The energy and utilities vertical includes energy, water, and oil and gas. It is one of the largest industries serving a huge customer base. The adoption of machine learning solutions is rapidly changing the operational and performance model of the energy and utilities industry. The need for new data sources, new programs, and efficient management of resources as well as increasing competition from alternative energy providers are the growth drivers for this market. The smart grid backed with machine learning capabilities has been trending in the energy and utilities sector lately. It gives access to the customer usage data that can be analyzed with the help of machine learning solutions to secure market competitiveness, increase energy efficiency, manage risks, and lower operational costs. Analytics solutions also enable oil and gas exploration companies to analyze the data about oil and gas reserves, which helps them to increase efficiency and lower the operational risks and costs.

Machine learning is helpful in this vertical in many cases, such as for finding new sources of energy, analyzing minerals in the ground, forecasting refinery sensor malfunctions, and restructuring oil delivery to gain cost effectiveness. The energy and utilities sector uses machine learning, majorly for power/energy usage analytics, seismic data processing, carbon emission, smart grid management, and others (customer-specific pricing and renewable energy management).

Frequently Asked Questions


Last updated on: Dec 14, 2019
  • IBM Watson machine learning software helps enterprises to use their data to create, train, and deploy self-learning models. It also helps users in building analytical models and neural networks. IBM data science experience is a cloud-based, social workspace that helps data professionals to consolidate, create, and collaborate across multiple open sources tools, such as R and Python.

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    • Enterprise
    • New York, USA
    • Founded: 1911
    • $50BN to $100BN
    • 1,00,001 to 5,00,000
  • SAS best machine learning software offers major features like automated model tuning, powerful data manipulation and management, flexible web-based programming environment, integrated text analytics, model assessment and scoring, and modern statistical, data mining, and machine-learning techniques. It facilitates the end-to-end data mining and machine learning process with a visual and programming interface. It also boosts analytics teams of all skill levels with a simple yet powerful and automated way to tackle all tasks in the analytics life cycle.

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    • Enterprise
    • North Carolina, USA
    • Founded: 1976
    • $1BN to $5BN
    • 10,001 to 15,000
  • Microsoft Azure Machine Learning is a fully managed machine learning software service for advanced analytics in the cloud. It enables enterprises to build advanced analytic web services quickly and eradicate much of the heavy lifting associated with deploying machine learning in modern data-driven applications.

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    • Enterprise
    • Washington, USA
    • Founded: 1975
    • More than $100 BN
    • 1,00,001 to 5,00,000
  • Google Cloud AutoML Machine Learning Software includes portable models, notebook developer experience, scalable service, managed service, HyperTune, and the ability to discover and share samples. The company's Advanced Solutions Lab (ASL) supports businesses to partner with Google Cloud and apply machine learning software to tackle high-impact business challenges. The solution offers a unique opportunity for technical teams to understand from Google’s machine learning software experts.

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    • Enterprise
    • California, USA
    • Founded: 1998
    • More than $100 BN
    • 75,001 to 1,00,000
  • Oracle Machine Learning allows data scientists, citizen data scientists, and data analysts to work together to discover their data visually and develop analytical methodologies in the Autonomous Data Warehouse Cloud. Oracle Machine Learning consists of complementary components supporting scalable machine learning algorithms for in-database and big data environments, notebook technology, SQL and R APIs, and Hadoop/Spark environments.

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    • Enterprise
    • California, USA
    • Founded: 1977
    • $10BN to $50BN
    • 1,00,001 to 5,00,000
  • The Dell EMC Machine Learning Ready Bundle with Hadoop builds on the power of tested and proven Dell EMC Ready Bundles for Hadoop, created in partnership with Cloudera and Hortonworks. The solution comprises an enhanced solution stack along with data science and framework optimization, so you can get up and running quickly. The solution also leverages DataRobot an advanced enterprise automated machine learning solution that encapsulates the knowledge, experience and best practices of the world’s leading data scientists, enabling you to quickly and easily build highly accurate Predictive models without previous coding and machine learning skills.

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    • Enterprise
    • Texas, USA
    • Founded: 1984
    • $50BN to $100BN
    • 1,00,001 to 5,00,000
  • H2O Sparkling Water permits users to combine the quick, scalable machine learning algorithms of H2O with the capabilities of Spark. Spark is an elegant and powerful general-purpose, open-source, an in-memory platform with tremendous momentum. H2O is an in-memory platform for machine learning that is reshaping how people apply math and predictive analytics to business problems. Integrating these two open-source environments provides a seamless experience for users who want to make a query using Spark SQL, feed the results into H2O to build a model and make predictions, and then use the results again in Spark.

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    • Enterprise
    • California, USA
    • Founded: 2012
    • $11MN to $50MN
    • 101 to 500
  • KNIME Analytics Platform is the open source software for creating data science. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and reusable components accessible to everyone.

