10 Best Machine Learning Platforms (2024)

eWEEK content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.

Machine learning (ML) platforms are specialized software solutions that enable users to manage data preparation, machine learning model development, model deployment, and model monitoring in a unified ecosystem.

Generally considered a subset of artificial intelligence (AI), machine learning systems generate algorithms based on training datasets and then deliver relevant outputs, often without expressly being programmed to produce the exact outcomes they drive.

The autonomous learning capabilities of AI and ML platforms are at the center of today’s enterprises. The technology is increasingly being used to make important decisions and drive automations that improve enterprise operations across disciplines. In recent years, ML technology has also formed the foundation for generative AI models, which are trained to generate new content through larger datasets and more complex ML algorithms.

With its range of relevant business use cases in the modern enterprise, machine learning platform technology has quickly grown in popularity, and vendors have expanded these platforms, capabilities and offerings to meet growing demands.

In this guide, we cover 10 of the best machine learning platforms on the market today, detailing their specific features, pros and cons, and any areas where they particularly stand out from the competition.

Best Machine Learning Software: Comparison Chart

Top 10 Machine Learning Software Platforms

Alteryx Machine Learning: Best for Citizen Data Scientists and Developers

Alteryx has emerged as a leader in the machine learning space for tackling extremely complex machine learning projects through an accessible interface. The drag-and-drop platform incorporates highly automated ML features for both experienced data scientists and less technical business users. Many users particularly praise this platform for its built-in Education Mode, which makes the no-code platform even easier to learn and adjust to your particular use cases.

The platform connects to an array of open-source GitHub libraries — including Woodwork, Compose, Featuretools, and EvalML — and handles numerous data formats and sources. Alteryx also offers powerful visualization tools and feature engineering tools as well as a large and active user community.

Pricing

Pricing information for Alteryx Machine Learning is only available upon request. Prospective buyers can contact Alteryx directly for more information and/or get started with the product’s free trial on either desktop or cloud.

Key Features

• Automated machine learning and feature engineering.
• Automated insight generation for data relationships.
• Built-in Education Mode for learning and optimizing ML development.
• Access to open-source packages and libraries in GitHub.
• No-code, cloud-based format.

Pros

• Offers strong data prep and integration tools along with a robust set of curated algorithms.
• Excellent interface and powerful automation features.

Cons

• Macros and APIs for connecting to various data sources can be difficult to set up and use.
• Some users complain about slow load and processing speeds.

Databricks Data Intelligence Platform: Best for Enterprise-Scale Data Management and Feature Engineering

The Databricks Data Intelligence Platform offers a centralized environment with powerful tools and features that facilitate machine learning and the data preparation work that goes into successful ML model developments.

Managed MLflow is one standout feature that relies on an open-source platform developed by Databricks to manage complex interactions across the ML lifecycle. This platform is particularly useful for organizations that want a combination of self-service and guided data management and feature engineering capabilities that work for data from disparate sources and in different formats.

Interested users can take advantage of the platform for data processing and preparation — including for generative AI and large language models — and to prepare data production pipelines. They can also register and manage models through the Model Registry feature. In addition, the platform provides users with collaborative notebooks, the Feature Registry, and the Feature Provider, all of which support feature engineering requirements and MLOps with a strong, big-data-driven backbone.

Pricing

The Databricks platform is available at no base cost; instead, interested users will sign up and then pay for the features and quantities they use on a per-second basis. Users with larger usage requirements may be eligible for committed use discounts, which work across cloud environments. If you have inconsistent or smaller usage requirements, you’ll need to pay per product and per Databricks Unit (DBU) used:

• Workflows & Streaming Jobs: Starting at $0.07 per DBU.
• Workflows & Streaming Delta Live Tables: Starting at $0.20 per DBU.
• Data Warehousing Databricks SQL: Starting at $0.22 per DBU.
• Data Science & Machine Learning All Purpose Compute for Interactive Workloads: Starting at $0.40 per DBU.
• Data Science & Machine Learning Serverless Real-Time Inference: Starting at $0.07 per DBU.
• Databricks Platform & Add-Ons: Information available upon request.

A 14-day free trial is also available with limited features.

Key Features

• Open lakehouse architecture.
• REST-API-driven model deployment.
• Pretrained and fine-tuned LLM integration options.
• Self-service data pipelines.
• Managed MLflow with experiment tracking and versioning.

