Recently, the use of Artificial Intelligence (AI) has become increasingly popular, making TensorFlow one of the most popular AI libraries. TensorFlow is an open-source library created by Google and used for building and training machine learning models.
Many companies want to be able to utilize machine learning in their products and services. However, the vast majority don't have the technical expertise or skills needed to develop machine-learning applications themselves. When hiring a developer that specializes in TensorFlow, you can be confident that they will have the knowledge and skills needed to bring your ideas to life.
As the demand for AI increases, so does the demand for TensorFlow developers. However, hiring the right TensorFlow developer can be a daunting task. So before you dive in, here are a few things you should know about TensorFlow to help you on your way to finding the right person for the job.
About TensorFlow
As you may already know, TensorFlow is a powerful machine-learning framework that enables you to create highly efficient and flexible machine-learning solutions. It's particularly useful for applications where you need to build models with many layers of abstraction, such as speech recognition, computer vision, and natural language processing.
Jezuina Koroveshi, a Machine learning engineer, defines TensorFlow as an end-to-end open-source platform for machine learning.
One thing that makes this programming language stand out from the rest, according to her, is that it has a comprehensive, flexible ecosystem of tools, libraries, and community resources. These allow researchers to push the state-of-the-art in ML and allow developers to quickly build and deploy ML-powered applications.
“TensorFlow provides a collection of workflows with intuitive, high-level APIs for both beginners and experts to create machine learning models in numerous languages. Developers can deploy models on several platforms, such as on servers, in the cloud, on mobile and edge devices, in browsers, and on many other JavaScript platforms. This enables developers to go from model building and training to deployment much more easily.”
Jezuina Koroveshi
It was developed by Google Brain and released on GitHub in November 2015. TensorFlow can run on multiple platforms, including Linux, MacOS, iOS, and Windows, and has been used to build a wide range of applications, including image recognition, video analysis, and self-driving cars.
“TensorFlow's main focus is on providing a flexible and powerful framework for building and training machine learning models. This includes data processing but also encompasses other key areas such as model architecture design, model training and optimization, and model deployment.”
Jezuina Koroveshi
TensorFlow has been used by more than 1 million developers worldwide since its launch in 2015. The company says it has shipped more than a billion models using TensorFlow across its own internal products, such as AlphaGo Zero and other machine learning systems like Cloud ML Engine.
And, according to Stack Overflow 2022, Tensor Flow is incredibly popular among professional developers., with 11.49% responding to working with it professionally.
Companies that use TensorFlow
TensorFlow is now used by some of the most recognizable names in tech, including Twitter, Uber, and Google Cloud Platform. These companies are using TensorFlow daily to solve problems that range from speech recognition to ride-share availability predictions.
More TensorFlow companies:
- WeightWatchers
- Ball Aerospace
- Jellyfish
- Dropbox
- Verkada Inc.
- Unity
- McKinsey & Company
- Netsuite Inc.
Based on Enlyft’s data on 33,205 companies that use TensorFlow captured over the course of 5 years and 11 months.
Different industries and applications
TensorFlow has been used in various industries and applications, ranging from robotics to finance to search engine optimization (SEO). The following list will provide some insight into how TensorFlow can be used in different industries:
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Robotics: Autonomous vehicles and robots are becoming more prevalent in our daily lives; TensorFlow can help these machines learn new skills by making them smarter than ever before! And more specifically, it can be used to train robots to perform complex tasks, such as object recognition, navigation, and control.
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Finance: TensorFlow makes it easier than ever before for financial institutions to track risk across their portfolio of assets while also predicting future values based on historical data.
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Healthcare: Analyzing medical images or patient data to aid in diagnosis and treatment.
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Marketing: Predicting future outcomes based on historical data by using predictive modelling.
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Image recognition: Recognizing objects within images or classifying images based on their content. This can be used in applications such as facial recognition, security systems, and autonomous vehicles.
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Recommender systems: Recommending products, movies, or music to users based on their past behavior or preferences.
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Natural language processing: Analyzing and understanding human language, including speech recognition, sentiment analysis, and language translation.
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Anomaly detection: Identifying unusual patterns or behavior within data, which can be helpful in fraud detection or network security.
