By Samuel Greengard

The term machine learning refers to a computational system that has the ability to ingest data, analyze it and spot patterns and trends. Generally considered a subset of artificial intelligence (AI), machine learning (ML) systems generate algorithms based on a set of sample data and then deliver predictions, without being expressly programmed to do so. Moreover, these algorithms change and adapt as new data appears or conditions change.

This autonomous learning capability is at the center of today’s enterprise. It’s increasingly used to make important decisions and drive automation. Although ML is closely related to statistical analysis and data mining—and there are often overlaps across these disciplines—what sets ML apart is the ability to spot patterns, trends and properties that would otherwise go unnoticed or remain out of reach. Typically, ML typically focuses on known knowledge and ways to put it to use more effectively.

How to Choose the Best Machine Learning Software

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

One of the most important decisions revolves around the underlying machine learning framework, which typically involves one of four approaches:

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.

Sci-Kit Learn: A user-friendly and highly flexible open-source framework that nevertheless 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 framework include data ingestion methods, built-in design tools, version control, automation features, collaboration and sharing capabilities, templates and tools for building and testing algorithms, and the ability to select and change compute resources that build ML models, including CPUs, GPUs and APUs.

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.

Also see: Top AI Software 

Top 10 Machine Learning Software Platforms

Alteryx Machine Learning Platform

Machine Learning Platform Key Features: 

Alteryx has emerged as a leader in the machine learning space. It is designed to tackle extremely complex machine learning projects. The drag-and-drop platform incorporates highly automated ML for both data scientists and business domain users. It 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 has a large and active user community.

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 Lakehouse

Machine Learning Platform Key Features: 

The Databricks Lakehouse machine learning platform offers a centralized environment with powerful tools and features that facilitate machine learning. Managed ML flow relies on an open source platform to manage complex interactions across the ML lifecycle. Machine Learning Runtime delivers scalable clusters that include AutoML and other optimization frameworks. The platform also supports collaborative notebooks and a Feature Store that simplifies MLOps.

Pros

  • Offers powerful industry-leading capabilities and features build on open source technologies.
  • Delivers a highly scalable environment with excellent performance, in a framework that users generally find easy to use.

Cons

  • Pricey, and some users complain about subpar support.
  • Lacks compatibility with some commonly used AI/ML libraries. This may limit the effectiveness of the solution in certain situations.

Dataiku

Machine Learning Platform Key Features: 

The popular platform delivers all the tools required to build robust ML models, including strong data preparation features. An AutoML feature is designed to fill in missing values and seamlessly convert non-numerical data into numerical values. Dataiku incorporates leading algorithms and ML frameworks like Sci-Kit and XGBoost, along with widely used deep learning tools such as Keras and Tensorflow. Dataiku also supports custom modeling using Phyton and Scala.

Pros

  • Dataiku is among the most flexible machine learning platforms, and it delivers strong training features.
  • Offers powerful collaborative data science features.

Cons

  • The solution doesn’t offer support for several widely used ML tools, including JupyterLab and RStudio in docker containers.
  • Dataiku has a somewhat quirky and unconventional development process that can slow down model development.

Google Vertex AI

Machine Learning Platform Key Features: 

The platform taps 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 model development, and a low-code framework that typically results in 80% fewer lines of code. Google Vertex supports nearly all open source frameworks, including TensorFlow, PyTorch, and scikit-learn.

Pros

  • Despite powerful ML capabilities, the platform is fairly user-friendly, relatively easy to use and highly scalable.
  • It delivers strong integration 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.ai

Machine Learning Platform Key Features: 

The open source data science platform supports numerous areas of AI, including machine learning. AutoML is pervasive through the H2O AI Cloud. There’s support for numerous automation features, including feature selection, feature engineering, hyperparameter autotuning, model ensembling, label assignment and model documentation, and machine learning interpretability (MLI). H2O.ai offer powerful features specifically designed for Natural Language Processing (NLP) and computer vision.

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

Machine Learning Platform Key Features: 

KNIME 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 Server platform integrates with AWS and Azure, and it delivers a low-code visual programming framework for building and managing models. These include a robust set of data integration tools, filters and reusable components that can be shared within a highly collaborative framework.

Pros

  • Provides an intuitive graphical user interface (GUI) 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 computational resources.

MathWorks MATLAB

Machine Learning Platform Key Features: 

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 that includes feature selection, model selection and hyperparameter tuning. MATLAB supports popular classification, regression, and clustering algorithms for supervised and unsupervised learning.

Pros

  • The platform offers an array of powerful tools and capabilities within a straightforward user interface.
  • Extremely flexible, with excellent collaboration features and scalability.

Cons

  • Relies on a somewhat proprietary approach to machine learning. Lacks support for some open source components and languages.
  • Can be difficult to use for business constituents and other non-data scientists.

Microsoft Azure Machine Learning

Machine Learning Platform Key Features: 

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. Azure Machine Learning offers strong support for open source components and delivers tight GitHub integration. It includes 60 compliance certifications, including HIPAA and FedRAMP.

Pros

  • The drag-and-drop interface and low-code framework simplifies building ML models.
  • Offers excellent integration with open source components such as Python, Jupyter Notebooks and R.

Cons

  • The interface is not as user-friendly and as polished as other machine learning platforms.
  • Some users complain about subpar documentation and difficulties with support.

RapidMiner

Machine Learning Platform Key Features: 

The company promotes the idea of “intuitive machine learning for all” through both code-based ML and visual low-code tools. It includes pre-built templates for common use cases, and guided modeling capabilities. It also include robust tools for validating and re-testing models. RapidMiner focuses on MLOps and automated data science through several key functions, including an auto engineering feature and automatic process explanations.