Pros

• The open data lakehouse format makes it easier to work with data from different sources and for different use cases; users appreciate that the platform can scale for data orchestration, data warehousing, advanced analytics, and data preparation for ML, even for larger datasets.
• This is a highly scalable environment with excellent performance in a framework that users generally find easy to use; many features are built on open-source data technologies.

Cons

• Can be pricey, especially when compared to completely free and open-source solutions in this space.
• Some visualization features are limited and difficult to set up.

Dataiku: Best for Extensibility

Dataiku is a popular, user-friendly ML platform that delivers all the tools required to build robust ML models, including strong data preparation features. An AutoML feature is another great component of the tool that is designed to fill in missing values and seamlessly convert non-numerical data into numerical values. Its data preparation, visualization, and feature engineering capabilities are well-reviewed components of the platform, but where Dataiku really sets itself apart is its extensibility and range of integrations.

Users can easily integrate many of today’s top generative AI services and platforms, including from OpenAI, Cohere, Anthropic, and Hugging Face. A range of public and proprietary plugins are available through GUI-based code packages, and integrations are also available with leading DevOps and data science visualization frameworks. Dataiku also supports custom modeling using Python, R, Scala, Julia, Pyspark, and other languages.

Pricing

Four plan options are available for Dataiku users. Pricing information is not provided for the paid plans, though other details about what each plan covers are included on the pricing page. A 14-day free trial is also available for each of the paid plans listed below:

• Free Edition: $0 for up to three users and installation on your personal infrastructure. Other limited features are included.
• Discover: A paid plan for up to five users that includes more than 20 database connectors, Spark-based data processing, and limited automations. Pricing information is available upon request.
• Business: A paid plan for up to 20 users that includes unlimited Kubernetes-based computations, full automation, and advanced security features. Pricing information is available upon request.
• Enterprise: A paid plan that includes all database connectors, full deployment capabilities, an isolation framework, and unlimited instances and resource governance. Pricing information is available upon request.

Key Features

• Feature store and automatic feature generation.
• Generative AI platform integrations.
• White-box explainability for ML model development.
• Prompt Studios for prompt-based LLM model development.
• Public and proprietary plugins for custom visual recipes, connectors, processors, and more.

Pros

• Dataiku is among the most flexible machine learning platforms, and it delivers strong training features.
• Dataiku easily integrates and extends its functionalities with third-party DevOps, data science visualization, and generative AI tools, frameworks, and services.

Cons

• Dataiku has a somewhat unconventional development process that can slow down model development.
• Especially as the tool updates, some users have experienced difficulties with outages.

Also see: Best Data Analytics Tools

Vertex AI: Best for Model Organization and Management

The Vertex AI platform is a leading cloud-based AI and ML solution that taps into the power of Google Cloud to deliver a complete set of tools and technologies for building, deploying, and scaling ML models. It supports pre-trained custom tooling, AutoML APIs that speed up model development, and a low-code framework that typically results in 80% fewer lines of code.

It’s also a highly organized platform that gives users accessible tools to manage their models at all stages of development. For example, the Vertex AI Model Registry is available for users who want a central repository where they can import their own models, create new models, classify models as ready for production, deploy models to an endpoint, evaluate models, and look at ML models both at a granular level and in an overview format. Additionally, Vertex AI supports nearly all open-source frameworks, including TensorFlow, PyTorch, and scikit-learn.

Pricing

Pricing for Vertex AI is highly modular and based on the tools and services, compute, and storage you use, as well as any other Google Cloud resources you use for ML projects. We’ll cover the estimates for some of the most commonly used features below, but it’s a good idea to use the pricing calculator or contact Google directly for a custom quote that fits your particular needs:

• Generative AI (Imagen model for image generation): Starting at $0.0001.
• Generative AI (Text, chat, and code generation): Starting at $0.0001 per 1,000 characters.
• AutoML Models (Image data training, deployment, and prediction): Starting at $1.375 per node hour.
• AutoML Models (Video data training and prediction): Starting at $0.462 per node hour.
• AutoML Models (Text data upload, training, deployment, prediction): Starting at $0.05 per hour.
• Vertex AI Pipelines: Starting at $0.03 per pipeline run.

