for specific applications like image recognition or predictive modeling.
  • Approach. AI takes a more comprehensive approach to developing intelligent systems, including methods such as expert systems and knowledge graphs, while machine learning is primarily concerned with building models that can learn from data.
  • Data requirements. Machine learning models require large amounts of high-quality training data to be effective, while AI systems can operate with less data but may need more expert knowledge to develop.
  • Flexibility. AI systems are typically designed to be more flexible and adaptable to changing environments and inputs, while ML models are usually optimized for specific tasks.

Given the above differences, what are the use cases for artificial intelligence and machine learning? We’ve outlined some in the table below.

Most Common AI & ML Applications

Artificial Intelligence Machine Learning
Natural language processing — speech recognition, text-to-speech, and sentiment analysis Image recognition — face recognition, object detection, and image segmentation
Robotics — autonomous vehicles, drones, and industrial robots Predictive modeling — fraud detection, customer segmentation, and recommendation systems
Expert systems — medical diagnosis, financial planning, and legal analysis

Let’s now continue our AI machine learning engineer comparison by outlining specialized skills and tools for each domain. Here are some of them:

Artificial Intelligence

Machine Learning

  • Profound understanding and development of natural language processing algorithms
  • In-depth knowledge and development of computer vision algorithms
  • Deep understanding of algorithms that control and interact with robots
  • Profound data analysis skills
  • Knowledge and development of machine learning algorithms
  • In-depth knowledge of data preparation for ML models training
  • Understanding of statistical concepts and methods for analyzing data and developing machine learning models
  • Profound knowledge of identifying and selecting relevant features in the data
  • Solid skills in model selection and optimization based on the data and problem domain
  • TensorFlow framework for building and training deep learning models
  • PyTorch library for developing and training deep learning models
  • Keras API as the interface of TensorFlow
  • OpenCV library for image and video processing, object detection, and face recognition
  • Scikit-learn for data preprocessing, feature selection, model selection, and evaluation
  • Pandas for data analysis and visualization
  • NumPy for complex computations
  • Jupyter Notebook for creating and sharing documents with live code, equations, and visualizations

The skills and tools we just mentioned may be helpful for an AI/machine learning engineer alike, as these domains overlap in some cases. Read on to discover how artificial intelligence and machine learning work together.

Overlap Between AI & Machine Learning Engineering

As we said, AI and machine learning companies may work on similar tasks, as these engineering fields intertwine and complement each other in some areas. Here are the most common cases:

  • Data engineering. AI and machine learning rely on large amounts of data for training, so data engineering skills are essential for both fields. It includes data management, storage, processing, and analysis.
  • Software engineering. Developing robust and scalable software systems is critical to AI machine learning companies. So these domains may overlap in such operations as software architecture development, programming languages selection, testing, and deployment.
  • Model deployment and monitoring. Once developed and trained, models must be deployed and watched in production environments. It involves containerization, orchestration, and monitoring skills necessary for both AI and ML.

What about the real-life products that AI and ML can produce when combined? Here are a few suggestions:

  • Autonomous vehicles. Developing AVs requires expertise in both computer vision (AI domain) and machine learning to enable the automobile to navigate the environment.
  • Personalized healthcare. Natural language processing and machine learning facilitate the development of tailored medical care systems for better patient data analysis and customized treatment recommendations.
  • Fraud detection. Fraud detection systems require AI and ML to detect anomalous behavior and flag potential scam.
  • Financial services. AI and machine learning in financial services facilitate risk assessment, portfolio optimization, and fraud detection. These domains together can also help analyze market trends and make investment decisions.

Now, you fully understand the machine learning engineer vs AI engineer distinctions. As you see, these specialists have slightly different responsibilities and skillsets, and it’s critical to know them before hiring an expert, setting the appropriate AI machine learning engineer salary, and building effective AI or ML projects.

Reasons to Hire Machine Learning Developers

Every commercial, technical and social activity has transformed into a knowledge-based activity in the present-day information era. All business processes have become so complex owing to big volumes of valuable data and modern business approaches adopted in this competitive marketplace. The influence of real-time data, business competitiveness, and modern technologies is substantially changing the landscapes of modern life.

To get insight into human behavior, past data, future trends, and promising technologies, the power of a normal human brain is not sufficient. It requires heavy processing power much more than a human mind can manage in a limited time frame. The analysis of big data coming from heterogeneous systems and the business arena requires the heavy power of machines to analyze in the complex business ecosystem. Many kinds of data mining strategies are used by the freelance machine learning specialists to analyze the complex and gigantic heterogeneous data to process the data into useful information at a fast pace and cheap cost. The major upsides of using machine learning into modern businesses include:

Languages a Machine Learning Specialist Should Know

The machine learning specialists and programmers use numerous languages to develop machine learning algorithms, which are used to process and respond automatically without any explicit computer instructions through coding. The artificial intelligence programming uses numerous languages, platforms, and tools for developing machine learning-based applications.

