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Leverage The Full Power Of PyTorch

Get rapid results

Build stunningly performing machine learning models in a short time. PyTorch is an incredibly user-friendly tool, allowing developers to deliver results fast.

Run reliable forecasts

Get machine learning models that deliver reliable outcomes. PyTorch can utilize GPU acceleration to truly power up your market forecasting.

Pick the industry standard

Utilize the support provided for PyTorch by its developers. The tool has been built by data experts at Facebook and is used by companies like Uber and Tesla.

Power up all your ML models

Automate and cut costs across all parts of your business. PyTorch is a versatile tool that can be utilized for all types of ML models, from forecasting to NLP.

How to Hire PyTorch Developers That Will Deliver Results?

Even in the ever-changing world of machine learning, PyTorch is considered a new technology. Developed by Facebook's AI Research lab, this ML library has been initially released in 2016, but the 1.0 launch came in late 2018. To many people’s surprise, the tool quickly gained traction and rose to be one of the most broadly used machine learning solutions.

In 2018, when the 1.0 released, PyTorch was still used by a minority of researchers — but 2019 suddenly brought it to the top. The trend began in academia but PyTorch quickly found its place in the world of business as well. Now, PyTorch is the core of some of the major deep learning solutions on the market, including Tesla Autopilot and Uber's probabilistic programming language Pyro.

In our guide below, we explain how PyTorch can be useful for companies across all industries as well as how to hire PyTorch developers who can build and maintain reliable and amazingly performing machine learning models.

Why should you invest in machine learning and hire PyTorch developers?

The clue of machine learning is right there in the term itself — the goal is to build models that allow computers (machines) to autonomously learn and develop their skills. Developers can give a machine a specific goal to be achieved and feed it with terabytes of data, but the rest is to be done by the computer. 

While it may take some time to develop a reliable machine learning model for your company’s needs, the outcome is worth it — a machine can handle routine tasks normally done by humans, but do them more efficiently, saving you both time and money.

PyTorch can be used to develop a variety of machine learning solutions and guarantee their top performance (for example, by utilizing GPU). Companies worldwide hire PyTorch developers to build models that help them with:

  1. Image recognition and image classification — useful for both small cataloging projects as well as robust autonomous car systems
  2. Natural language processing (NLP) — especially popular use case of PyTorch
  3. Smart marketing tailored to each customer
  4. Reliable forecasting
  5. Risk calculation
  6. Automating customer service using solutions like chatbots or smart replies recommendations (see the use case of Airbnb)
  7. Detecting fraud and security breaches
  8. Data tagging
  9. Spam/message filtering
  10. And much, much more!

As machine learning and PyTorch are the answers to so many different problems, it shouldn’t be a surprise that the technology is now used by both huge and very small businesses alike. The number of PyTorch developers is also growing — according to the 2020 Stack Overflow Developer Survey, currently, approximately 4.1% of professional developers use PyTorch in their work. To help you navigate the market and hire PyTorch developers who truly can get the job done, we dedicate the rest of our piece solely to the hiring process.

The hard skills to look for when you hire PyTorch developers

As a highly complex specialty, machine learning with PyTorch requires a set of very specific technical skills. We list the most important ones below.

  1. Expert knowledge of Python (C++ also welcome)

    The primary language used for writing in PyTorch is — quite obviously, considering the name of the library — Python. However, using C++ is also possible, although developers consider PyTorch’s C++ interface less user-friendly. In the end, both languages guarantee a similar performance of your models.

    PyTorch also supports the C language and CUDA — a platform allowing developers to utilize Nvidia’s high-end GPUs to power their models. Expert knowledge of all four technologies isn’t necessary tough — you can hire PyTorch developers focused on Python but with some understanding of the other three.

  2. Understanding of machine learning concepts

    Not every Python or C++ developer is automatically a machine learning engineer. Building effective models requires not only a practical understanding of the language but also theoretical knowledge about machine learning and other related concepts (deep learning, AI).

  3. Understanding of complex maths

    Another level of theoretical knowledge required for machine learning development. The most important areas of mathematics for a PyTorch developer are probability and algorithms.

  4. Experience with PyTorch

    We haven’t put this on the top of our list on purpose. As PyTorch is still a very young technology, you might hear from candidates who worked longer with other ML tools — and that’s completely fine. Understanding of the core machine learning is much more important than knowledge of a specific tool. Of course, your candidate should have previous experience with PyTorch. Even if the library is easy-to-use and user-friendly, it may take a while for a developer to get accustomed to it. That’s why we highly encourage you to check whether your candidate really is familiar with PyTorch.