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    • Startup
    • Zurich, Switzerland
    • Founded: 2008
    • Below $10 MN
    • 1 to 50
  • Dataiku DSS is designed to create potent machine learning models easy. It enables one to click through the interface for most use cases, whether one is an expert Data Scientist or a beginner. Dataiku makes it easy to leverage machine learning technologies and get instant visual and statistical feedback on model performance.

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    • Startup
    • New York, US
    • Founded: 2013
    • $11MN to $50MN
    • 101 to 500
  • RapidMiner Auto Model provides a complete solution on a unified platform that supports the entire Machine Learning workflow from data preparation through model deployment to ongoing model management. The quick-to-learn and easy-to-use workflow designer accelerates end-to-end data science for improved productivity. With the cutting-edge tools and innovative solutions that RapidMiner provides, insights can now be delivered on a scale and speed greater than was ever possible.

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    • Startup
    • Massachusetts, USA
    • Founded: 2006
    • $11MN to $50MN
    • 51 to 100
    • Enterprise
    • San Francisco, California, US
    • Founded: 2010
    • Below $10 MN
    • 51 to 100
  • Fractal Analytics enables to reveal valuable insights by accurately recognizing objects in images and videos. From surveilling people in real-time at events to detecting if products are in the right place in shopping aisles, AI can drive value in many ways. This helps in creating in-depth analyses by placing image objects into relevant segments. Fractal Analytics AI-based algorithms help insurers analyze home and auto damage to create more accurate claims for customers.

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    • Enterprise
    • California, USA
    • Founded: 2000
    • $11MN to $50MN
    • 1,001 to 5,000
  • TIBCO Software is AI-powered, search-driven experience with built-in data wrangling and advanced analytics. It connects the creativity of the entire team, citizens to experts.  Maintains transparency, security, version control, and audibility. It is capable to combine AutoML, intuitive drag-and-drop workflows, and embedded Jupyter Notebooks that make creating and sharing reusable modules easy. Enables to run workflows from Spotfire analytics to bring ML, data, processes, and people together to create operational solutions.

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    • Enterprise
    • California, USA
    • Founded: 1997
    • $1BN to $5BN
    • 1,001 to 5,000
  • Domino is a data science platform that allows data science teams to quickly develop and deploy models that drive ground-breaking innovation and competitive advantage. Domino automates DevOps for data science so that one can spend more time doing research and test more ideas faster. Enables automatic tracking of work for easy reproducibility, reusability, and collaboration.

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    • Startup
    • California, USA
    • Founded: 2013
    • $11MN to $50MN
    • 101 to 500
  • Teradata Vantage, an analytics platform that boasts a built-in machine learning engine provides a wide variety of descriptive, predictive and prescriptive analytics; autonomous decision making and visualization tools. Teradata Vantage is compatible with SQL, R, and Python, and can interface with visualization and BI tools like RStudio, SAS and Jupyter. Users can access and analyze all their data without having to learn a new tool or language, or worry about where data is located. This streamlined access is bolstered by Vantage's integration with popular third-party tools and analytic languages, meeting users where they are and allowing them to work in the environments they already know best.

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    • Enterprise
    • California, USA
    • Founded: 1979
    • $1BN to $5BN
    • 10,001 to 15,000

Machine Learning Software in Energy and Utilities Quadrant

Comparing 38 vendors in Machine Learning Software across 114 criteria.

Find the best Machine Learning Software solution for your business, using ratings and reviews from buyers, analysts, vendors and industry experts

EVALUATION CRITERIA

Below criteria are most commonly used for comparing Machine Learning Software tools.
  • Breadth and Depth of Product Offerings
    • Products/Solutions Offered
    • licenses
    • Services
      • Professional Services (Consulting & Training)
      • Managed services (Support & Maintenance)
    • Organization Size
      • Small and Medium sized Enterprise
      • Large Enterprises (Revenue> 500 Million)
  • Product Features and Functionality
    • cutomer satisfaction
    • Types of Offerings
      • Software Tools
      • Platform
      • Services
    • Product Features
      • Data visualization and exploration
      • Model Evaluation and Interpretation Tools
      • Modeling APIs
      • Machine Learning Algorithms
      • Model assessment and scoring
      • APIs for Batch and Real-time Predictions
      • Data Transformations
  • Focus on Product Innovation
    • Product Innovation
    • R&D Spend
    • New Product Developments
      • Product Upgradation
      • New Product Launches
  • Product Differentiation and Impact on Customer Value
    • Customer Feedback Frequency
    • Solution Scalability
  • Product Quality and Reliability
    • Level of Support
    • Customer Redressal Mechanism/Program
    • services
      • Technical Support
      • Customer Support
      • Sales Support
      • Others, please specify
    • Pre Sales Support
      • Software Requirement Specification (SRS)
      • Product Demos
      • Proof of Concept
      • Dedicated Account Manager (DAM)
    • Channel for Delivery of Support Services
      • On-Site Support
      • Remote Support

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Marie Stelle

Engagement Partner - 360Quadrants.com