Artificial intelligence (AI) and machine learning (ML) are becoming more widespread. According to McKinsey, 44% of businesses report that AI helped them save on costs. Sales and marketing teams are also seeing tangible benefits from AI and ML. As a result, AI and ML job listings on LinkedIn have grown by a whopping 650%. As demand for AI and ML increases, tech giants need to give developers tools to build solutions. To succeed, they need to know what software and tools to use. It can be the difference between a functioning AI algorithm, and a failure.
You don’t need to spend a lot on AI or ML tools. You have many options for open source tools. Still, make sure your chosen open source tool works with all your stack.
Let’s look at the top 6 open source tools in the AI/ML space.
1. TensorFlow
Google’s TensorFlow is a free, end-to-end platform for machine learning. It’s one of the most used, well-maintained frameworks for ML. It’s easy to deploy across multiple platforms. It also works for large-scale and numerical machine learning. What’s more, its framework helps you develop neural networks and other computational models.
TensorFlow helps complete AI and ML in one solution. It supports many languages, including JavaScript. This is a rare sight in the open source AI space. TensorFlow also provides platform-agnostic ML production. That means you can use it on the cloud, in-browser, or on-device. Its sheer flexibility qualifies it for any production environment and in any language.
The TensorFlow team also released a new version, TensorFlow 2.0, in September 2019. It introduced the following changes:
- Library consolidation.
- GPU performance improvements.
- Cross-compatibility between trained models on different TensorFlow versions.
- TensorFlow Eager. It’s an automatic differentiation scheme. It’s updated from a static computational graph to a “Define-by-Run” scheme.
2. PyTorch
PyTorch is a Torch-based machine learning library. It improves upon torch’s foundational framework. It’s a fast learning framework. It has a Python-based interface for AI development. It also has a C++ interface.
PyTorch provides different optimization algorithms for building neural networks. PyTorch uses the Autograd Module to build these networks. It provides a production-ready environment powered by TorchServe. That enables quick model deployment.
Pytorch has a specific USP. It’s supported by all major public clouds for flexible developments. This allows you to download software libraries from relevant application marketplaces. You can also start without leaving your present cloud-based development environment.
PyTorch boasts an expansive range of use cases. These include computer vision, natural language processing (NLP), and language translation.
PyTorch Live, a new tool, is a set of tools that help build mobile AI-powered experiences easier. These tools support both Android and iOS platforms, using JavaScript. That eliminates the need to write the same thing in two languages. It also allows mobile application bootstrap.
3. MLflow
MLflow is a machine learning lifecycle management platform. It covers the complete machine learning cycle, from development to production. This streamlines the ML process and packages code into reproducible runs. MLFlow also tracks projects and experiments. It shares and deploys models. It also maintains a central model registry.
MLflow is written in Python and JavaScript. It also works with any ML library. What’s more, it works with any language, framework, deployment tool, or algorithm. MLflow runs the same way in any cloud. By design, it can scale from one user to large organizations. MLflow can also deploy ML models in different serving environments.
PyTorch announced its integration with MLflow in November 2020. This merger offered PyTorch developers an end-to-end AI solution with MLflow. It provided lightning, logging, loading, and TorchScript models. PyTorch models were also added to TouchServe for better performance.
4. NumPy
NumPy is a library for the Python programming language. It’s a well-known general-purpose array-processing package. NumPy is the ML stack’s base. It performs different numerical calculation types. You can also process multi and single-dimensional array elements with it.
NumPy is interoperable: it supports an extensive range of computing platforms. It works well with GPU, distributed, and sparse array libraries. It’s also accessible for programmers with any experience level.
NumPy’s uniqueness lies in its powerful N-dimensional array. This allows for homogenous object typing. It also helps define arbitrary data and integrates with most databases.
Early last year, NumPy added two exciting new features. The first is type annotations for large parts of NumPy. The second was a new submodule. That submodule contains ArrayLike and DtypeLike aliases. These allow users to add type annotations in their code.
5. Keras
Keras is a library used for neural networks and machine learning. It supports convolutional and recurrent neural networks. It’s written in Python and runs on CPUs and GPUs. It’s modular and user-friendly. It also has fast experimentation with deep neural networks.
The library works with many neural network building block implementations. This includes objectives, optimizers, layers, and activation functions. It provides industry-strength scalability and performance. It also provides a Python frontend.s That makes it really beginner-friendly.
Keras also makes working with images and text data easier. This further simplifies writing Deep Neural Network (DNN) code. It provides a random network architecture. That allows you to develop any deep learning network.
In December last year, the Keras team announced new developments in ‘Keras for R‘. This package would give Keras an R interface. That allows us to leverage Keras’ power in R.
6. Pandas
Pandas is a popular Python library. It contains many in-built functions. These allow you to clean, transform, visualize, manipulate, and analyze data. It provides high-performance data manipulation in Python. Pandas can work on both relational and labeled data. Relational data is a series of data points. These relate to each other to form a structure. Usually, relational data comes in a table with columns and rows. Labeled data is somewhat unstructured. It uses labels and tags to organize data.
Pandas is stable and provides optimized performance when needed. Pandas uses two main data structure types: The ‘Series (1-dimensional)‘ and ‘DataFrame (2-dimensional)‘. This combination is a great feature. It helps Pandas handle the data requirements and use cases from most sectors.
Final Thoughts
AI and ML tools are evolving quickly. Using the best ones can help you enjoy their scalability and flexibility. If you’re looking for the leading open source AI and ML tool, Tensorflow is what you need. If you’re a Python loyalist, PyTorch and MLflow are a good choice. Numpy is also another capable Python alternative. Keras excels at neural networks, and Pandas is great for those looking for something close to R or Excel. The better choice comes down to your unique software development needs.
FAQs
Why are the top AI and ML tools open source?
AI and ML started in the academic arena. Open source projects help grow the technology. Eventually, the main goal was academic knowledge. No single vendor has been able to reach the open source community’s scale. Instead, vendors integrated these solutions in their software stack and hardware provisioning.
Which programming language is best suited for AI and ML?
The Python-based open source AI and ML tools eclipse other programming languages. Python is also a high-level, easy to use programming language. That makes it very useful for novices. Yet, as AI needs more hardware optimization, low-level programming languages are more efficient. These include languages like C. Unfortunately, they’re also too time-consuming to code with for most production environments. No matter what language you use, it’s a compromise.
Which is the most popular AI and ML tool today?
TensorFlow is the most popular open source AI and ML tool. It allows developers to use containers easily. These allow developers to quickly move projects to hardware running AI compatible SDK tools. SMEs can also leverage this technology to repurpose hardware for service-based B2B offerings.
Which AI and ML tools are Python-based?
PyTorch, MLflow, and NumPy are some of the most used Python-based AI and ML tools.
Which tool is a great option for analyzing labeled data?
Pandas is great for analyzing tagged or labeled data.
Resources
Become a Data Scientist
Read this article about how to become a data scientist here!
TensorFlow’s Popularity
Here are 8 reasons developers love TensorFlow.
Learn More About TensorFlow
Everything you need to know about TensorFlow is right here.
W3Schools ML Course
A free course from W3Schools to get started with AI and ML using Python.
AI for Cybersecurity
Want to use AI within your business to help cybersecurity, check out this article here.
Those are all libraries and as such not complete usable solutions some not even with GUI’s. Made from phD’s for phD’s. Implementing those into work-able usable applications is a BRAINER.