Believe it or not, we have come a long way from the time when artificial intelligence was dismissed as a fantastical sci-fi concept. From Siri to the viral Google Duplex demo – AI has stepped up its game to not just match but even beat human abilities in many areas.
Powering this rapid evolution are AI frameworks that provide the nuts and bolts to build and execute intelligent applications. Choosing the right framework can make or break your project so let‘s get you up to speed!
AI frameworks – the secret ingredients empowering AI growth
In the last decade, AI frameworks have progressed hugely from complex libraries used solely by PhDs to full-fledged platforms accessible even to novice coders.
Their flexibility allows experimenting easily with the hottest algorithms like transformers or building custom solutions. High performance distributed execution enables training models rapidly on vast datasets. And deployment support makes putting complete applications with AI capabilities into production a breeze.
Little wonder AI is predicted to contribute $15.7 trillion to the global economy by 2030 as per PwC analysis. A significant chunk of this will be fueled by powerful and easier to use frameworks!
Popular examples like TensorFlow and PyTorch clock over 100 million downloads highlighting the scale at which they are embraced. And companies across industries are racing to infuse AI within their DNA. Over 80% of enterprises are already piloting or adopting AI solutions as per Gartner.
Quick web search, effective predictive maintenance, near real-time fraud detection – all powered by models built on robust frameworks!
Top 15 AI frameworks leading the charge
I have compiled a comprehensive list of full-stack frameworks along with their target users and core strengths:
….detailed comparison of 15 frameworks..
As you may notice, leading options like TensorFlow and PyTorch dominate the scene. While PyTorch growth at 62% outpaced TensorFlow in 2021 as per State of AI report.
Among commercial offerings, Amazon SageMaker leads the pack with over 30% market share on cloud. Followed by Google Cloud AI and Microsoft Azure Machine Learning.
Specialized libraries like OpenCV catering specifically to computer vision continue holding relevance. Thanks to active development keeping pace with research.
And Keras retains popularity among beginners needing quick prototypes before diving deeper.
Cloud machine learning platforms are growing rapidly. Over 50% of models are currently deployed on cloud versus on-premise or hybrid according to Gartner.
So which one should you pick for your next project?
Key criteria for selecting the right AI framework
Instead of blindly following hype cycles, I would suggest carefully reflecting on parameters that matter most to your use case:
Performance
If you need to rapidly train models over huge datasets or perform blazing fast inference, analyze various benchmarks. Frameworks built for leveraging GPUs like TensorFlow, PyTorch or PHP will shine here.
Productivity
If ease of use for rapid experimentation is vital, Tensorflow with Keras frontend or SciKit-Learn will minimize coding allowing you to focus on concepts.
Scalability
For large scale deployment over clusters or latency sensitive applications, distributed offerings like Apache Spark MLlib is ideal.
Transfer learning
Like a headstart, this allows pre-built state of the art models to be customized reducing overall training times. TensorFlow Hub and Model Zoo provide such out-of-the-box models.
MLOps integration
Frameworks like Seldon Core and Tensorflow Extended(TFX) offer complete model production pipelines reducing go-live complexity.
TinyML support
To squeeze ML models into embedded devices with constraints, Tensorflow Lite Micro and other tinified frameworks are gaining traction.
…..
Of course your knowledge of coding languages can be a practical consideration. Along with availability of reference architectures, active community support in case you are stuck.
And it helps keeping an eye on latest developments like Graphcore‘s IPUs or Cerebras that promise to accelerate frameworks to new levels!
Key takeaways as you get started
With the landscape evolving at breakneck pace, here are my closing tips:
- Learn high demand frameworks like TensorFlow even if starting out
- Experiment locally before picking cloud or on-premise deployment
- Enable GPU and distributed training to scale out models
- Monitor model drift, fairness, explainability in production
- Stay up-to-date on advances in model quantization, compression
- Upskill on MLOps engineering concepts to productionize smoothly
I hope you found this guide useful. Feel free to ping me any time if you need help selecting the right AI framework or building your next intelligent solution!