A free trial is available for Vertex AI as well, though only as part of a greater free trial for all of Google Cloud. The Google Cloud free trial gives all users $300 in free credits to test out the platform.

Key Features

• Model Garden library with models that can be customized and fine-tuned.
• Native MLOps tools, including Vertex AI Evaluation, Vertex AI Pipelines, and Feature Store.
• Custom ML model training workflows.
• Vertex AI prediction service with custom prediction routines and prebuilt containers.
• Vertex AI Model Registry for production-ready model deployment.

Pros

• Despite powerful ML capabilities, the platform is fairly user-friendly, relatively easy to use, and highly scalable.
• It delivers strong integrations with other Google solutions, including BigQuery and Dataflow.

Cons

• Vertex AI is not as flexible and as customizable as other ML platforms. It also lacks support for custom algorithms.
• Some users complain about the high price and limited support for languages beyond Python.

H2O-3: Best for R and Python Programmers

H2O-3 is the latest iteration of the open-source data science platform that supports numerous areas of AI, including machine learning. The platform is designed with numerous automation features, including feature selection, feature engineering, hyperparameter autotuning, model ensembling, label assignment, model documentation, and machine learning interpretability (MLI).

H2O-3 offers powerful features specifically designed for Natural Language Processing (NLP) and computer vision. R and Python programmers particularly appreciate this platform for its wide-ranging community support and easy download options that are compatible with the two languages.

Pricing

H2O-3 is a free and open-source solution that users can download directly from the vendor site or in AWS, Microsoft Azure, or Google Cloud.

Key Features

• Open-source, distributed, in-memory format.
• Support for gradient-boosted machines, generalized linear models, and deep learning models.
• AutoML-driven leaderboard for model algorithms and hyperparameters.
• Algorithms include Random Forest, GLM, GBM, XGBoost, GLRM, and Word2Vec.
• H2O Flow for no-code interface option; code-based options include R and Python.

Pros

• Excellent support for open-source tools, components, and technologies.
• Offers powerful bias detection and model scoring features.

Cons

• Some users complain about missing analysis tools and limited algorithm support.
• Overall performance and customer support lag behind competitors.

KNIME Analytics Platform: Best for Community-Driven ML Development

The KNIME Analytics Platform promotes an end-to-end data science framework designed for both technical and business users. This includes a comprehensive set of automation tools for tackling machine learning and deep learning. The KNIME platform delivers a low-code/no-code visual programming framework for building and managing models.

The platform includes a robust set of data integration tools, filters, and reusable components that can be shared within a highly collaborative framework. Speaking of collaboration, the KNIME community is one of the most active and collaborative open-source communities in this space. Users can additionally benefit from KNIME Community Hub, a separate software solution that allows users to collaborate with data science and business users from other organizations and review other users’ samples with few overhead limitations.

Pricing

KNIME is a free and open-source solution, though interested users may want to contact the vendor directly to determine if their particular use case will incur additional costs. The KNIME Analytics Platform can be freely downloaded on Windows, Mac, and Linux.

Key Features

• Open-source, low-code/no-code tooling.
• Drag-and-drop analytic workflows.
• Access to ML libraries like TensorFlow, Keras, and H2O.
• Workflow-building node repository and workflow editor.
• AutoML for automated binary and multiclass classification and supervised ML training.

Pros

• Provides an intuitive, low-code/no-code interface that makes it easy for non-data scientists and new users to build ML models.
• Delivers strong automation capabilities across the spectrum of ML tasks.

Cons

• Code-based scripting requirements through Python and R can introduce challenges for certain types of customizations.
• Some users complain that the platform is prone to consume excessive computational resources.

Also see: Top Data Mining Tools

MATLAB: Best for Supportive ML Apps and Trainings

MathWorks MATLAB is popular among engineers, data scientists, and others looking to construct sophisticated machine learning models. It includes point-and-click apps for training and comparing models, advanced signal processing and feature extraction techniques, and AutoML, which supports feature selection, model selection, and hyperparameter tuning.

MATLAB works with popular classification, regression, and clustering algorithms for supervised and unsupervised learning. And, despite its many complex features and capabilities, it is a relatively accessible tool that offers a range of detailed training and documentation to users, as well as accessible and easy-to-incorporate apps.