TensorFlow has already proved itself in many fields, including medicine, finance, and transportation. One example is Medtronic's mobile assistant app, which uses machine learning techniques to help diabetic patients manage their diabetes more efficiently. In China, TensorFlow has helped improve traffic flow by using data from RFID chips embedded in traffic lights and license plates on cars.
The reasons for its widespread success are simple: TensorFlow gives researchers the power to analyze their data more effectively than ever before, thanks to its flexibility, scalability, and compatibility with other open-source libraries. It also allows them to build more efficient networks.
What makes TensorFlow stand out?
TensorFlow has a variety of models and algorithms, but it is more than just a collection of tools for deep learning; it also provides a platform for building these models.
It allows users to create their own custom software and then integrate it with other applications, such as databases and cloud computing platforms. This flexibility allows companies to use TensorFlow in many different ways. For example, some companies use TensorFlow to build applications tailored specifically to their needs. Others use TensorFlow as part of their production process so they can scale up or down according to demand without having to rebuild everything from scratch each time there's an increase in demand (e.g., if there's an increase in sales).
TensorFlow is designed to be easy to use, so you don't have to worry about learning how to code to get started.
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Its flexibility makes it easy to integrate into your own applications. You can use it in production environments or as a data science playground for new ideas.
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TensorFlow's built-in support for standard machine learning models means that any business can quickly implement them into their products without having to write custom code or spend time researching which model is best.
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It also has a large and active community that makes it easy for businesses to find answers when needed, whether that's a question about how to implement a particular model or what data sources are available for a specific task.
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As mentioned before, it runs on top of the CPU, GPU, or mobile processors, which means that it works with any hardware you have to train your own models. The programming language is very easy to learn and use, so it can be a good choice for beginners who are just starting out in AI development.
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The data science tools in TensorFlow allow you to do all kinds of things with your data, from building models to training them to evaluate their effectiveness. You can even load data from other programs or files into TensorFlow and use it for training.
Machine learning is an integral part of many companies' strategies today because it allows organizations to make predictions about what users are likely to do next based on their past behavior. This can be used for everything from improving email marketing campaigns to predicting which products will sell better based on past customer purchases.
Why TensorFlow is a great skill to have
TensorFlow is a great skill to have because it's used in some of the most cutting-edge technology out there. If you know how to use this software, you'll be able to work on projects that are at the forefront of innovation.
TensorFlow is also often used in machine learning – a field that is snowballing and has much potential for future growth. There are so many ways that machine learning can be applied, which means there will be plenty of opportunities for people who know how to use TensorFlow.
Some of the reasons Jezuina suggests why learning TensorFlow is a great skill to have:
1. High demand: TensorFlow is used by a large and growing number of companies, organizations, and researchers in various industries such as healthcare, finance, and retail. Therefore, the demand for machine learning engineers with TensorFlow skills is high, making it a valuable skill in the job market.
2. Large community: It has a large and active community of developers and users, which means that there are many resources and support available for learning and using the library.
3. Flexibility: It provides a flexible and modular platform for developing various machine learning models, including deep learning models. This flexibility allows machine learning engineers to experiment easily with different architectures and techniques to find the best approach for a problem.
4. Scalability: TensorFlow is designed to scale easily, allowing machine learning engineers to train and deploy models on large datasets and multiple machines.
5. Integration: It integrates with other popular programming languages and libraries, such as Python, Java, and C++, making it easy to incorporate into existing engagements and workflows.
6. Innovation: It is continuously being developed and updated with new features and capabilities, which means that machine learning engineers who learn and use TensorFlow can stay up-to-date with the latest advances in machine learning technology.
The benefits of TensorFlow
TensorFlow works primarily through two different components: the TensorFlow API, which allows you to construct neural networks, and TensorBoard, which provides a way to visualize the results of your training tasks. These components work together to provide a comprehensive solution for many of the most challenging aspects of building machine learning applications.
At its core, TensorFlow is just software for running computations; however, it can be easily extended with new functionality provided by existing packages or custom modules written in Python. This extensibility means that there are hundreds or thousands of ways to tailor TensorFlow to your needs or solve specific problems.
Using TensorFlow has many benefits. You can use TensorFlow to efficiently run complicated algorithms for deep learning, which involves multiple steps and computations across tensor data arrays. Jezuina lists:
“TensorFlow allows developers to experiment with different models and architectures to find the best approach for their particular application. It also offers a wide range of APIs, making it easy to integrate with other tools and platforms.”
