TensorFlow, a machine learning platform developed by Google, has changed how developers think about building AI applications. Since its most recently released version became available , it has seen tremendous growth over its lifetime. This growth can be attributed to several factors, including its ability to provide high-quality models in a timely manner and its ease of use.
It is no secret that TensorFlow has been one of the most popular and widely used machine learning frameworks in recent years. It has provided developers with many tools and functionalities to build complex, deep-learning models. It has also been extensively used in various applications, including image and speech recognition, natural language processing, and robotics.
As with any rapidly evolving technology, TensorFlow is constantly undergoing changes and improvements. In this article, we will highlight some of the most important trends and changes that TensorFlow has experienced recently and its impact on developers.
Noteworthy changes
One significant change that TensorFlow made was the addition of support for TensorBoard. It allows you to visualize your data in real-time and make sense of any patterns or trends that may be present. This feature is handy for CTOs who need quick access to their data to make informed decisions about how best to utilize it.
Another useful trend is its ability to run models on mobile devices via the Google Cloud Platform (GCP). This means you can access powerful machine learning capabilities without purchasing dedicated hardware or paying costly licensing fees associated with operating software on physical machines located elsewhere in their network infrastructure.
Other changes worth mentioning:
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TensorFlow has become more accessible. It is now possible to use this library without needing to be a programmer or even have any programming experience. This makes it an excellent tool for those who want to start with machine learning but don't have the time or money to invest. It's also helpful for companies who wish to keep their costs down while ensuring their data science team can work effectively with the technology.
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TensorFlow is being used with other tools and platforms, such as Google Cloud Platform (GCP). This enables users of these products to take advantage of an already established ecosystem of developers and tools that can allow them to move faster than if they were developing everything from scratch by themselves.
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It has become a powerful framework for building machine learning and deep learning models.
Trends
The AI world is rapidly evolving; therefore, there are several trends and changes that skilled TensorFlow developers should be aware of. In the world of machine learning, learning never stops. Your employees should constantly upskill themselves in relevant programming languages and understand the technological world better.
As the head of technology in your business, you must be confident in your team to know some of these trends:
TensorFlow 2.0 and beyond
TensorFlow 2.0 has introduced many new features and improvements over the previous version. It was designed to be more user-friendly, emphasizing simplicity and ease of use. TensorFlow 2.0 includes Keras as a high-level API, making creating and training neural networks easier.
Jezuina Koroveshi, an ML engineer, backs this up as one of the biggest trends and has noted that TensorFlow 2.0 has grown and matured since its inception.
“This major update to the TensorFlow platform introduced many improvements and simplifications to the API, making it easier for developers to use and optimize their models. Some of the key changes included the adoption of Keras as the official high-level API for TensorFlow, the addition of eager execution mode for more intuitive debugging and performance optimization, and the integration of the TensorFlow Data API for efficient data loading and processing.”
Jezuina Koroveshi
In 2023, skilled developers should be familiar with TensorFlow 2.0 and its features. CTOs should test developers on their understanding of Keras and how it can be used to build neural networks. Developers should also be familiar with the latest updates to TensorFlow, as new features and improvements are constantly being added.
Custom layers and operators
Custom layers and operators are an essential feature of TensorFlow that allows developers to create custom layers and operations for their models. This can be useful when working with unique datasets or optimizing performance.
What your developers should know: how to create custom layers and operators in TensorFlow. They should be able to explain the benefits of using custom layers and operators and how they can be used to optimize performance.
TFX
TensorFlow Extended (TFX) is a framework for building end-to-end machine learning pipelines. It includes tools for data validation, preprocessing, transformation, monitoring, training, and serving as well as tools for deploying and serving models.
What your developers should know: be familiar with TFX and how it can be used to build machine learning pipelines. You should test a TensorFlow developer’s understanding of TFX and its ability to use it to build machine learning pipelines.
TensorFlow Lite
TensorFlow Lite is a version of TensorFlow designed for mobile and embedded devices. It allows developers to run TensorFlow models on devices with limited resources, such as smartphones and IoT devices.
“TensorFlow Lite allows developers to build and deploy models that can run on a wide range of devices, from smartphones to smart cameras.”
Jezuina Koroveshi
What your developers should know: be familiar with TensorFlow Lite and how it can be used to run TensorFlow models on mobile and embedded devices. Test a developer’s ability to build TensorFlow models that can be run on mobile and embedded devices using TensorFlow Lite.
AutoML
AutoML is a technique for automatically building machine learning models. It involves using algorithms to automatically select the best model architecture and hyperparameters for a given dataset.
What your developers should know: how it can be used to build machine learning models more efficiently. Test a TensorFlow developer on their understanding of AutoML and their ability to use it to build machine-learning models.
Explainability and fairness
As machine learning becomes more prevalent in society, there is a growing need for models to be transparent and explainable. This is especially important in healthcare and finance, where decisions based on machine learning models can have significant consequences.