Pricing

MATLAB can be used by organizations and individuals of all different backgrounds and is sometimes used in combination with Simulink, a MATLAB-based environment for multidomain model programming. Multiple subscription options are available:

• Standard: $940 per year, or $2,350 for a perpetual license.
• MATLAB and Simulink Startup Suite: $3,800 per year.
• Academic: $275 per year, or $550 for a perpetual license.
• MATLAB and Simulink Student Suite: $99 for a perpetual license.
• Home/personal use: $149 for a perpetual license.

A 30-day free trial option is available for MATLAB, Simulink, and several other products.

Key Features

• Prebuilt MATLAB apps and toolboxes.
• Live Editor for scripting.
• Simulink for model-based design.
• Classification Learner App for data classification and training.
• Onramp, interactive examples, tutorials, and e-books for getting started with machine learning.

Pros

• The platform offers an array of powerful tools and capabilities within a straightforward user interface that is particularly friendly to advanced mathematical, research, and data science use cases.
• Extremely flexible, with excellent collaboration features, app integration opportunities, and scalability.

Cons

• Relies on a somewhat proprietary approach to machine learning. Lacks support for some open-source components and languages, which can also make the tool more expensive than other players in this space.
• Can be difficult to use for business constituents and other non-data scientists to get started, though the platform comes with extensive training options to bridge that gap.

Azure Machine Learning: Best for LLM Development

Automation is at the center of Azure Machine Learning. The low-code platform boasts 70% fewer steps for model training and 90% fewer lines of code for pipelines. It also includes powerful data preparation tools and data labeling capabilities, along with collaborative notebooks, which makes it a great one-stop shop for MLOps requirements.

As modern use cases for machine learning have drifted more and more toward generative AI, Azure Machine Learning has proven itself a leader in this type of ML model development. Users can track and optimize training prompts with prompt flow, improve outcomes with the Responsible AI dashboard, benefit from scalable GPU infrastructure, and work within a wide range of tools and frameworks.

Pricing

Similar to many other platforms in this space, Azure Machine Learning itself comes at no cost, but users will quickly rack up costs based on the compute and other Azure services they use. Pricing is highly variable for this tool, so we’ve only included estimates and starting prices for a few key compute options; prospective buyers should contact Microsoft directly for additional pricing information beyond what we’ve included here:

• D2-64 v3: Individual components range from $0 per hour to $2.67 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected. For this option and the ones below, many of these costs will be stacked on top of each other, depending on which instance you select.
• D2s-64s v3: Individual components range from $0 per hour to $3.072 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• E2-64 v3: Individual components range from $0 per hour to $1.008 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• M-series: Individual components range from $0 per hour to $26.688 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• H-series: Individual components range from $0 per hour to $2.664 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.

Discounted prices may be available for stable and predictable workloads through Azure Reserved Virtual Machine Instances. A free trial of Azure is also available.

Key Features

• Open-source library and framework interoperability.
• Responsible AI framework and dashboard.
• Prompt flow for AI workflow orchestration, including for LLMs.
• Data preparation and labeling.
• Drag-and-drop designer with notebooks, automated machine learning, and experiments.
• Managed endpoints for model deployment and scoring.

Pros

• The drag-and-drop interface and low-code framework simplify ML model building.
• Extensive LLM development and optimization features are available; the platform also benefits from Microsoft’s deep investment in generative AI and OpenAI in particular.

Cons

• The pricing structure is difficult to understand and can quickly get expensive
• Some users complain about subpar documentation and difficulties with support.

RapidMiner: Best for Cross-Disciplinary Teams

RapidMiner is an ML platform vendor that promotes the idea of “intuitive machine learning for all” through both code-based ML and visual low-code tools that non-technical team members can learn how to use. The platform includes prebuilt templates for common use cases, as well as guided modeling capabilities. It also provides robust tools for validating and retesting models.

RapidMiner focuses on MLOps and automated data science through several key functions, including an auto engineering feature and automatic process explanations. It is a highly collaborative platform with a project-based framework, co-editing capabilities, and built-in user authentication and access control features.

Pricing

A free version of RapidMiner, called RapidMiner Studio Free, is available for desktop users who require no more than 10,000 data rows and one logical processor. The enterprise version of the platform is a paid subscription; prospective buyers will need to contact RapidMiner directly for specific pricing information. All users can benefit from a 30-day free trial of the full platform, and discounts are available for certain groups, including academics.

