You have likely heard about TensorFlow in the machine & deep learning circles for quite a while now, and for good reason. This Google-developed framework excels where many other libraries don’t, such as with its scalable nature designed for production deployment. With that, here are just a few reasons why you should be looking at learning TensorFlow to make your resume stand out when applying for jobs, creating scalable deliverables for your existing job, or if you’re just looking to learn a new tool.
1. Companies are actively searching for TensorFlow experts
A quick search in LinkedIn jobs or Indeed for jobs including TensorFlow will speak for itself – companies are looking for deep learning experts who know TensorFlow. As of writing, Indeed has listings for over 3000 jobs requesting TensorFlow pros, with PyTorch only getting around 2000. Many data science job listings focus around metropolitan areas like San Francisco, Austin, and Boston.
2. Frequent updates combined with flexibility
Largely thanks to Google’s support, TensorFlow is constantly updated. With revisions coming every 2-3 months, this library will keep models fresh and relevant for longer. It continues to abstract many of the complexities of deep learning and machine learning algorithms. However, as a low-level library, it still provides the flexibility to define your own functions and modify models.
3. TensorFlow is compatible with multiple languages and suitable for all levels.
While most data scientists use Python for TensorFlow, it’s still possible to use the library with a multitude of other languages, including but not limited to R (as supported by rstudio), Julia, C++, and plenty more. Additionally, TensorFlow’s suite of tools is great for advanced experts, but it’s also user-friend and built to quickly allow novice programmers to get up and running.
TensorFlow’s architecture allows it to deliver very high performance across a variety of platforms. Users can easily develop for any size of system or product from mobile apps, to websites, commercial cloud platforms, and dedicated hardware. You can build a model from a single GPU and then scale it to multiple GPUs with simple APIs.
5. Build in MLOps to get from pilot to production
TensorFlow is not just an experimentation sandbox. It takes MLOps seriously and allows you to quickly create production-quality machine learning workflow pipelines to automate, manage, and audit. TensorFlow Serving makes it easy to deploy new algorithms and experiments and integration with TensorFlow models. TFX builds on TensorFlow and is an MLOps toolkit and allows your process to run on other pipeline platforms such as Airflow and Kubeflow
6. Tools like Tensorboard
The TensorFlow team developed a full suite of tools around TensorFlow. Tensorbard is another great example. Training complex neural networks can be difficult and confusing. Tensorboard makes it incredibly easy to visualize and debug your DL models. This is key to inspecting and using your models.
7. Companies you’ve heard of are using it
TensorFlow is already incredibly popular, as noted by the use by companies like Airbnb, DeepMind, Twitter, Google, Intel, and more. Many of these companies are hiring data scientists with TensorFlow experience as well, so this may be your in to work at Google, PayPal, or other known-brands.
How to Start?
TensorFlow is a fun platform to learn and experts are in demand. However, while easy to use, it’s a large platform with features that may require deep learning training
So, why struggle by bouncing around online resources when you can learn TensorfFow from some of the top experts in the world such as Dr. John Krohn, author of Deep Learning Illustrated. Our AI+ training platform features these on-demand courses including
Deep Learning (with TensorFlow 2 & PyTorch): Dr. Jon Krohn | Chief Data Scientist, Author of Deep Learning Illustrated | Untapt
This deep learning primer brings the revolutionary approach behind contemporary artificial intelligence to life with interactive demos featuring TensorFlow 2 and PyTorch, the two leading deep learning libraries. To facilitate an intuitive understanding of deep learning’s artificial-neural-network foundations, the essential theory will be introduced visually and pragmatically. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in Python Jupyter notebooks, this foundational knowledge empowers you to build powerful state-of-the-art deep neural network models.
Data, I/O, and TensorFlow: Building a Reliable Machine Learning Data Pipeline: Yong Tang, PhD | Director of Engineering | MobileIron
1. Understanding the essential knowledge of the TensorFlow framework, including building models with Keras (tf.keras)
2. Understanding the data pipeline for machine learning with TensorFlow (tf.data)
3. Build machine learning data pipeline in production with different input sources