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Machine learning is quickly becoming the standard in many industries, from self-driving cars to targeted ads. As organizations realize the business value of machine learning, tools are becoming more available to make your workflows more efficient.
With many options, selecting the best one that fits your project requirements can be time-consuming. Learn about the most popular and commonly used machine learning tools and discover which one is right for your next project.
Kubeflow is a free and open-source machine learning tool that deploys and manages an ML stack on Kubernetes. It includes TensorFlow models and APIs, a high-level API for building custom training and serving workflows, and tools for deploying ML workloads at scale.
Kubeflow makes it easy to build and manage machine learning (ML) models on Kubernetes. It provides a turnkey solution for deploying a model from inception to production. Kubeflow also provides a high-level API for building custom training and serving workflows and tools for deploying ML workloads at scale.
Metaflow is a user-friendly Python framework that helps data scientists and engineers manage, deploy and run their code in a production environment. It was initially developed at Netflix to boost the productivity of data scientists.
Now, it caters to other data scientists’ needs, whether solo practitioners or working with big teams. It has since become a user-friendly Python framework that helps data scientists and engineers manage, deploy and run their code in a production environment.
Vertex AI is a managed machine learning platform that provides you with all of Google’s cloud services in one place to deploy and maintain AI models.
Vertex AI lets you train and deploy machine learning models without coding experience. You can also perform deep learning on your data using CNNs, RNNs, or LSTMs. The platform also offers pre-trained models that you can use out of the box, such as ImageNet (Image Classification), FashionMNIST (Image Classification), and GoogLeNet (Object Detection).
It is the best tool for developers who want to deploy machine learning into production but do not have the time or expertise to manage their infrastructure.
SageMaker is a cloud-based machine learning platform built on Amazon’s decades of experience developing real-world ML applications. The machine learning tool provides an easy way to build, train, and deploy production-ready machine learning models. It has a rich feature set that includes a robust model management system, auto-scaling capabilities, and flexible pricing.
Another factor that makes it a crowd favorite is its easy-to-use drag-and-drop interface that enables developers and data scientists to create state-of-the-art machine learning models with minimal code. It also includes out-of-the-box integrations with popular tools like Apache Spark and TensorFlow, so you can quickly deploy your models into production.
Gradio is a fast, low-code, open-source web application for creating demos of machine learning models with a friendly user interface. Users can quickly create beautiful UI for machine learning models and allow user trials by dragging and dropping in their images, pasting text, recording their voices, and interacting with your demo.
The best thing about Gradio is that it bridges the gap between your data and ML-based decisions. It brings advanced analytics capabilities right into your business application. With Gradio, you can easily automate decision-making in many use cases, such as pricing, marketing, fraud detection, the discovery of new trends, and more.
New Relic is a cloud-based observability platform that primarily helps website and application owners track the performance of their services. Their new suite of machine learning tools gives developers visibility into how their algorithms perform in production, helping them make better decisions about how best to tune their models. New Relic has recently extended its full-stack observability to ML with model performance monitoring.
Aquarium is an ML data management system that enhances model performance by assessing a model’s data quality. The DataRobot team created the platform to make it easier for people to collaborate on data science projects. It is designed from the ground up with collaboration in mind, which means that all experiments are stored in one place and can be shared easily with others on your team (or across groups). This technology makes it much easier for multiple people on your team to work together on a single project without stepping on each other’s toes or having conflicting versions of code floating around the pipeline.
It utilizes a model-free approach, which does not require any model specification or training. Instead, users provide data, and the platform automatically extracts a set of features. These features are then used to train a random forest classifier that predicts each record’s quality (and, thus, usefulness) in the dataset.
Finding approximate nearest neighbors may be done using the C++ Python library called Annoy, which stands for Approximate Nearest Neighbors Oh Yeah. It uses the same algorithm as libkdtree but with a different data structure and a few modifications to the search algorithm. Spotify uses it for music recommendations, and it may be the platform you will use for your next ML project.
GitLab is primarily a DevOps software package but is also helpful for machine learning projects. Its top features include private cloud repositories, cross-functional collaboration, version tracking, automation, and agile planning. It can run on your servers, giving you complete control over your data. Many practitioners prefer GitLab because of its flexible plans—from free plans to enterprise packages that pack features like LDAP support, hardware monitoring, and more.
From the developer’s point of view, GitLab provides many useful features for free and offers unlimited private repositories at no cost. The only cost associated with GitLab is their premium support service or their extra features (like CI/CD pipelines).
Comet allows users to track, manage, visualize, and optimize models—from training runs to production monitoring in a single platform. Comet uses Jupyter Notebook as the default interface for all its features; however, it can also be used as an API-first solution.
Comet supports both Python and R programming languages, so that you can use either one depending on your preference or the requirements for your project. The language you choose will not affect Comet’s core functionality, only its user interface. For example: If you use Python in your project, the user interface will be a Jupyter notebook.
There are many advantages to using Comet. However, what makes Comet different from other ML platforms is its ability to bring all the components of an ML project together in one place—no more jumping from one tool to another as you go through the stages of building your model. Comet also features a library of over 500 customizable ML algorithms, deployable on virtual machines or your chosen infrastructure. These include linear regression, logistic regression, K-means clustering, nearest-neighbor classification, and more.
The competition surrounding machine learning tools is fierce, so it is important to understand options and weigh the pros and cons. Building successful models requires mastering ML tools, but not all machine learning tools are equal. Determine what you need most and narrow your options before choosing the best tool.
Use this list to make an educated decision on which machine learning tool to use. Check out the Comet blog for more insights into the latest machine learning news and trends.