Pros

  • Delivers nearly unmatched features and functionality that make the solution extremely popular.
  • Offers excellent and intuitive tools for non-data scientists, but also sophisticated support for data scientists.

Cons

  • Some users complain about a heavy use of computational resources when using RapidMiner.
  • Can be crash prone in certain situations and scenarios.

TIBCO Data Science

Machine Learning Platform Key Features: 

Tibco is an acknowledged leader in the data science field, including machine learning. It delivers sophisticated end-to-end ML capabilities through a framework that includes open source libraries and cloud-centric analytics. The machine learning solutions offers a robust set of features, including auto-data detection and interactive mapping, robust visualizations, automation and strong collaboration capabilities. The platform supports TensorFlow, SageMaker, Rekognition, Cognitive Services, and other tools to deliver orchestration through AutoML and Jupyter Notebooks.

Pros

  • Offers powerful features in a modular and highly scalable solution.
  • Excellent no code drag-and drop user interface that makes it suitable for business users as well as data scientists.

Cons

  • Limited options for libraries, visualizations and reporting.
  • Users complain that algorithm accuracy can sometimes be a problem.

Also see: Real Time Data Management Trends

Machine Learning Software: Vendor Comparison

ML Vendor Key Product Differentiators
Alteryx Machine Learning Platform Good interface and strong automation features
Databricks Lakehouse Excellent open source support and highly scalable
Dataiku Dataiku Highly flexible with strong collaboration features
Google Vertex AI User-friendly interface; highly scalable
H2O.ai H2O.ai Strong open source support and bias detection
KNIME KNIME Server Intuitive GUI and strong automation features
MathWorks MATLAB Powerful ML tools in a highly flexible framework
Microsoft Azure Machine Learning Powerful automation and low code interface
RapidMiner RapidMiner Excellent for data-scientists and business users
Tibco Data Science Powerful platform that’s highly scalable

Key Features for Machine Learning Software

While the goal is typically the same—solved 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. Among the crucial areas to examine:

How a package ingests and processes data. 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, others do not.

Support for Feature Engineering. This capability 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 support. Modern ML platforms typically support multiple algorithms. 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.

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. 

“Ensembling” Tools. Within ML, it’s common to rely on multiple algorithms to accomplish a single task. This helps “balance” strengths and weaknesses—and minimize the impacts of data bias. Ensembling refers to the process of integrating and using different algorithms effectively. 

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. As a result, it’s important to ensure that an ML platform offers automation tools and, for some situations, one-click deployment.

Dashboards and Monitoring. It’s essential to have visibility into the machine learning model, including algorithms, and understand how they are performing over time. That way, an organization can add, subtract and change ML models as needed.

Additionally, three primary types of machine learning exist:

Supervised learning, which builds a mathematical model based on both inputs and outputs from human-labeled training data.

Unsupervised learning, which operates on data without labels or classifications.

Semi-supervised learning, which falls between the previous two categories in terms of labels and classifications.

Machine learning systems frequently tap artificial neural networks, commonly referred to as “deep learning” systems, to provide the desired brainpower.

Most dedicated ML software connects to cloud platforms, which provide the processing power required to accommodate increasingly large volumes of data and complex tasks. However, machine learning is an increasingly common feature in a vast array of platforms, services and software used in the enterprise.

Also see: Top Data Visualization Tools 

Benefits of Machine Learning Platforms

Organizations that use machine learning effectively gain insights that aren’t possible through conventional data mining and analytics methods. These gains typically revolve around:

  • Greater personalization and customization
  • Improved forecasting
  • More effective competitive analysis
  • Faster detection of problems and potential risks
  • Improved process automation
  • A deeper insight into materials and interactions
  • Better staff training
  • Improved customer service and support

These gains often lead to new and improved products, time and cost savings, and various features that customers often find useful. The machine learning space is advancing rapidly. What’s more, today’s cloud-based platforms make it easier to embed ML in containers, microservices and across APIs—thus extending the reach and benefits of ML.

For example, machine learning systems can extend across business partnerships and supply chains. As a result, the technology is increasingly valuable for enterprise sustainability efforts and Environmental, Social, and Governance (ESG) initiatives.

Also see: Data Mining Techniques 

Machine Learning Use Cases

Use cases for machine learning are broad—and they continue to grow. ML is a valuable tool for government agencies, educational institutions and businesses. It can be used in numerous ways, including statistical regression, classification, clustering, natural language processing, audio and video processing, speech recognition and computer vision. This makes it a valuable addition to most digital tools and technologies, including automation systems, robotics, and the Internet of Things (IoT).

For instance, ML is commonly used in the retail industry to create recommendation engines, manage dynamic pricing and forecast demand. Similarly, it’s used by companies like Netflix and Spotify to generate recommendations. Banks, credit card providers and others use ML to address everything from fraud detection and risk analysis to dynamic trading and portfolio management. Healthcare providers and epidemiologists tap ML for more accurate patient diagnostics and understanding overall health trends, such as virus transmission during the pandemic or causal factors for cancer.

Organizations also use ML to personalize and contextualize marketing, better understand customer journeys and for cybersecurity. In many cases, ML can spot malware and other threats that are too complex for humans to identity or detect, or alert someone that a problem may exist before humans would normally notice an issue. For example, ML might identity an unusual pattern on a network—such as increased processor activity—that might indicate the presence of malware or a ransomware gang encrypting files.

Also see: Data Analytics Trends 

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