Key Features

• Codeless model ops.
• Accurate and finance-based model scoring.
• Built-in drift prevention.
• Native dashboards and reports and integrations with BI platforms.
• User-level choice between code-based, visual, and automated model creation with logging for all options.

Pros

• A strong focus on administrative controls for governance, reporting, and user access.
• Offers intuitive, low-code/no-code tools for non-data scientists as well as sophisticated code-based tools for data scientists.

Cons

• Some users complain about the heavy computational resource requirements involved with using RapidMiner.
• Can be crash-prone in certain situations and scenarios.

TensorFlow: Best for MLOps

TensorFlow is an open-source machine learning software library that extends itself beyond this primary role to support end-to-end machine learning platform requirements. It works well for basic ML model development but also has the resources and capacity to support more complex model developments, including for neural networks and deep learning models.

Although TensorFlow rarely labels itself as an MLOps platform, it offers all of the open-source flexibility, extensibility, and full-lifecycle capabilities MLOps teams need to prepare their data, build models, and deploy and monitor models on an ongoing basis. TensorFlow Extended (TFX) is a particularly effective version of the tool for creating scalable ML pipelines, training and analyzing models, and deploying models in a production-ready environment.

Pricing

TensorFlow is a free and open-source tool, though additional costs may be incurred, depending on other tools you choose to integrate with the platform. The tool can be deployed directly on the web, on servers, or on mobile or edge devices.

Key Features

• Pretrained models in the model garden and TensorFlow Hub.
• On-premises, mobile-device, browser, and cloud-based deployment options.
• Simple ML add-on for Google Sheets model training and evaluation.
• Production-ready ML pipelines.
• Data preparation and responsible AI tools to eliminate data bias.

Pros

• Many other platforms, including those on this list, are compatible with TensorFlow and its software library.
• TensorFlow is known for its helpful and active user community.

Cons

• The models you can build within TensorFlow are mostly static, which may not be the most agile option.
• Many users have commented on how it’s more difficult to use and understand than most other Python-based software libraries.

Also see: Real-Time Data Management Trends

Key Features of Machine Learning Software

While the goal is typically the same — solving difficult computing problems — machine learning software varies greatly. It’s important to review vendors and platforms thoroughly and understand how different features and tools work. The following key features are some of the most important to consider when selecting machine learning software:

Data Processing and Ingestion

It’s important to understand how the software ingests data, what data formats it supports, and whether it can handle tasks such as data partitioning in an automated way. Some packages offer a wealth of templates and connectors, while others do not.

Support for Feature Engineering

Feature engineering is crucial for manipulating data and building viable algorithms. The embedded intelligence converts and transforms strings of text, dates, and other variables into meaningful patterns and information that the ML system uses to deliver results.

Algorithm and Framework Support

Modern ML platforms typically support multiple algorithms and frameworks; this flexibility is crucial. In some cases, dozens or hundreds of algorithms may be required for a business process. Yet, it’s also important to have automated algorithm selection capabilities that suggest and match algorithms with tasks. This feature typically reduces complexity and improves ML performance. Additionally, having access to a range of framework options gives users more agility when automating ML development tasks.

Training and Tuning Tools

It’s vital to determine how well algorithms function and what business value the ML framework delivers. Most users benefit from smart hyperparameter tuning, which simplifies the ability to optimize each algorithm. Various packages include different tools and capabilities, and, not surprisingly, some work better for certain types of tasks and algorithms. Especially with large language models and other larger ML models, you’ll want to identify tools that make training and fine-tuning easy, regardless of your particular use cases.

Ensembling Tools

Within ML, it’s common to rely on multiple algorithms to accomplish a single task. This helps balance out strengths and weaknesses and minimize the impacts of data bias. Ensembling refers to the process of integrating and using different algorithms effectively and is an important feature to look for in ML platforms.

Competition Modeling

Since there is no way to know how an algorithm or ML model works before it’s deployed, it’s often necessary to conduct competition modeling. As the name implies, this pits multiple algorithms against each other to find out how accurate and valuable each is in predicting events. This leads to the selection of the best algorithms.

Deployment Tools

Putting an ML model into motion can involve numerous steps—and any error can result in subpar results or even failure. To prevent these kinds of issues, it’s important to ensure that an ML platform offers automation tools and, for some situations, one-click deployment. Many top-tier tools also offer both experimental and production-focused deployment workflows and support.

