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HONEYPOTZ INC.
Dover, United States
B
11-50 Employees
2016
Key takeaway
The company emphasizes its commitment to supporting organizations in their transition to MLOps, offering a range of machine learning and MLOps tools through its AI STUDIO.
Reference
Product
AI STUDIO - Machine Learning and MLOps tools
CapeStart
Cambridge, United States
B
11-50 Employees
2013
Key takeaway
CapeStart offers comprehensive MLOps services that facilitate the development, deployment, and management of machine learning projects. Their experienced team ensures efficient and scalable data science solutions, enabling businesses to enhance their AI capabilities and achieve their objectives effectively.
Reference
Service
MLOps Services - CapeStart
Develop, deploy, maintain, and scale data science and ML projects faster and keep them running smoothly with our end-to-end MLOps services.
Arrikto
United States
B
11-50 Employees
2014
Key takeaway
Arrikto focuses on applying DevOps principles to the machine learning lifecycle, treating 'Data as Code' to enhance MLOps practices. Their Enterprise Kubeflow platform significantly accelerates the deployment of machine learning models, making it a preferred solution for many Fortune 500 companies.
Reference
Core business
Arrikto MLOps | Scalable Machine Learning Models, Delivered.
Arrikto’s Enterprise Kubeflow distribution is a complete MLOps platform that reduces costs, while accelerating the delivery of scalable models from laptop to production.
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Datatron
San Francisco, United States
B
11-50 Employees
2016
Key takeaway
Datatron is an enterprise MLOps platform designed to streamline the deployment, management, and monitoring of machine learning models at scale. With its automation features and centralized approach, Datatron enhances the efficiency of MLOps practices, allowing teams to focus on building models and delivering business value.
Reference
Product
Enterprise MLOps Platform by Datatron
The enterprise MLOps AI platform that delivers revenue at scale by rapidly and reliably helping you operationalize, monitor, and govern your AI Models.
Neuro Inc.
San Francisco, United States
B
11-50 Employees
2019
Key takeaway
Neuro specializes in enterprise AI transformation, offering a comprehensive MLOps platform that automates the entire machine learning pipeline from data collection to deployment. Their team of expert researchers and engineers ensures efficient management of infrastructure and processes, addressing key challenges in ML development.
Reference
Core business
MLOps platform | Neuro
Neuro MLOps platform provides complete solution and management of the infrastructure and processes you need for successful ML development at scale.
AIron
Boston, United States
B
11-50 Employees
-
Key takeaway
The company emphasizes its expertise in MLOps, highlighting its role in enabling AI transformation through the continuous integration and delivery of machine learning models. They offer consultancy services and support for deploying and monitoring ML models in production, ensuring high quality and security.
Reference
Core business
airon - Machine Learning Operations (MLOps)
Monitor ML
Berkeley, United States
B
1-10 Employees
-
Key takeaway
The ML Ops community offers valuable resources and discussions about MLOps, emphasizing the importance of data quality and monitoring in machine learning models.
Reference
Core business
Monitor ML: Arize AI
H2O.ai
Mountain View, United States
B
251-500 Employees
2012
Key takeaway
H2O.ai offers MLOps solutions that enable the operation of AI models with transparency and scalability, streamlining performance monitoring and facilitating rapid adaptation to changing conditions. Their goal is to make AI technologies accessible, empowering users to deliver innovative solutions effectively.
Reference
Product
H2O MLOps - Operate AI Models with Transparency and Scale
Mission Automate
Raleigh, United States
B
1-10 Employees
2017
Key takeaway
The company specializes in developing optimized MLOps pipelines that enhance machine learning processes, leveraging over 25 years of experience in AI development. Their focus on high-quality, agile methodologies ensures effective management of the machine learning lifecycle, from training to production.
Reference
Core business
Home | Mission Automate
AI and ML | Mission Automate | California
Reliam Inc.
Los Angeles, United States
B
11-50 Employees
2011
Key takeaway
Mission Cloud offers comprehensive MLOps services that equip customers with the necessary tools and expertise to deploy, manage, govern, and secure their machine learning models in production. Their focus on simplifying the adoption and management of AWS workloads directly supports organizations in enhancing their MLOps capabilities.
Reference
Product
Mission Machine Learning Operations (MLOps)
Mission MLOps services provide you with all the capabilities and technologies required to deploy, manage, govern, and secure your ML models in production.
Technologies which have been searched by others and may be interesting for you:
When exploring the MLOps industry in the United States, several key considerations emerge. The regulatory landscape is crucial, as compliance with data privacy laws such as the GDPR and CCPA can impact how companies handle data. Challenges include the integration of machine learning models into existing workflows, ensuring model accuracy, and addressing scalability issues. The MLOps sector presents significant opportunities, particularly in automating machine learning processes, which can enhance productivity and reduce time to market for AI solutions. Environmental concerns are gaining traction, especially regarding the energy consumption of large AI models; companies are increasingly focused on developing sustainable practices. The competitive landscape is dynamic, with numerous startups and established tech giants vying for market share, leading to rapid innovation. Additionally, the global relevance of MLOps cannot be overlooked, as organizations worldwide are adopting machine learning technologies, creating a demand for robust operational frameworks. Understanding these factors will help individuals navigate the MLOps industry effectively, identify potential employers, and assess career prospects in this evolving field.
Some interesting numbers and facts about your company results for MLOps
| Country with most fitting companies | United States |
| Amount of fitting manufacturers | 3036 |
| Amount of suitable service providers | 2428 |
| Average amount of employees | 11-50 |
| Oldest suiting company | 2011 |
| Youngest suiting company | 2019 |
Some interesting questions that has been asked about the results you have just received for MLOps
What are related technologies to MLOps?
Based on our calculations related technologies to MLOps are Big Data, E-Health, Retail Tech, Artificial Intelligence & Machine Learning, E-Commerce
Which industries are mostly working on MLOps?
The most represented industries which are working in MLOps are IT, Software and Services, Other, Consulting, Marketing Services, Finance and Insurance
How does ensun find these MLOps Companies?
ensun uses an advanced search and ranking system capable of sifting through millions of companies and hundreds of millions of products and services to identify suitable matches. This is achieved by leveraging cutting-edge technologies, including Artificial Intelligence.