    Experience with other machine learning tools is, of course, welcome as well, as it gives the developer a larger perspective on the market. The most important "rival" of PyTorch is TensorFlow. Previous experience with Caffe is also useful, as Caffe2 has been in the end integrated with PyTorch by Facebook.

Picking PyTorch developers with the right soft skills

While all the above technical skills are surely a must for a PyTorch developer, you should not ignore the power of soft skills either. They can significantly improve how well your whole data/machine learning team operates and lead to even better results.

When you hire PyTorch developers, look especially for candidates with the following soft skills…

  1. Attention to details

    To build models that are truly issue-free — and therefore reliable.

  2. Proactivity

    To bring to the table new ideas for improvement.

  3. Expert time management

    To always be able to fit multiple different tasks into one day of work.

The educational background of PyTorch developers

It shouldn’t be a surprise that PyTorch became a huge thing first in the world of research. Universities across the world are deeply involved in the machine learning revolution and give students the chance to learn more about the new technology by themselves. 

When you hire PyTorch developers, you should preferably be looking for professionals with a degree that proves their understanding of the field. PyTorch developers are often graduates in computer science, mathematics, physics, statistics, or other similar fields. Many hold at least a Master’s degree.

The value of work experience

But you shouldn’t measure your candidates only by their degrees. Business experience is as much — or possibly even more — important. 

Make sure to hire PyTorch developers who have previously worked with machine learning in a business environment. The worlds of university research and commercial ML are very different and it’s important for your new hire to be already knowledgable about these differences. You will then avoid any misunderstanding and make sure the person you hire won’t leave you after hitting the first obstacle.

Remember also to focus on PyTorch developers who are specialized in the machine learning solutions you are specifically looking for. If your focus is natural language processing, you should hire PyTorch developers experienced in this unique area, and not those working primarily on forecasting.

Asking the right PyTorch job interview questions

As machine learning with PyTorch is a highly specialized field, remember that technical job interviews for your candidates should be run by a true ML expert. If you are not one yourself, you can ask for assistance from other machine learning engineers or a software consultant/IT project manager who is deeply involved in the world of data.

Below we list examples of popular PyTorch job interview questions to give you an idea of how such an interview may look like.

  1. What is overfitting?
  2. How would you do NER from scratch?
  3. How can you combat an imbalanced dataset?
  4. k-nearest neighbor algorithm vs k-means clustering — what is the difference?
  5. What is naïve Bayes? Why is it called „naïve”?
  6. Curse of Dimensionality — what is it and how can you overcome it?
  7. Fixed-width binning vs adaptive binning — what is the difference?
  8. When would you use stratified cross-validation?
  9. What is convex hull?
  10. In what circumstances Ridge regression is considered better than Lasso regression?

Who else you may need to leverage the power of machine learning?

Hiring PyTorch developers is a great starting point for taking your company into the future. If you want to leverage the power of data even more, you should also consider hiring other specialists in the field, such as AI experts, TensorFlow developers, data scientists (including data engineers), or GAN experts. 

And whoever you are currently looking for, Ideamotive can deliver. We run an industry-leading network of IT specialists from all fields, including machine learning. Whether you need to start up your business with amazing web or mobile developers or power up your enterprise with talented machine learning engineers, we can connect you with professionals perfectly matching your unique requirements.

PyTorch FAQ

PyTorch is a library for Python programs that facilitates building deep learning projects. ... Better yet, PyTorch supports dynamic computation graphs that allow you to change how the network behaves on the fly , unlike static graphs that are used in frameworks such as Tensorflow
So, both TensorFlow and PyTorch provide useful abstractions to reduce amounts of boilerplate code and speed up model development. The main difference between them is that PyTorch may feel more “pythonic” and has an object-oriented approach while TensorFlow has several options from which you may choose.
PyTorch is more of a pythonic framework and TensorFlow feels like a completely new language. These differ a lot in the software fields based on the framework you use. TensorFlow provides a way of implementing dynamic graph using a library called TensorFlow Fold, but PyTorch has it inbuilt.
Absolutely! In fact, PyTorch is a framework that I personally recommend to anyone more than those already well-known frameworks such as TensorFlow. Absolutely!!!! In fact, PyTorch is a framework that I personally recommend to anyone more than those already well-known frameworks such as TensorFlow.
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