Dashboards and Monitoring

It’s essential to have visibility into the machine learning model’s performance and how it works, including the algorithms that are running and how they are evolving to meet new needs over time. Dashboards and monitoring tools are particularly effective in this area, especially if they come with filters and visual elements that help all stakeholders review important data. Having this kind of visibility helps an organization add, subtract, and change ML models as needed.

Also see: Top Data Visualization Tools

Benefits of Machine Learning Platforms

Organizations that use machine learning platforms to develop their ML models can create models on a greater scale, at a greater speed, and with higher levels of accuracy and utility. Some of the most common benefits that come from using machine learning platforms include the following:

• End-to-end ML: Many platforms take an end-to-end approach and give you all the tools you need to manage the full ML development and deployment lifecycle.
• ML model organization: The unified platform makes it easier to organize, find, and retrieve new and old ML models.
• Flexibility and extensibility: Users can work with various frameworks, software libraries, and programming languages to produce a model that fits their needs.
• Features for ease of use: Low-code/no-code tools are often available to simplify model development, deployment, and monitoring.
• Automation capabilities: Automation workflows can be set up for various areas of the ML lifecycle, simplifying, standardizing, and speeding up the entire process.
• Scalable platform capabilities: Several platforms work with big-data ML training sets and goals, including for large language models.
• Governance and ethical considerations: A growing number of ML vendors are incorporating model governance, cybersecurity, and other responsible frameworks into their platforms to make ML modeling a more ethical and manageable process.

Also see: Data Mining Techniques

How to Choose the Best Machine Learning Software

While it’s possible to build a custom ML system, most organizations rely on a dedicated machine learning platform from an ML, data science, or data analytics vendor. It’s best to evaluate your organization’s needs, including the type of machine-learning technology you require, before making your selection. Consider whether your organization would benefit from a classical method or deep learning approach, what programming languages are needed, and which hardware, software, and cloud services are necessary to deploy and scale a model effectively.

Another of the most important decisions you can make revolves around the underlying machine learning frameworks and libraries you choose. There are four main options to consider in this area:

• TensorFlow: An open-source and highly modular framework created by Google.
• PyTorch: A more intuitive open-source framework that incorporates Torch and Caffe2 and integrates with Python.
• scikit-learn: A user-friendly and highly flexible open-source framework that delivers sophisticated functionality.
• H2O: An open-source ML framework that’s heavily slanted to decision support and risk analysis.

Other key factors to consider when choosing an ML platform include available data ingestion methods, built-in design tools, version control capabilities, automation features, collaboration and sharing capabilities, templates and tools for building and testing algorithms, and the quantity and variety of compute resources.

Throughout the selection process, keep in mind that most of today’s platforms offer their solutions within a platform-as-a-service (PaaS) framework that includes cloud-based machine learning software and processing along with data storage and other tools and components. Pay close attention to how much support is offered through this model and if any community-driven support or training opportunities are included to help you get started.

Also see: Top AI Software

Review Methodology

The platforms in this machine learning platform review were assessed through a combination of multiple research techniques: combing through user reviews and ratings, reading whitepapers and product sheets, considering the range of common and differentiating features listed on product pages, and researching how each tool compares across a few key metrics. More than 25 platforms were assessed before we narrowed our list to these top players.

eWeek chose the top 10 selections in this list based on how well they addressed key feature requirements in areas like advanced data processing and management, feature engineering, model training and fine-tuning, performance monitoring, and reporting and analytics.

Beyond key features, we also considered how well each tool would meet the needs of a wide range of enterprise user audiences, whether your primary user is an experienced ML developer or data scientist or a non-technical team member who needs low-code model-building solutions. Finally, we looked at the affordability and scalability of each tool.

Bottom Line: Selecting the Best Machine Learning Solution for Your Business

The right ML solution for your business may end up being a combination of multiple solutions, as different platforms bring different strengths to the table. Some of these tools particularly excel at preparing data for high-quality model development. Others provide the frameworks and integrations necessary to build the model. Still others offer recommendations and managed support to help you optimize existing models for future performance goals.

With so many of these tools not only integrating well with each other but also available in free and/or open-source formats, it may well be worth the time it would take to incorporate multiple of these leading tools into your existing machine-learning development strategies.

Read next: Top 9 Generative AI Applications and Tools