Top Machine Learning Toolkits for Python Developers

Last updated by Rishabh Dev Choudhary on Jul 25, 2024 at 06:47 PM | Reading time: 9 minutes

At 20 years of age, Python has established itself as one of the most frequently employed and versatile programming languages in the world. It's easy to learn, provides seamless integration with various database management systems, and integrates easily with several toolkits that are either used generally or used exclusively for the creation of machine learning algorithms.

This article provides information on top machine learning toolkits for Python developers.

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Importance of Machine Learning Toolkits

Before we explore top machine learning toolkits for Python developers, it’s important to consider factors that contribute to their significance:

● The availability of machine learning toolkits makes it possible to implement a machine learning algorithm without completely knowing its inner workings in complete detail.

● Machine learning toolkits speed up the processes because, with their presence, Python developers don’t require to rewrite every algorithm from their very basics making it possible to perform experiments at a much faster rate.

Also read: What is Machine Learning? A Comprehensive Guide

Differences Between Toolkits and Libraries

Before we look into top machine learning toolkits for Python developers, it’s important to understand the difference between toolkits and libraries. Often confused as the same, there are subtle differences between the two.

● A toolkit is a collection of related tools or utilities designed to solve specific problems, a library is a collection of pre-written code, functions, and classes used to perform specific tasks or provide specific functionality to programs.

● Toolkits are focused on specific tasks focused on data analysis, web development, or machine learning. On the other hand, libraries provide a range of functionalities and can be applied to various use cases. In other words, they are quite generic in nature.

● SkiPy is a toolkit that contains several libraries including NumPy, Pandas, and Matplotlib to name a few.

Top Machine Learning Toolkits for Python Developers

Having understood the importance of machine learning toolkits and their subtle but noteworthy differences with Python libraries, now let’s explore top machine learning toolkits for Python developers.

Microsoft Azure Machine Learning

Microsoft Azure Machine Learning is developed to help developers and data scientists manage a lifecycle of machine learning projects faster and more efficiently.

With machine learning operations (MLOps), open-source interoperability, and integrated toolkits, Microsoft Azure Machine Learning can speed up processes. It’s also highly secure and is designed to keep responsible AI in perspective.

Microsoft Azure Machine Learning: Notable Features

● The data preparation tools of Microsoft Azure Machine Learning can handle large datasets on a large scale using Apache Spark clusters, an open-source, distributed computing system.

● With Microsoft Azure Machine Learning developers can collaborate using tools like Jupyter Notebooks or Visual Studio Code.

● Microsoft Azure Machine Learning comes integrated with Designer, a drag-and-drop interface to build machine learning pipelines.

● To ensure the best and ethical outcomes are delivered to customers, with the use of Microsoft Azure Machine Learning, developers can perform thorough investigations into their models and continuously monitor them while they are in production.

● With managed endpoints, developers can separate the services running in a production environment with behind-the-scenes code, infrastructure, and services.

Microsoft Azure Machine Learning: Benefits and Drawbacks

● The machine learning processes and tasks can be run from anywhere in the cloud, or in a hybrid environment.

● However, there are resource limits related to endpoints and computation instances in the Microsoft Azure Machine Learning toolkit.

● Developers have less control over the toolkit as details and complexities are abstracted. So, they have no option but to follow the process provided by Microsoft.

Amazon SageMaker

Amazon Sagemaker is a cloud-based machine learning platform launched in November 2017. It allows developers to create, train, and deploy machine learning platforms over the cloud.

Developers use a broad set of tools including notebooks, debuggers, profilers, pipelines, MLOps, and many more to deploy ML models in a single integrated environment (IDE) on embedded systems and edge-devices.

Sagemaker provides tools related to the management of various access components of your machine-learning projects. This includes setting roles and permissions to users related to viewing, modifying, or deploying models and datasets.

Amazon Sagemaker: Notable Features

● Amazon Sagemaker comes with Canvas, a no-code interface to create machine learning models. Amazon claims that even users with no machine learning or programming experience can deploy models using Canvas.

