Machine Learning Tools
What are Machine Learning Tools?
Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling. They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.
They can be used, for example, to build recommendation engines, predict search patterns, filter spam, build news feeds, detect fraud and security threats, and much more. There are four types of machine learning algorithms: supervised, unsupervised, semi-supervised, and reinforced.
Supervised algorithms are machine learning tools with training wheels. They require a person to program both the input and the desired output, as well as provide feedback as to the accuracy of the end results.
Unsupervised algorithms require very little human intervention by instead using an approach called “deep learning” to review massive banks of data and arrive at conclusions based on previous examples of training data; they are, therefore, generally used for more complex processing tasks such as image recognition, speech-to-text, and natural language generation.
Semi-supervised algorithms tend to fall in the middle ground.
Reinforced algorithms force models to repeat a process until it produces the most favorable outcomes. Attempts that produce these favorable outcomes are rewarded and attempts that produce unfavorable results are penalized until the algorithm learns the optimal process.
Machine Learning Tools' Features & Capabilities
Most vendors offering machine learning tools include:
Data collection and preparation
Training and application deployment
Some vendors also offer additional tools to
Store aggregated data on the Cloud
Build models with easy drag and drop capabilities
Access libraries with pre-modeled routines and functions
Machine Learning Tools Comparison
When beginning your process for a machine learning tool with which to get started, walk through the following flow of questions to help you narrow down your options:
- Am I looking for a managed machine learning platform? (Think time-and-cost efficiency.)
- Do I need a mobile-supported tool?
- For what scripting language do I need support? (R, Python, Java, C++, etc.)
- If you're on a budget, ask yourself: Can I effectively manage my training data with the storage space I have? (You may need to consider moving to the cloud, which is typically more expensive.)
- Do I need support for multiple model types? (Binary, regression, multi-class, etc.)
- Would I prefer having an extensive library with which to start?
- Are my projects expected to grow (and thus requiring a need for scalability)?
- Do I need a full cycle deep learning system?
Ready to start comparing your shortlist head-to-head? Start a Machine Learning Tools comparison
Many vendors offering machine learning tools will offer a free trial or a free version with a limited batch of predictions. There are several free and open-source machine learning platforms, like Google's Tensorflow, for developers on a budget. For paid plans, prices are generally Pay-As-You-Go, tiered by price per hour and training units, typically starting at $0.10/Unit/Hour. Deep learning capabilities are typically more expensive, at $1+/GPU/Hour.
RStudio is a modular data science platform, combining open source and commercial products. The vendor states their open source offerings, such as the RStudio IDE, Shiny, rmarkdown and the many packages in the tidyverse, are used by millions of data scientists around the world to…
IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies…
Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and…
Kira is software that excels at searching and analyzing contract text. Kira offers pre-built, machine learning models covering due diligence, general commercial, corporate organization, real estate and compliance. Using Kira Quick Study, anyone can train additional models that can…
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data…
Microsoft's Azure Machine Learning Studio is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.
Cloudera Data Platform (CDP), launched September 2019, is designed to combine the best of Hortonworks and Cloudera technologies to deliver an enterprise data cloud. CDP includes the Cloudera Data Warehouse and machine learning services as well as a Data Hub service for building custom…
AMIs are Amazon Machine Images, virtual appliance deployed on EC2. The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at scale. Users can launch Amazon EC2 instances pre-installed…
IBM Watson Machine Learning allows businesses to deploy self-learning models at scale, allowing AI to deployed in applications and available free to try, free for limited use (5 deployed models and 5,000 predictions per month), or at cost for high workloads priced per thousands of…
The Intel Deep Learning Cloud, or Intel Nervana, is a deep learning framework based on Nervana Systems' Nervana Cloud AI framework, with industry leading performance on GPUs thanks to its custom assembly kernels and optimized algorithms. Intel acquired Nervana Systems in 2016.