What your developers should know: be familiar with techniques for building explainable and fair machine learning models. Consider testing developers on their understanding of explainability and fairness and their ability to build transparent and fair models.
Federated learning
Federated learning is a technique for training machine learning models on decentralized data. It allows multiple devices to collaborate in training a machine learning model without sharing their data.
What your developers should know: be familiar with federated learning and how it can be used to train machines.
TensorFlow.js
This library allows developers to run TensorFlow models in a web browser. It makes it possible to build web applications that use deep learning models for tasks such as image recognition and natural language processing.
“Additionally, TensorFlow has introduced several features, such as distributed training and GPU acceleration, to speed up the training of large and complex machine learning models.”
Jezuina Koroveshi
Why developers need to keep up with TensorFlow trends
There are several reasons why developers need to keep up with TensorFlow trends. Jezuina lists five of the most important reasons:
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Access to New Features: TensorFlow is a fast-evolving technology, and new features and improvements are constantly being introduced. Keeping up with these changes can help developers take advantage of new tools and utilize the latest features to create more powerful and efficient models.
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Improve development efficiency: With the constantly evolving technology, the ways to optimize the performance and efficiency of TensorFlow models are also changing. Staying up-to-date with TensorFlow trends can also help developers improve their development efficiency using new tools, APIs, and best practices. By keeping up with trends, developers can identify and implement new techniques to improve their models' speed, accuracy, and resource efficiency.
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Enhancing skills and knowledge: Keeping up with TensorFlow trends can help developers improve their skills and expertise in machine learning and deep learning, which are in high demand in the job market. Developers can expand their capabilities and stay competitive by learning new techniques and algorithms.
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Access to new research and innovations: TensorFlow is used extensively in research. Keeping up with the latest trends can provide developers access to further research and innovations in machine learning and AI.
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Career Advancement: As TensorFlow is widely used in several industries, staying up-to-date with TensorFlow trends can also increase career opportunities and help developers stay competitive in the job market.
Challenges a company may face if they do not keep up with TensorFlow trends
If your company does not keep up with the latest TensorFlow trends, it may face several challenges, including:
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Inability to take advantage of new features: By not keeping up with the latest features, you may miss out on the opportunity to leverage new features, putting you at a disadvantage compared to your competitors.
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Reduced Efficiency: As new TensorFlow updates are released, they often include performance improvements and optimizations to help developers build more efficient machine learning models. If your company fails to keep up with these updates, your models may become outdated and inefficient, reducing productivity and increasing costs.
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Difficulty recruiting top talent: If your company is not investing in the latest TensorFlow tools and techniques, you may find it challenging to attract top talent looking for opportunities to work with the latest and most advanced technologies.
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Falling behind the competition: Staying up-to-date with the latest TensorFlow trends is essential to remain competitive in the rapidly evolving field of machine learning. If your company falls behind the competition, you may miss out on crucial business opportunities, lose market share, and struggle to keep up with evolving customer demands.
“By staying updated with the latest trends, businesses can ensure they are making the most of TensorFlow's capabilities and stay ahead of the competition in the fast-evolving field of machine learning.”
Jezuina Koroveshi
The benefits of hiring TensorFlow developers who are updated with the latest trends
Hiring TensorFlow developers who are updated with the latest trends is good practice.
“Machine learning is a rapidly evolving field, and new techniques, algorithms, and frameworks are constantly being developed. Staying updated with the latest TensorFlow trends can help developers build better machine learning models, work more efficiently, and solve complex business problems more effectively.”
Jezuina Koroveshi
Jezuina also suggests that hiring TensorFlow developers who are updated with the latest trends brings several benefits, such as improved efficiency, access to the latest tools and techniques, and increased innovation.
What TensforFlow is expected to bring in 2023
TensorFlow is expected to continue to evolve and bring significant advancements in artificial intelligence in 2023. Jezuina lists some of the most anticipated updates:
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XLA Compilation to make most model training and inference workflows faster on GPU and CPU.
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Distributed computing: DTensor is a new API for large-scale model parallelism. DTensor unlocks the future of ultra-large model training and deployment and allows you to develop your model as if training on a single device, even while using multiple clients. DTensor will be unified with the tf.distribute API, allowing for flexible model and data parallelism.
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New tools for CV (Computer Vision) and NLP (Natural Language Processing): The ecosystem is increasing via the KerasCV and KerasNLP packages, which offer modular components for applied CV and NLP use cases, including an extensive array of state-of-the-art pre-trained models.
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C++ API for applications: Development of a new public TF2 C++ API for native server-side inference as part of a C++ application.
TensorFlow is an ever-evolving framework that continues to shape machine learning and artificial intelligence.
As it becomes an industry-standard in solving many machine learning problems, keeping up with changes and updates will be prudent.
By staying up-to-date with these changes, skilled developers can continue to push the boundaries of what's possible with TensorFlow and create innovative machine-learning solutions that drive progress in various industries.