● Data Wrangler rapidly creates tabular and image data for machine learning.

● Clarify provides insights about accuracy, robustness, toxicity, and bias associated with the deployed machine learning models.

● Experiments help developers track and scale various iterations of their machine-learning experiment runs.

Also read: Mastering Data Wrangling Techniques: Cleaning and Preparing Messy Datasets

Amazon Sagemaker: Benefits and Drawbacks

● With Amazon Sagemaker, users have the advantage of deciding the IDEs they will use to deploy machine learning models. This includes the use of third-party frameworks such as TensorFlow, PyTorch, XGBoost, Scikit-learn, and ONNX.

● Costs incurred to process power, memory, and storage can increase when machine learning models are used for applications that work on significant user interaction.

Apache Mahout

Apache Mahout, is a product of Apache Foundation with which users can implement free-of-cost distributed and scalable machine learning algorithms. It is built on Apache Hadoop to let mathematicians, scientists, and statisticians rapidly build implementations on linear algebra framework and mathematically expressive Scala domain-specific language (DSL).

Apache Mahout: Features

● Apache Mahout comes with a range of machine-learning algorithms, including clustering, classification, recommendation, and collaborative filtering. These algorithms are optimized for distributed computing environments, making them suitable for big data applications.

● Apache Mahout can be integrated with other tools and libraries in the big data framework.

Apache Mahout: Drawbacks

To leverage the capabilities of Apache Mahout one needs to have in-depth knowledge about machine learning algorithms and frameworks.

Weka

Developed by the University of Waikato in New Zealand, Weka or Waikato Environment for Knowledge Analysis is a popular software suite used extensively for data mining operations,

Weka offers clustering techniques like k-means, EM (Expectation - Maximization), and hierarchical clustering, and supports association rule mining.

Weka: Notable Features

● Supports multiple platforms and functionalities including Native NVIDIA GPUDirect Storage, POSIX, NFS, SMB, and S3 access.

● When it comes to deployment options, the application is designed to be highly flexible and versatile.

Weka: Benefits and Drawbacks

● Applications built on Weka can be implemented on any computing platform.

● Navigating the platform is simpler with a graphical user interface.

● However, Weka doesn’t support distributed computing or big data processing. It also doesn’t support deep learning and reinforcement learning,

Vertex AI

Vertex AI is a fully-managed machine learning toolkit developed by Google. By using this tool, data scientists, data engineers, and machine learning engineers can develop, deploy, and scale machine learning models, while collaborating with each other through a common toolset.

With Vertex AI, developers can also customize large language models that users can utilize in their AI-powered applications.

Vertex AI: Notable Features

● With the AutoML feature users can train algorithms without writing code or preparing data splits.

● Vertex AI allows developers to prototype, customize, integrate, and deploy generative AI models in AI applications.

● The toolkit comes with specialized MLOps tools to automate, standardize, and manage machine learning projects.

Vertex AI: Benefits and Drawbacks

● Offers high scalability and performance with the use of Google’s Cloud Infrastructure.

● Integrates seamlessly with popular machine learning libraries like PyTorch and TensorFlow.

● Pricing is expensive and complex for small business enterprises and startups.

Also read: Top 10 Machine Learning Algorithms Engineers Need To Know in 2024

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FAQs: Top Machine Learning Toolkits for Python Developers

1. Is Python an ML Tool?

Python is not an ML tool but a programming language that uses ML toolkits. Python is very popular in the ML space because of its versatility, flexibility, and extensive library of frameworks and toolkits specifically designed for data analysis, machine learning, and artificial intelligence.

2. Is ChatGPT an AI Tool?

ChatGPT is an AI chatbot, which allows you to have human-like conversations to perform several tasks. It does a lot of tasks like creating images and graphics, drafting emails, replying to queries, composing lyrics, and a lot more.

3. How to Setup Google Vertex AI?

Follow these steps to set up a Vertex AI:

If you're new to Google Cloud, create an account to evaluate how Google’s products perform in real-world scenarios. New customers get $300 in free credits to run, test, and deploy workloads.
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.