Mihail Eric
Jun 25, 20204 min
Updated: Aug 3, 2020
Nowadays it's hard to find a single industry where machine learning and data science aren't being used to improve productivity and deliver results. Indeed that is why people are so excited about the promise of artificial intelligence: it can be applied to so many diverse problem spaces effectively and it works!
In this post, we'll go over some impactful companies doing data science and machine learning today across various industries. This list has been aggregated after analyzing over 200 company descriptions, and we've broadly organized them by the problem domain being tackled and have included a brief description of their mission.
TLDR: A framework for providing data integrations and web interfaces for trained machine learning models.
TLDR: A lightweight experiment management tool that integrates into existing machine learning workflows
TLDR: A deep learning platform to help scale up training jobs and track experiments easily
TLDR: A tool for easily building web interfaces for data science and machine learning experiments in Python
TLDR: Helps organizations build out entire data science teams by handling screening and training
TLDR: An enterprise platform for helping companies automate the full machine learning pipeline starting with raw data all the way to production-level predictions.
TLDR: Provides tools to build end-to-end workflows for models, using visual interfaces and emphasizing explainability
TLDR: Builds a suite of problem-agnostic tools and libraries for machine learning and data science experiments
TLDR: Developing a machine-learning specific processor that promises faster training times and lower inference latency than existing offerings such as GPUs
Petuum
TLDR: Building a general purpose suite of artificial intelligence building blocks that enable enterprises to build their own vertical solutions relying on machine learning
TLDR: A fully-integrated and collaborative data studio that enables organizations to iterates on and develop date-driven solutions through tools for various stakeholders
TLDR: Building a data platform for AI that helps companies build high quality training and validation datasets for their applications
TLDR: Employs data storytelling to convert data analytics from structured formats to natural language output that can be more easily interpreted
TLDR: An enterprise search engine for analyzing and understanding business and financial data
TLDR: A platform employing natural language understanding and generation to create more relevant and impactful marketing campaigns
TLDR: A search engine optimized for litigation and legal document research
TLDR: Develops voice recognition and natural language understanding tools used by enterprises
TLDR: Building a suite of natural language processing SDKs for integrating recommendation systems, conversational managers, and user profiling into applications
TLDR: Leverages data science and natural language processing to build a chatbot for helping users with their personal finances
TLDR: Helps enterprises with product localization through their efficient, AI-powered machine translation platform
TLDR: Provides an enterprise API for performing various computer vision tasks
TLDR: A geospatial enterprise data platform that enables deriving insights in problems such as GIS mapping frequency, supply chain monitoring, and real estate due diligence
TLDR: Applying machine learning and big data to precision medicine, thereby helping physicians make data-driven decisions for personalized healthcare
TLDR: Employs machine learning and computational biology for early detection and precision intervention in cancer
TLDR: Employs big data and statistical analyses for drug discovery, biomarker development, and aging research
TLDR: Developing a platform for accelerated drug discovery that leverages machine learning and big data
TLDR: Develops medical imaging tools powered by AI to help improve the efficacy of radiologists in detecting illnesses.
TLDR: Leveraging machine learning and data science for various tasks like precision medicine, drug discovery, and molecular design
TLDR: Building a platform to help healthcare companies derive insights from structured medical language data
TLDR: Developing technology to monitor in real-time what is going on inside someone's heart via the use of electrographic flows
TLDR: Uses images of CT scans to automatically detect stroke threats in patients and alert relevant health teams
TLDR: Helps companies find appropriate candidates for patient trials by scanning and analyzing clinical records
TLDR: Provides a framework for quicker tagging and training of AI models for industrial and robotics applications
TLDR: Leverages technology such as drones, robots, and fixed cameras to help large-scale retailers automatically answer questions about item supplies and out-of-stock products
TLDR: Provides a 2D/3D perception platform for use in sensors of autonomous vehicles
TLDR: Uses machine learning to power an in-vehicle device that helps reduce traffic accidents due to distracted driving
TLDR: Develops autonomous vehicle technology including perception, planning and control, and HD mapping
TLDR: Using computer vision to build more efficient crop weed control and agricultural herbicide resistance
TLDR: Leverages computer vision to detect, analyze, and treat early signs of agricultural crop threats
TLDR: AI-powered meeting and email scheduling
TLDR: Builds sophisticated optical character recognition technology to extract insights from paper documents and forms
TLDR: Real-time monitoring of analyze and correlate company data for tasks like revenue monitoring, customer monitoring, and digital partners monitoring.
TLDR: An end-to-end data platform for deriving insights from internal data within an enterprise, focusing in particular on the financial sector
TLDR: Provides a platform for helping companies write more targeted and effective hiring content such as job postings, tailored toward building more diverse and inclusive work environments
TLDR: Develops a suite of fraud detection APIs to help businesses ensure the digital trust and safety of their products
TLDR: Uses machine learning to power various document and identify verification offerings
TLDR: Uses machine learning to detect cyberattacks that bypass existing security controls
TLDR: Utilizes machine learning to battle cybersecurity threats via endpoint detection of network threats
TLDR: Uses AI to improve infrastructure and devops at companies helping to improve monitoring tools and root cause analysis.
TLDR: An adaptive platform that uses machine learning to help improve the learning experience and techniques around remote education curricula
While 50 seems like a lot of companies, this only scratches the surface of all the groups out there tackling challenging problems with machine learning and data science and all the companies that will be formed in the future. If these companies deliver on their goals, artificial intelligence promises to be one of the biggest technological paradigm shifts in human history. The future looks bright!
If you're interested in preparing for a job in machine learning or data science, get in touch!
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