Jezuina Koroveshi
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It is optimized for performance and can use hardware acceleration, such as GPUs and TPUs. This means that models can be trained faster and more efficiently, which is particularly important for large-scale applications.
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TensorFlow models can be run on various platforms, including desktops, servers, mobile devices, and the cloud. This means that developers can quickly deploy models across different environments, making testing and scaling applications easier. For running inference on mobile and edge devices, you can use TensorFlow Lite. To train and deploy models in JavaScript environments, you can use TensorFlow.js.
Qualifications and skills a TensorFlow developer must have
When hiring a TensorFlow developer, it is essential to ensure that they have the necessary qualifications and skills. The following are some of the qualifications and skills to look out for:
- A degree in Computer Science, Mathematics, or a related field;
- Experience in Python programming language;
- Knowledge of machine learning concepts and algorithms;
- Familiarity with deep learning frameworks, including TensorFlow, Keras, and PyTorch;
- Experience in developing and deploying machine learning models.
It is essential to look at the portfolio and projects of a TensorFlow developer before hiring them. By looking at their portfolio and engagements, you can determine their level of experience, expertise, and creativity level. The following are some of the things to look out for in a TensorFlow developer's portfolio and engagements:
- The complexity and diversity of the engagements they have worked on.
- The ability to develop and deploy models on different platforms.
- The ability to work with different datasets and models.
- The ability to optimize models for performance and accuracy.
Benefits of hiring a TensorFlow developer
Hiring a TensorFlow developer can provide several benefits to a company. First, a TensorFlow developer has expertise in machine learning which can contribute to the success of several engagements. Jezuina writes:
“TensorFlow developers have deep knowledge and understanding of machine learning concepts and algorithms. They can use this knowledge to develop custom machine learning models to help solve complex business problems, deploy machine learning models faster, and enable the company to bring new products and services to market more quickly than their competitors.”
Jezuina Koroveshi
Second, a TensorFlow developer can help a company analyze large data sets and gain insights into customer behavior, market trends, and other valuable information. This can help the company make data-driven decisions and stay ahead of the competition.
Third, machine learning models developed by TensorFlow developers can help companies provide a better customer experience by offering personalized recommendations, improving search results, and automating repetitive tasks. This can lead to increased customer loyalty and improved satisfaction.
Things to consider before hiring
When you're looking to hire a TensorFlow developer, it's essential to consider the following:
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The depth of their experience with TensorFlow. This includes the number of engagements they've worked on and the time they've spent working with it.
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Their knowledge of how TensorFlow works. This includes how it interacts with other tools, its role in an engagement, and any challenges they have faced using it and how they solved them.
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Their ability to communicate well with other people on your team – especially if they're working as part of a larger team.
Job requirements
The TensorFlow Developer is a critical position in the company's data science team and is responsible for implementing machine learning models, training them on large datasets, and evaluating their performance.
To hire an experienced TensorFlow developer, you need to know what job requirements you should be looking for in your candidates. Since there are many different kinds of TensorFlow developers, the requirements will vary depending on their level of expertise. Here is a list of things that you should consider:
Technical skills
TensorFlow developers are expected to be proficient in the following technical skills:
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Proficiency in programming languages such as Python or C++.
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Machine learning and machine learning libraries (e.g., NumPy, Scikit-learn, Theano)
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Deep learning models (e.g., CNNs, RNNs, LSTMs) and techniques (e.g., gradient descent)
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Design patterns for data-intensive problems
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GPU computing
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Knowledge of the structure and process of computer programming
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Knowledge of the principles, practices, and tools of software engineering
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Experience with natural language processing (NLP)
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Understand how the underlying technology works and what its capabilities are
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Experience working with large datasets, i.e.working with databases, and streaming data sets
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Be able to write code that runs in production environments
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Perform debugging tasks when things go wrong so the team can fix them quickly
Soft skills
Apart from technical qualifications and skills, it is also essential to consider a TensorFlow developer's soft skills. Soft skills refer to personal attributes that enable a person to interact effectively and harmoniously with other people. The following are some of the essential soft skills to look out for:
- Communication skills: A TensorFlow developer should be able to communicate effectively with other team members and stakeholders.
“Good communication skills are important for explaining technical concepts to non-technical stakeholders and for collaborating with other team members.”
