Drive Innovation With These Top AI Tools for Developers

Artificial intelligence is transforming software development. As developers, we now have incredible new capabilities at our fingertips - from lightning-fast neural networks, to natural language processing, computer vision algorithms, and more. These AI tools and frameworks help us build smarter, more intuitive applications, automate mundane tasks, and take our code to the next level.

In this post, we'll highlight some of the most popular AI libraries, APIs, and platforms used by developers today. Whether you're just beginning your AI journey or are looking to expand your skillset, these tools can accelerate your projects and supercharge your coding. Let's dive in!

Top Machine Learning Frameworks

Machine learning has unlocked a world of possibilities in software. Here are some of the most widely used ML frameworks that data scientists and developers rely on:

TensorFlow

Created by Google's Brain team, TensorFlow excels at numerical computation and building deep neural networks. It supports advanced configurations like convolutional and recurrent nets. TensorFlow leverages GPU acceleration and distributed training across devices like TPUs. Through TensorFlow Hub, you can access a variety of pre-trained models like BERT and Inception. It's great for NLP, time series analysis, computer vision, and more. TensorBoard provides useful visualization and debugging capabilities.

PyTorch

Developed by Facebook's AI research group, PyTorch shines for its dynamic computation graphs and beginner-friendly workflows. It offers strong GPU support and integrates natively with Python. PyTorch powers numerous NLP models like GPT-2 as well as computer vision applications. The active open source community provides ample tutorials and projects to learn from.

Keras

Keras is a high-level API capable of running TensorFlow, CNTK, Theano, and other backends. It makes prototyping deep learning models incredibly easy. Keras supports convolutional and recurrent neural networks for tackling image and text problems. With pretrained models like Xception, VGG16, BERT, and more, it's simple to get started. Keras is perfect for beginners with its extensive guides and tutorials.

Scikit-learn

For simpler machine learning tasks like classification, regression, and clustering, Scikit-learn is a popular Python library. It implements algorithms like support vector machines, random forests, gradient boosting, and more. Scikit-learn is more approachable for beginners compared to deep learning libraries. It also includes handy utilities for data processing, model evaluation, hyperparameter tuning, and more.

MXNet

MXNet is a flexible, lightweight framework supporting major languages like Python, R, Julia, Scala, and C++. With its emphasis on speed and portability, it enables fast model training on GPUs and deployment on diverse hardware environments. MXNet ships with ready-made neural network layers, automatic differentiation, and other features. Amazon uses MXNet to power services like SageMaker Neo.

While all these ML frameworks have their strengths, TensorFlow and PyTorch stand out as the most popular options for their advanced capabilities, strong community support, and cloud integrations. TensorFlow excels for production deployment while PyTorch offers a more flexible workflow preferred by researchers. Both frameworks empower developers to build and deploy powerful AI applications.

Natural Language Processing Libraries

For breathing intelligence into text, NLP libraries provide the algorithms and models for understanding human language.

NLTK

NLTK is the most popular Python NLP library used by students, researchers, and developers. It supports tokenization, part-of-speech tagging, chunking, classification, translation, and more - over 50 tasks in total! NLTK comes bundled with text corpora like WordNet and OntoNotes as well as pretrained models for chunking and parsing. While extremely comprehensive, it requires more effort to productionize compared to spaCy or Hugging Face.

spaCy

spaCy excels at production-grade NLP. It includes industrial-strength pipelines for text processing, multi-task learning, and convolutional neural networks. spaCy's statistical models and word vectors support over 50 languages. The library is easy to use and integrate into large projects. It also offers a visualization module and pretrained pipelines for sentiment analysis, named entity recognition, and other capabilities.

TextBlob

Built on top of NLTK and pattern.en, TextBlob provides a simple API for common NLP tasks like part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, spelling correction, and more. It integrates neatly with WordNet alongside multilingual support. TextBlob offers a great starting point for students or smaller NLP experiments. For large production systems, other libraries might be preferable.

gensim

If you need semantic similarity calculations, topic modelling, document indexing, gensim is a great open source library. It implements algorithms like TF-IDF, latent semantic analysis (LSA), latent Dirichlet allocation (LDA), Word2Vec, FastText, and more. gensim is useful for building search engines, contextual recommender systems, semantic similarity, and other applications. It also integrates nicely with NumPy and SciPy stacks for additional mathematical and scientific computations.

AllenNLP

Created by the AI2 team, AllenNLP provides reference implementations of the latest state-of-the-art NLP models. It offers an easy way to experiment with things like BERT, ELMo, GPT, and other recent advances. AllenNLP comes bundled with pretrained models and benchmark datasets to apply these techniques. It's a handy library for reproducing and building on top of cutting-edge NLP research.

