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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
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)
MLPro
San Francisco, United States
B
1-10 Employees
2021
Key takeaway
MLPro specializes in transforming Matlab code into production-ready solutions, leveraging MLOps to ensure that your projects are enterprise-grade. Their managed service streamlines the process, allowing users to easily upload and compile code for efficient deployment.
Reference
Core business
MLPRO - Productionize Matlab with MLOps
MLPro turns your Matlab code into enterprise-ready production grade Matlab Container solutions with MLOps. .
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omega|ml
-
1-10 Employees
2018
Key takeaway
Omega-ML offers a comprehensive MLOps solution that streamlines the deployment of machine learning pipelines, enabling teams to deliver data products quickly and efficiently. With features like experiment tracking, production monitoring, and instant deployment, it addresses the challenges of traditional ML operations by treating models as data, ensuring consistency across workflows.
Reference
Core business
About | omegaml
Intellekt AI LLP
Vadodara, India
D
1-10 Employees
2021
Key takeaway
Intellekt AI offers MLOps consulting and services, focusing on implementing end-to-end pipelines with the best technologies and software tailored for businesses. Their mission is to optimize AI's impact, enhancing productivity and unlocking new possibilities in the digital landscape.
Reference
Service
MLOps Consulting
We provide MLOps consulting and services for implementing end-to-end pipeline using technologies and software best suited for the business.
Akoios
Madrid, Spain
A
1-10 Employees
2018
Key takeaway
The company provides products and services designed to integrate Machine Learning into business strategies, highlighting their expertise in automating and enhancing processes through MLOps. Their team of engineers and data scientists is equipped to support deployments both On-Prem and in the Cloud, facilitating a seamless transformation for your business.
Reference
Core business
MLOps made easy | Akoios
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.
Winder.AI
Harrogate, United Kingdom
A
1-10 Employees
2013
Key takeaway
Winder.AI specializes in MLOps development, offering services that help businesses scale their machine learning workflows and deployments. With a focus on creating product-quality AI platforms, Winder.AI is well-equipped to support organizations in overcoming real-world challenges in AI implementation.
Reference
Service
MLOps Development - Machine Learning Operations Services
We've developed MLOps platforms for companies like Shell, Grafana, Neste, and Living Optics. MLOps helps you scale your ML workflows and deployments. Empower your data scientists with GitOps for ML!
MLOps World: Machine Learning in Production
Old Toronto, Canada
A
1-10 Employees
-
Key takeaway
MLOps World is an international community dedicated to enhancing the understanding and practice of deploying machine learning models into production environments. Through events and gatherings, the community focuses on best practices and strategies in MLOps, covering essential topics like version management, CI/CD, and model deployment.
Reference
Core business
Home — MLOps World
A Uniquely Interactive Experience2nd Annual MLOps World Conference on Machine Learning in Production. Join our community of over 9,000 members as we learn best practices, methods, and principles for putting ML models into production environments.Why MLOps? MLOps World will help you put machine learning models into production environments; responsibly, effectively, and efficiently. We’ll be covering topics such as Version Management CI/CD Architecture for Model Deployment Pipeline Scheduling Optimizations Feature Engineering Feature Store Design and Maintenance Effective Data/Machine Learning Strategies New Research And more!Come share your stories and join us June 14-17thCreated in collaboration with MLOps Community.
Neuromation.io
Tallinn, Estonia
A
11-50 Employees
2017
Key takeaway
Neuromation offers an MLOps platform designed to streamline the deployment of machine learning and deep learning projects, addressing the common challenges of infrastructure setup and management. Their end-to-end AI implementation services ensure that teams can focus on delivering new models efficiently, transforming AI projects from concept to production.
Reference
Core business
Enterprise AI Transformation. MLOps Services
More than an MLOps platform. Receive end-to-end AI implementation services. From adoption advisory to custom infrastructure setup, we can support you at every leg of your transformative journey.
Technologies which have been searched by others and may be interesting for you:
MLOps refers to the set of practices that aims to automate and improve the deployment, monitoring, and management of machine learning models in production. By integrating machine learning system development with IT operations, MLOps facilitates collaboration between data scientists and IT teams, ensuring that models can be deployed efficiently and scaled as needed. The process often involves continuous integration and continuous delivery (CI/CD) practices tailored for machine learning, allowing for rapid iterations and adjustments based on real-time data. This leads to enhanced model performance, reduced downtime, and a more streamlined workflow for managing the complexities of machine learning applications.
MLOps enhances machine learning workflows by streamlining the integration of machine learning models into the software development lifecycle. It enables teams to automate various processes, including model training, validation, and deployment. This automation reduces the time and effort required to move from experimentation to production, ensuring that models can be updated and improved continuously. Moreover, MLOps fosters collaboration among data scientists, engineers, and operations teams. By providing standardized tools and practices, it helps in maintaining consistency and quality across different stages of model development. Effective monitoring and management of models in production lead to better performance and quicker adjustments when needed, ultimately improving overall efficiency and effectiveness in machine learning projects.
1. Data Preparation
This component involves collecting, cleaning, and transforming data into a usable format. It ensures that the datasets are of high quality and relevant for training the machine learning models.
2. Model Training
During this phase, algorithms are applied to the prepared datasets to train models. This includes selecting appropriate algorithms, tuning hyperparameters, and validating model performance using techniques such as cross-validation.
3. Model Deployment
Once the model is trained and validated, it is deployed into a production environment. This step involves integrating the model into existing systems and making it accessible for real-time predictions.
4. Monitoring and Maintenance
After deployment, continuous monitoring is essential to ensure the model performs well over time. This includes tracking metrics, managing model drift, and retraining models as new data becomes available to maintain accuracy and relevance.
MLOps significantly improves model deployment and monitoring by streamlining the entire lifecycle of machine learning models. By automating deployment processes, MLOps minimizes human error and ensures that models can be released quickly and consistently across environments. This efficiency allows data scientists to focus more on model development rather than the intricacies of deployment. Additionally, MLOps provides robust monitoring capabilities that track model performance in real time. This continuous observation helps in identifying issues such as model drift or data quality problems, allowing for timely interventions. With integrated feedback loops, MLOps facilitates rapid iteration, ensuring that models remain effective and aligned with business objectives.
MLOps addresses several critical challenges in machine learning projects to enhance efficiency and collaboration. It streamlines the deployment and monitoring of machine learning models, ensuring that they operate consistently in production environments. By automating workflows, MLOps reduces the time and effort required for model training and deployment, thereby accelerating the delivery of AI solutions. Additionally, MLOps tackles issues related to scalability and reproducibility. Managing diverse machine learning environments can be complex, but MLOps provides frameworks that facilitate consistent experimentation and model versioning. This enables teams to track changes, reproduce results, and manage dependencies effectively, ultimately leading to more robust and reliable machine learning applications.
Some interesting numbers and facts about your company results for MLOps
| Country with most fitting companies | United States |
| Amount of fitting manufacturers | 6904 |
| Amount of suitable service providers | 6443 |
| Average amount of employees | 1-10 |
| Oldest suiting company | 2013 |
| Youngest suiting company | 2021 |
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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
Who are Start-Ups in the field of MLOps?
Start-Ups who are working in MLOps are MLPro, Intellekt AI LLP
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.