Jezuina Koroveshi
- Problem-solving skills: A TensorFlow developer should be able to identify and solve problems that may arise during the development process.
- Collaboration skills: A TensorFlow developer should be able to work in a team and collaborate effectively with other team members.
- Adaptability: A TensorFlow developer should be able to adapt to new technologies, tools, and methodologies.
In addition, Jezuina listed creativity, time management, and paying attention to detail as critical soft skills the candidate must have to be successful.
TensorFlow developers should be able to think creatively and outside the box to design and implement innovative solutions. TensorFlow engagements often have tight deadlines, so managing time effectively and prioritizing tasks is essential.
Lastly:
“TensorFlow developers should be detail-oriented and meticulous regarding data preprocessing, model training, and software development.”
Jezuina Koroveshi
Skills that would make a developer successful in this role
Hiring a TensorFlow developer is an exciting prospect that can be very rewarding for your company. However, it is essential to consider what skills are needed to be successful in this role. Jezuina writes:
Solid understanding of the underlying concepts and techniques of machine learning and deep learning: TensorFlow is a powerful tool for implementing machine learning algorithms. However, it requires a solid understanding of the underlying concepts to use it effectively. Developers should have a good grasp of supervised and unsupervised learning, neural networks, and optimization algorithms.
Familiarity with neural networks and deep learning concepts such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks.
Ability to work with data: TensorFlow is a data-driven tool, so developers should be comfortable working with data in various formats, such as images, text, and numerical data. They should also be able to preprocess and clean data to ensure it is suitable for use with TensorFlow.
Knowledge of TensorFlow APIs and tools: TensorFlow provides a variety of APIs and tools to build, train, and evaluate machine learning models. A developer should have a good understanding of these APIs and tools and how to use them effectively.
Good coding practices: A developer should have a good understanding of coding practices, such as version control, testing, and documentation, to ensure that their TensorFlow code is robust and maintainable.
Continuous learning: Machine learning is a rapidly evolving field, and new techniques and tools are constantly being developed. Successful TensorFlow developers should be willing to continuously learn and adapt to new developments in the field.
Creativity and innovation: Successful TensorFlow developers are often those who can come up with new and innovative ways of using the tool to solve real-world problems.
To sum up, success in using TensorFlow requires a combination of technical skills, problem-solving abilities, creativity, and a willingness to learn continuously and adapt to new challenges.
How to set apart a great TensorFlow developer from a good one
Jezuina writes that a great TensorFlow developer should have the following skills:
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Strong foundation in machine learning fundamentals: has a deep understanding of the foundational concepts and theories behind machine learning, such as statistical inference, linear algebra, optimization, and more. Also has a deep understanding of various machine learning algorithms and techniques, such as regression, classification, clustering, deep learning, and more, and can select and apply the appropriate algorithms for different use cases.
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Expertise in data preparation and feature engineering: skilled in data preparation, data cleaning, and feature engineering, which are crucial in developing effective machine learning models.
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Ability to collaborate with stakeholders: such as business executives, project managers, and other members of the development team. These stakeholders may have varying levels of technical expertise, so a great developer should be able to explain complex technical concepts clearly and concisely that is understandable to everyone. Additionally, they should be able to actively listen to feedback from stakeholders and adjust their approach as needed.
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Knowledge of software engineering best practices: A great machine learning engineer is familiar with software engineering best practices such as version control, testing, and code review and can develop scalable and maintainable code.
Interview questions and expected answers
1. How many types of Tensors are there?
Expected answer: There are three types of Tensors used to create neural network models:
Constant Tensors create a node that takes a value and does not change it. A constant is created using tf.constant.
tf.constant(value, dtype=None, shape=None, name='Const', verify_shape=False)
It accepts the five arguments.
Variable Tensors are the nodes that provide their current value as output. This means that they can retain their value over multiple executions of a graph.
Placeholders Tensors are used to assign data in a later time. Placeholders are the nodes whose value is fed at the time of execution.
2. Explain TensorBoard.
Expected answer: TensorBoard is a suite of visualizing tools for inspecting and understanding TensorFlow runs and graphs. It plots quantitative metrics about the chart with additional data like images to pass through it.TensorBoard currently supports five visualization techniques: scalars, images, audio, histograms, and graphs. It improves the accuracy and flow of graphs.