For most production use cases, spaCy is a leading choice for its speed, scalability, and ease of use. NLTK provides the most comprehensive set of algorithms and models for research.

Leading Computer Vision Libraries and Frameworks

For software that can "see" and understand visual data, computer vision libraries provide the necessary ingredients. Here are some popular options:

OpenCV

The go-to library for image and video analysis, OpenCV offers over 2500 algorithms for computer vision tasks. It supports facial recognition, object detection, computational photography, augmented reality, and more. The active open source community provides examples and tutorials in C++, Python, and Java. Integrating OpenCV with machine learning frameworks like TensorFlow unlocks additional capabilities.

TensorFlow Object Detection API

Part of the TensorFlow library, this provides ready-to-use models like SSD and Faster R-CNN for detecting multiple objects in images or videos. The pretrained models can be run in real-time for analysis. It also simplifies training custom detectors on your own datasets. The Object Detection API includes useful utilities like visualization tools to debug models during training.

For building robust computer vision applications, OpenCV and TensorFlow Object Detection API offer the most mature tooling. OpenCV provides the core image processing functions while TensorFlow powers the deep learning components for tasks like real-time object detection. Together they enable developers to build sophisticated CV systems.

Detectron2 (By Facebook AI Research)

Created by Facebook AI Research (FAIR), Detectron2 focuses on instance segmentation including object detection and semantic segmentation. It provides high-performance implementations of models like Mask R-CNN, RetinaNet, and Faster R-CNN in PyTorch. Detectron2 ships with useful utilities like data loaders, evaluation metrics, regularization strategies, and more to accelerate computer vision deep learning.

Labelbox

Labelbox offers a cloud platform to support image, video, text and data labeling workflows. It includes AutoLabeling to accelerate the process using model predictions. Integrations with TensorFlow, PyTorch, and SageMaker simplify model building. Better training data leads to improved model iteration and performance.

MMDetection (By OpenMMLab)

Part of the OpenMMLab project, MMDetection provides an object detection toolbox based on PyTorch. It implements leading models like Cascade RCNN, SSD, YOLOv3, and more with modular design for customization. MMDetection supports model training, testing, and deployment with SOTA algorithms. It excels at tasks like instance segmentation for research and real-world applications.

Key AI Application Frameworks

Beyond the core libraries, these frameworks simplify building and deploying AI-powered apps:

Spark MLlib

Built on top of Apache Spark, MLlib provides distributed machine learning on big datasets. It supports end-to-end pipelines from data preprocessing to model tuning and selection. Algorithms include classification, regression, clustering, collaborative filtering, and more. MLlib leverages Spark's speed, scalability, and fault tolerance for production applications.

H20 Driverless AI

Driverless AI automates key steps in machine learning workflows like feature engineering, model tuning, selection, and deployment. It generates production-ready pipelines for frameworks like Tensorflow, PyTorch, and XGBoost. Driverless AI reduces the effort of delivering ML applications from months to hours.

BigML

BigML makes it easy to build and integrate machine learning capabilities into applications via their REST API or SDKs for various languages. It offers high-level abstractions versus coding models from scratch. BigML supports datasets, model training, anomaly detection, time series forecasting, and other ML functionality out of the box.

Azure Cognitive Services

Microsoft Azure provides a suite of prebuilt AI APIs and services covering vision, speech, language, search, and conversational bots. Developers can quickly add cognitive features like image tagging, QnA bots, anomaly detection, without data science expertise. Cognitive Services integrate nicely with Azure Machine Learning for no-code/low-code development.

IBM Watson Studio

IBM's cloud-based environment for data scientists and developers, Watson Studio supports the end-to-end machine learning lifecycle. It includes managed Jupyter notebooks, AutoAI for automated model building, tools to explain model decisions, and more. Watson Studio offers collaboration capabilities for teams and enables building AI applications via integrations with Watson Assistant, Knowledge Catalog, and other Watson services.

Conclusion

The AI developer toolbox continues to rapidly evolve. As the examples above illustrate, we now have incredibly powerful frameworks for machine learning, NLP, computer vision, and more. Whether you need flexibility for research or fast integration for products, there are options to match your use case. With compute resources more accessible than ever, it's an exciting time to leverage AI and turbocharge our applications.

This post covered some of the most popular tools, but new projects emerge constantly as developers everywhere push the boundaries of what's possible with artificial intelligence. The future looks bright as AI enables software that's smarter, more intuitive, and capable of improving people's lives.

If you're looking to stay on top of the latest developer tools and technologies, check out DevHunt. Their platform makes it easy to discover, compare, and get started with hundreds of code libraries, APIs, testing tools, hosting solutions and more. With new products launching every day, DevHunt is the perfect resource for exploring everything that's now possible with AI.

What will you build next with these incredible new capabilities? The potential is endless and we've only scratched the surface.