Best Machine Learning Platforms Gather, Analyze, and Spot Trends & Patterns in Data

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Machine learning (ML) is the branch of artificial intelligence that empowers machines to learn from data. In nearly all industries, from healthcare to finance to manufacturing to government, ML is used to make important decisions based on historical data and automate processes.

If you’re also considering leveraging the great capabilities of ML to grow your business, you can use machine learning platforms.

ML platforms offer the suite of tools demanded by machine learning algorithms to be developed, deployed, and improved.

Here in this blog, we’ll discuss the top machine-learning platforms and how they can be used to meet a variety of needs.

So, let’s get started.

Best Machine Learning Platforms

1. MLflow

MLflow is an open-source machine-learning platform that supports the end-to-end machine-learning development lifecycle, including modeling, reproducibility, deployment, and a central repository.

MLflow primarily tracks the four main functions in the ML lifecycle such as:

• Keeping track of experiments to record and compare the parameters and results of the experiments with the help of MLflow tracking.
• Makes the machine learning code reproducible so that it can be run across platforms as a part of its MLflow project capabilities.
• MLflow models provide machine learning app developers to deploy ML models in diverse serving environments.
• Models are stored, annotated, discoverable, and managed in MLflow Model Registry.

Some of the world’s leading organizations including Microsoft, R Studio, Toyota, Zillow, Sk Telecom, etc. use MLflow to develop, train and deploy machine learning solutions.

2. Databricks Lakehouse

Databricks Lakehouse combines the great capabilities of the data warehouse and data lakes. With this integration, developers are able to provide the reliability, strong governance, and performance of data warehouses together with the openness, flexibility, and machine learning support of data lakes.

Databricks Lakehouse is built with low-cost storage and enhanced scalability to ensure the speed and ACID transactional integrity of data lakes. Having a single source of truth for all your data will also prevent you from experiencing stale, inconsistent data.

This machine learning tool is employed with versioning, reliable, and fault-tolerance transactions.

3. Alteryx

Alteryx offers a suite of AutoML products that enable data scientists to build advanced machine-learning modules with no code. Platforms like this rely on best-in-class machine learning models to assist organizations in accelerating results while maximizing trust across the organization. Furthermore, it makes use of democratizing advanced analytics & machine learning to upskill developers to come across the necessary needs more easily.

You can develop models for your business by detecting and synthesizing features in your data through deep feature synthesis.

4. Anaconda

Anaconda is a fully loaded platform for data scientists that combines the great capabilities of Python and machine learning in a single machine. Today, the platform can offer thousands of open-source packages and libraries that users can choose from. Machine learning and data science can be performed on Linux, Windows, and Mac platforms using the tool. The platform is explainable, repeatable, and free to help people achieve their goal of machine learning model development.

5. KNIME Analytics Platform

Knime is one of the most renowned open-source machine-learning platforms that offer data scientists a complete range of analytics tools to build, train and deploy machine-learning solutions.

Being an open-source platform means that the community is at the forefront of innovation. The low-code interface of the platform makes it easier for beginners to get started with the platform via a drag-and-drop-style graphical interface.

With the help of AI and machine learning, KNIME can blend data from any source and shape data, generate statistics, clean data, extract and select features, and visualize it in both classic charts and advanced charts.

6. BigML

BigML is the leading machine-learning platform that is simple to be explored by both novice and seasoned data scientists. Machine learning solutions that address real-world problems can be deployed on the platform for free. This platform has an interactive visualization and explainability for easy understanding. BigML provides developers with private deployments and rich tool sets that will continue to help people create and experiment with automated machine learning workflows.  It also serves as a collaborative platform to enable all members of organizations to effectively communicate with each other.

7. Iguazio

With Iguazio, machine learning pipelines can be automated end-to-end, simplifying development, accelerating performance, and facilitating collaboration. Implementing real-time serving pipelines, and developing scalable data pipelines and machine learning pipelines can be possible.  Iguazio can ingest data from any source whether it is structured or unstructured in real-time to improve the learning capabilities of machine learning models. The platform is capable of continually training and evaluating the ML models at the scale. The simplified integration of the platform with the Jupyter notebook ensures the quick deployment of the ML models to production in seconds with a few simple clicks. Considering the intriguing capabilities of Iguazio, it is one of the best ML platforms for beginner machine-learning solutions engineers.

8. TensorFlow

TensorFlow (TFX) is an array of tools and services for the development and deployment of machine learning pipelines. A configuration framework and shared libraries make it easier to define, launch, and monitor machine learning systems. With the help of TensorFlow, you can even host your machine learning pipelines on Google Cloud. Some of the world’s leading companies like Spotify, Airbus, Gmail, and OpenX are relying on TensorFlow to design scalable and high-performance machine learning tasks.

With TensorFlow, you can quickly train and deploy machine learning models irrespective of what programming language or platform you use. TensorFlow Extended (TFX) is capable of running a complete production pipeline; TensorFlow Lite is capable of operating on mobile or edge devices, whereas TensorFlow.js can work with JavaScript environments.

9. Kubeflow

The Kubeflow application provides you with the ability to prepare and manage docker containers for maintaining machine learning applications.

The open-source project simplifies the deployment of machine learning workflows by orchestrating and orchestrating the use of curated tools and frameworks.

The Kubeflow Pipelines platform deploys and manages end-to-end machine learning workflows easily and reliably. Every run generates detailed reports. Subsequent runs can also be scheduled and compared.

10. DataRobot

DataRobot is an augmented machine-learning platform that enables organizations to build and deploy ML models leveraging the great potential of AI.

By leveraging DataRobot’s Automated Machine Learning (AutoML) solution, any AI Creator at any organization can produce top-of-the-line models without sacrificing time or reliability.

With DataRobot, you can experiment, compare, and build the best model that meets your business requirements. It has built and ranked dozens of models for every AI use case and recommends the most accurate and fastest models.

Final Take

These are a few of the most popular machine learning platforms that can be used by a machine learning development company to build, train and deploy ML models. Each of the ML models is equipped with different features, therefore you need to choose the right model for your organization to implement the right solution.

Machine learning development companies can provide you with a detailed evaluation of your requirements and suggest the best platform that will meet your needs.

Frequently Asked Questions

1. What are the different use cases of machine learning?

Machine learning technology can have different use cases as mentioned below:

• Personalized shopping recommendations
• Fake reviews detection
• Image and speech recognition
• Virtual personal assistant
• Automated customer services
• Visual product search
• Smart home integrations

2. What are the different types of machine learning platforms?

Generally speaking, there are two types of machine learning platforms: cloud and on-premises. A vast array of ML platforms are hosted on the cloud to ensure their accessibility to large organizations regardless of their geographical location.