3. List a few limitations of Tensorflow.
Expected answer:
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It does not provide support for OpenCL (Open Computing Language).
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To use it, one must have prior knowledge of advanced calculus and linear algebra and an advanced understanding of Machine learning.
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It has GPU memory, which conflicts with Theano if it is imported in the same scope.
4. What are the three working components of TensorFlow Architecture?
Expected answer: TensorFlow architecture works in three parts:
- Preprocessing the data
- Building the model
- Training and estimating the model
5. Describe the common steps to most TensorFlow algorithms?
Expected answer:
- Import data, generate data or set a data pipeline through placeholders.
- Feed the data through the computational graph.
- Evaluate output on the loss function.
- Use backpropagation to modify the variables.
- Repeat until stopping condition.
6. Mention the name of some methods to deal with overfitting in TensorFlow.
Expected answer:
- Dropout Technique
- Regularization
- Batch Normalization
7. Where is TensorFlow mostly used?
Expected answer: TensorFlow is used in all domains covering Machine Learning and Deep Learning. Being the most essential tool, the following are some of the primary use cases of TensorFlow:
- Time series analysis
- Image recognition
- Voice recognition
- Video upscaling
- Test-based applications
8. How does the Python API work with TensorFlow?
Expected answer: Python is the primary language for working with TensorFlow. TensorFlow provides several functionalities when used with the API, such as:
- Automatic checkpoints
- Automatic logging
- Simple training distribution
- Queue-runner design methods
9. How is audio stored in the audio dashboard?
Expected answer: The audio dashboard serves to help users embed playable widgets stored in files primarily. Tf.summary.audio is used to store these files, and the tagging system embeds the latest audio based on the storage policies.
10. What is the use of a histogram dashboard in TensorFlow?
Expected answer: Histogram dashboards are commonly used to simply display complex statistical distributions of a tensor. Each histogram chart will have a slice of data that denotes the tensor's data at the point of representation.
11. What does Deep Speech mean?
Expected answer: Deep Speech is an open-source speech-to-text engine that uses TensorFlow. It is trained based on Machine Learning techniques and uses a simple syntax to process speech from an input to produce textual output on the other end.
The following syntax can be used to view all of the CLI options for Deep Speech:
./deepspeech.py
12. When using TensorFlow, is performance always preferred over accuracy?
Expected answer: No, performance is sometimes preferred over accuracy when using TensorFlow. This ultimately depends on the type of requirement and what the model is trying to achieve. The general rule of thumb is to provide equal weightage to model accuracy and performance.
Should every company use TensorFlow?
Jezuina writes:
No, not necessarily every company should use TensorFlow or any other machine learning framework for that matter. Whether to use TensorFlow or not depends on several factors, such as the nature of the company’s business, data requirements, resources, and goals.
“TensorFlow is particularly well-suited for large-scale machine learning applications, such as image and speech recognition, natural language processing, and recommendation systems. However, not all companies require the complexity and scale of machine learning that TensorFlow provides. Small businesses or those not relying heavily on data analytics may find other more appropriate solutions.”
Jezuina Koroveshi
For example, suppose a company has a small dataset and relatively simple models. In that case, they may be able to use simpler frameworks or tools that require less computational resources, such as Scikit-learn and Caffe. On the other hand, if a company is working with complex data such as images, videos, or natural language, the TensorFlow framework may be the best choice.
“Before using TensorFlow, a company should carefully consider its goals and requirements, assess its existing infrastructure and skill sets, and evaluate the potential benefits and risks of using the framework.”
Jezuina Koroveshi
Final thoughts
Machine learning and artificial intelligence are becoming increasingly prevalent in the modern world, and TensorFlow is now the most popular open-source machine-learning library for data scientists. Billions of dollars have been poured into ML solutions, so it's not surprising that AI is arguably taking over the world.
Despite its relative newness, TensorFlow is being used in many production environments because of its high level of performance, flexibility, and ease of use.
Hiring the right TensorFlow developer can be challenging. For CTOs, getting the right people is crucial for maximizing the value of new technologies. Having developers familiar with TensorFlow can help your company stay competitive and give you a leg up on the competition when developing new products that benefit from TensorFlow. So, if you think it might be time to hire a TensorFlow expert, contact us today!
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