Artificial intelligence (AI) is transforming how businesses operate and provide services. As AI continues to evolve, developers need robust tools and frameworks to build, deploy and manage AI applications efficiently. In this post, we will look at 5 of the most promising emerging frameworks and APIs that are shaping the future of AI development.

1. PyTorch

PyTorch is one of the leading open-source frameworks for deep learning and AI research. Originally developed by Facebook’s AI research group, PyTorch provides a Python-based environment for building neural networks and running tensor computations on GPUs and other hardware accelerators. 

Some key advantages of PyTorch:

– Dynamic computational graphs – PyTorch utilizes dynamic neural networks graphs that allow for more flexibility compared to static graph frameworks like TensorFlow. This makes debugging and iterating models quicker.

– Strong Python integration – PyTorch code feels more native Pythonic and integrates well with Python data science stacks like NumPy, SciPy and scikit-learn. This improves developer productivity.

– Ease of use – The PyTorch API is designed to be simple and intuitive, lowering the barriers to entry for AI research and development.

– Broad ecosystem – PyTorch has a thriving open-source community with libraries for computer vision, natural language processing and other domains. There is also extensive educational resources and documentation available.

In 2023, we expect PyTorch adoption to continue growing, especially for natural language and recommender systems where its dynamic approach provides advantages.

2. TensorFlow 

TensorFlow pioneered the field of deep learning frameworks and remains a widely used platform from Google. It utilizes static neural networks graphs and has a huge range of tools and libraries for building and deploying machine learning models at scale.

Key strengths of TensorFlow include:

– Production-ready – TensorFlow is very robust and can handle massive datasets and distributed training across clusters of GPUs. This makes it ideal for large-scale deployments.

– Cross-platform – TensorFlow supports desktops, servers, mobile and embedded devices through a single API. This simplifies model development and deployment.

– Google ecosystem – TensorFlow integrates tightly with other Google services for data analytics, monitoring, logging and more. This is beneficial for organizations invested in Google Cloud.

– Simplified workflow – Features like Keras and TensorFlow Extended (TFX) provide high-level APIs that abstract away low-level details, allowing developers to focus on the model.

TensorFlow 2.0 improved usability and we expect it to be a mainstay for enterprises, particularly for computer vision where its static graphs excel.

3. PyTorch Lightning 

Built on top of PyTorch, PyTorch Lightning is a newer framework that aims to simplify a lot of the repetitive coding involved in research and applying PyTorch. It lets you concentrate on the actual research rather than boilerplate.

Some of the key advantages of PyTorch Lightning include:

– Rapid prototyping – The simplified interface makes iterating on AI models faster and more productive.

– Promotes best practices – Lightning enforces techniques like separating models from datasets to improve code quality.

– Minimal code – Lightning reduces the amount of code required compared to native PyTorch. Models are more compact and readable.

– High performance – Lightning makes it easy to use multiple GPUs or TPUs and also supports distributed training with minimal changes.

– Model agnostic – Lightning is designed to work seamlessly with any model architecture or data. This flexibility allows tackling more types of problems.

PyTorch Lightning is gaining popularity with researchers and companies doing cutting-edge work. We expect its high-level abstractions to continue spreading in 2023.

4. ONNX

The Open Neural Network Exchange (ONNX) is an important emerging standard for model interoperability. ONNX provides an open format for representing deep learning and traditional ML models that works across frameworks like PyTorch, TensorFlow, and more.

Some of the major advantages of ONNX include:

– Vendor neutral – ONNX is not tied to any specific framework or vendor, providing flexibility to use different tools in a model’s lifecycle.

– Hardware portability – ONNX models can run on a wide range of hardware including CPUs, GPUs, and dedicated AI accelerators from different vendors.

– Language independence – Models defined in ONNX can be trained and deployed through various programming languages beyond just Python.

– Optimization – ONNX’s intermediate representation allows additional performance optimizations and graph tweaks than working within a single framework.

– Broad adoption – Major companies like Microsoft, AWS, and Facebook support ONNX in their offerings indicating its emergence as a standard.

ONNX enables new cross-platform AI workflows. We expect its usage to grow significantly through 2023 and beyond as companies look to avoid vendor lock-in.

5. TensorFlow Serving

Once models are built, serving them at scale in production is a major challenge. TensorFlow Serving is one of the leading open-source projects from Google for deploying trained TensorFlow models in production environments.

Some of TensorFlow Serving’s key capabilities:

– High performance – Optimized for low latency and high throughput inference even under heavy load.

– Scalability – TensorFlow Serving runs efficiently across a cluster of GPU/TPU machines to distribute requests. This allows scaling up capacity as needed.

– Version management – Easy rollouts and rollbacks of different model versions and configurations without downtime.

– Language APIs – Exposes gRPC endpoints for interfacing with models which allows creating clients in many languages.

– Monitoring – Prometheus integration provides metrics for monitoring and alerting on critical model KPIs in production.

By taking care of several devops and infrastructure challenges, TensorFlow Serving enables teams to focus more on their models vs operational complexity. Its wide usage and reliability make it a key project to watch as models get embedded in more production systems in 2023.

The Future of AI Development

The AI ecosystem continues evolving rapidly with new frameworks, libraries and tools emerging constantly. These innovations are lowering barriers for developing performant and scalable AI applications.

In 2023, we can expect deeper integrations between ML frameworks and adjacent data analytics stacks. AutoML will also gain more traction through projects like PyTorch Forecasting. Companies will invest more in internal platforms to manage the end-to-end ML lifecycle as they productionize more models.

Interoperability standards like ONNX will allow combining different frameworks and compilers for tackling cutting edge techniques like multimodal learning. Robust serving solutions like TensorFlow Serving will enable higher throughput and lower latency for real-time inference.

The democratization of AI development means every software engineering team needs awareness of the latest frameworks and APIs. This will only accelerate as businesses infuse intelligence into more products and processes to remain competitive.

Conclusion

This summarizes our look at 5 of the most important frameworks shaping the next wave of AI application development – PyTorch, TensorFlow, PyTorch Lightning, ONNX and TensorFlow Serving. Each brings unique capabilities catering to different modeling and operational requirements.

For those new to AI development, PyTorch and TensorFlow remain the most broadly applicable starting points today. As needs scale and evolve, the other emerging specialized frameworks highlighted will provide critical solutions for managing complexity, maximizing performance and avoiding lock-in.

Staying up-to-date on the latest frameworks and continuously evaluating their fit allows architecting systems optimized for productivity, agility and reliability as AI becomes central to everyday software.