ML/AI Frameworks

Python ML/AI frameworks

• TensorFlow

TensorFlow was developed by Google Brain team AI researchers and engineers, supporting CPUs, GPUs, TPUs. Google's proprietary TPU is the future AI computing platform. So, for long-term considerations, TensorFlow is the best choice for ML and DL.

• PyTorch

Being supported by Facebook, PyTorch has good compatibility with Numpy, and it provides strong tensor computations and dynamic neural networks in Python with strong GPU accelerations.

Feature	TensorFlow	PyTorch
Learning Curve	🚀 More difficult	🚀 Easier
Performance	⚡ More performant	⚡ Less performant
Expressiveness	💨 Less expressive	💨 More expressive
Deployment	💪 Better for production	🤼 Good for research and development
Community	👥 Larger and more active	👨‍💻 Smaller and growing
Use Cases	🏭 Production-oriented	👩‍🔬 Research and development

• Flax

Flax is a neural network library built on top of JAX, which is a high-performance Python library designed for numerical computation and machine learning with features:

1. Automatic differentiation: Simplifies calculating gradients, crucial for training neural networks.
2. Vectorization: Speeds up computations by operating on entire arrays at once.
3. Just-In-Time (JIT) compilation: Optimizes code for specific hardware, leading to significant performance gains.

So, JAX's JIT compilation and Flax's efficient use of JAX lead to fast training times. The cons is that both of these are less mature ecosystems, but it is worth exploring.

Golang ML frameworks

• mlpack

It is a fast, lightweight header-only C++ machine learning library that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. It depends only on the Armadillo linear algebra library and the cereal serialization library.

It has Golang, Python, R and Julia bindings, it is a good choice for data scientists who need to deploy machine learning models to production.

One test case is Iris dataset classification and visualization.

Rust ML frameworks

• Candle

Candle is a minimalist machine learning framework for Rust with a focus on performance (including GPU support) and ease of use. It aims to provide a performant and user-friendly alternative to existing Python-based machine learning frameworks for developers who prefer Rust's syntax and performance benefits.

One use case is MINST classification.

Cloud AI platforms

• GCP Vertex AI
• Azure Machine Learning

Phased out frameworks

As the field of ML/AI evolves, new techniques and algorithms are constantly being developed. If a framework cannot keep up with these new developments, it will become less competitive and less useful for solving real-world problems.

• Turi Create

Turi Create was sponsored by Apple, if familiar with Mac OS, then Turi Create is much easier to code and export models to CoreML for use in iOS, macOS, watchOS, and tvOS apps. It has been archived since 2020.

• tensorflow_macos

It was also archived by Apple, and it only supports TensorFlow up to v2.5.

It is important to be aware of the potential risks of using a framework that has not been actively developed or that is not compatible with the latest versions of the software you depend on.

Databases for Training

• kaggle
• UCI Machine Learning Repository
• BigQuery public database

NLP and Generative AI Resources

• ChatGPT
• Meta AI Llama 2
• Google Gemini
• Google Bard

To solve real problems, we must combine using these LLM tools, as each tool has strengths and weaknesses.

NLP and LLM Training and Deployment

huggingface is to democratize good machine learning. They offer a variety of tools and resources for machine learning, including models, datasets, and spaces, specifically in NLP and LLM re-training and model sharing.

Here are some of the specific things that Hugging Face does:

• Provide a platform for sharing and using machine learning models. This platform, called Transformers Hub, allows developers to upload their models and share them with others. It also makes it easy for users to find and use relevant models.

• Develop and maintain many open-source machine learning libraries such as Transformers and Datasets.

• Host plenty of events and workshops about machine learning. These events are a great way for developers to learn about new techniques and tools and to network with other people in the field.

Overall, Hugging Face is a valuable resource for anyone who is interested in machine learning. They are committed to making machine learning more accessible and easier to use, and they are making a real difference in the field.

Related Paper Searching

• Google Scholar
• arxiv-sanity

Impacts of ML/AI on Industry

ML and AI are rapidly transforming industries across the globe, bringing about significant changes and impacting various aspects of business operations. From healthcare to finance, manufacturing to retail, the applications of ML/AI are becoming increasingly pervasive, driving innovation and enhancing efficiency.

Here are some specific examples of how ML/AI is impacting industries:

• Manufacturing:

1. Predictive maintenance algorithms for preventing equipment breakdowns
2. AI-powered quality control for identifying and resolving defects in real-time
3. ML-driven supply chain optimization for reduced costs and improved efficiency
4. Shop floor overall equipment effectiveness (OEE) optimization

• Healthcare:

1. AI-powered diagnostics for early disease detection
2. ML-driven drug discovery for accelerated drug development
3. Personalized medicine for tailored treatment plans

• Finance:

1. AI-powered trading algorithms for optimized investment decisions
2. ML-driven fraud detection for secure financial systems, especially for cryptocurrency blockchain scam detection and tracking
3. Smart chatbots for 24/7 customer support
4. AI-powered credit scoring for informed lending decisions

• Retail:

1. AI-powered recommendation engines for personalized product suggestions
2. ML-driven demand forecasting for optimized inventory levels
3. Chatbots for personalized customer support
4. AI-powered pricing strategies for maximizing revenue

Industrial Insights on ML/AI

Industrial Careers in the Age of Machine Learning

ML/AI can improve our daily life efficiency, e.g., we can use Google SGE, generative AI in Search to summarize this article by TEGUAR as the following:

• This article examines machine learning's impact on the industrial sector and explains its different types, including supervised, unsupervised, and reinforcement learning.

• Machine learning is already being used in various ways in the industrial sector, including predictive maintenance, predictive quality, and employee training.

• As machine learning becomes more common, it will change the types of jobs available and the skills needed to perform them.