According to the Towards Data Science research report, Python leads distantly from other popular languages with 57% popularity in the artificial intelligence developers. In this report, C/C++ stood distant second in the survey with 44% popularity in the domain of artificial intelligence programming. The most important computer languages used in machine learning include:

  • Python for general AI projects
  • C/C++ for engineering projects
  • R language
  • Java for ERP solutions
  • JavaScript for web visualization
  • Artificial Intelligence Markup Language (AIML)
  • Julia, Lisp & Scala
  • Ruby & Matlab

According to IBM research, the demand for Python machine learning professionals was at the top of all other languages. So, the demand for Python artificial intelligence developers will last for many years to come.

Machine Learning Developers and Machine Learning Development Team

The computer programmers cum mathematicians who develop machine learning applications and tools for artificial intelligence purpose are known as machine learning developers. A programmer that creates logical algorithms based on logical reasoning and learning of the data is known as a machine learning developer. There is continuous growth in the demand of machine learning teams, especially the Python machine learning professionals.

Demand for Machine Learning Developers in the Market

According to IBM research cited in Forbes article, more than 700,000 jobs related data scientist are expected globally in 2017. The job demand will surge from 364,000 openings to whopping 2,720,000 openings by 2020 in the USA only. This huge demand in machine learning developers, as well as data scientists for hire, will create new opportunities and rise in machine learning developer salary in the near future. The growth in other parts of the world, especially in Europe and Asia Pacific regions will increase tremendously in the upcoming years.

Demand for Machine Learning Engineers in Major AI Industries

Artificial intelligence is one of the most talked-about technology in the present-day marketplace. Many new startups are emerging in this domain along with the heavy investments of AI giant companies in this regard. According to a new technological article in Forbes, depicts a list of the companies that carry out huge research and development in the domain of artificial intelligence. The main companies that are the major drivers of the machine learning job market in artificial intelligence are listed below.

  • IBM Corporation
  • Google Inc
  • Deepmind
  • OpenAI
  • Microsoft Corporation
  • Pintrest Inc
  • Baidu
  • Apple Corporation
  • SoundHound

The above-listed companies are only a few prominent companies that hire machine learning developers. There are many other companies dealing with data science also hire machine learning engineer in a sizable number.

Main Skills of Good ML Developers

The skills of a machine learning developer vary from industry to industry and the level of job. The fundamental activity of all machine learning teams is an artificial intelligence development in one way or the other. Let’s have a look at a generalized skill set required for a good machine learning developer.

  • Knowledge of fundamental computer science concepts.
  • Extensive knowledge of computer languages like Java, R, Python, C/C++, and others.
  • Strong knowledge of mathematics, statistics, and probability.
  • Data evaluation and modelings such as clustering, correlations, and others.
  • Great command over applying machine learning libraries and algorithms such as H2O, Scikit-Learn, SparkMlib, and others.
  • Expert-level knowledge of databases like SQL, Oracle, PostgreSQL, and others.
  • Knowledge of modern software engineering concepts and methodologies.
  • Fluent in all major backend platforms like Linux, iOS, Windows, and others.
  • Expert-level knowledge of computer processing.
  • A professional attitude like commitment, composition, target-oriented and others.

Machine Learning Developer Salary: How Much It Costs to Hire ML Developers

According to IBM research report, the average salary for the job requiring machine learning skills is about $114,000. It is a bit higher than the advertised jobs, which is about $105,000 per annum. But, still, the machine learning developer salary is in the high slabs in the industry. Let’s compare the average salaries of a machine learning developer in 5 major countries.

These salaries may vary a bit with respect to location, city, experience, qualification and other factors.

Check CV Samples Before You Hire Machine Learning Engineer

Normally, companies higher machine learning developers in three categories. The fundamental objective of hiring a machine learning engineer is to accomplish artificial intelligence development. Let’s explore the main features of CVs for those three categories.

Entry Level Machine Learning Programmer Resume Sample

Middle-Level Machine Learning Developer CV Sample

Senior Level Machine Learning Engineer Resume Sample

Find Your Dedicated AI/ML Developer at Mobilunity

Right now our dedicated developers are working on exciting machine learning project of Canadian customer. He is involved in developing Social Patrol integrated machine learning capabilities, particularly image recognition component for social network streams pre-moderation and analysis. According to Sergey, Python developer working on this project:

“My project is about social network streams pre-moderation and analysis. We use supervised and unsupervised algorithms for Natural Language Processing and Image Classification”.

Why to Outstaff the AI/ML Developers Hiring

As we know, the machine learning engineer salary, as well as rates for Internet of Things developer or remote GIS developer, is very high in many parts of the world, especially in Europe and North America where major AI activities are concentrated at this time. The burden of stringent local rules and regulations governing human resource hiring is also very high. So, it is always a preferred option to outstaff the machine learning development projects to third party contractors. It is very easy to manage and cheaper to finance an AI project through outstaffing. There are many good outstaffing companies like Mobilunity that offer highly professional machine learning services at very cheap prices.

Contact us now and we will help you to hire machine learning developer to take on your machine learning development tasks!

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