One of the biggest challenges of any AI and machine learning initiative is that what works in the lab often does not work in real life. Managing and maintaining AI models between the lab and production environments can be a long and time-consuming process that wastes resources and lowers faith in the efficacy of AI as a business strategy. dotData’s continuous deployment model makes operationalization of your data science initiatives fast, simple, and reliable.
Continuous AI & Machine Learning Deployment
AI Models That Update In Real Time
Integration as easy as one line of code
With dotData, deploying your AI and machine learning models is as simple as using a single line of code. Our API-based process lets you deploy models built in dotData quickly and with accuracy without having to worry about writing lengthy code or replicating data science development environments in production systems.
Increase ROI of AI initiatives
Measuring the return on investment of your AI initiatives is often directly associated with the pain experienced by the organization while your AI models were being “tuned” based on real-world data. dotData’s continuous AI integration makes it easy to adjust your AI models removing the pain and boosting the ROI from AI initiatives.
Update models with zero production impact
The reality of an AI initiative is that maximum value is achieved once your data science team has been able to modify machine learning models based on real-world usage. With dotData’s API-based integration, you can modify models and deploy changes without impacting production system performance. Seamless, continuous integration.
More than 90% of data science projects never make it past the lab. Download our free white paper: Data Science Operationalization and learn about the challenges with deploying ML and AI projects – and what you can do to get ahead of the problem. Learn how AutoML 2.0 solutions can help you get to production faster – with greater transparency.
The Right Product for the Right User
Start by selecting the product you need, based on your environment, your use-case and your need to “get dirty” with the details of your data science workflow.
AutoML 2.0 & Data Science Automation
Leverage a full GUI experience to automate as much of your data science workflow as necessary. Empower citizen data scientists and data scientists alike.
AutoML 2.0: Data Science Automation Accelerates Your Business
Scaling a data science practice is challenging, time-consuming, and expensive. With Automated Data Science, you can empower data analysts, software engineers, and BI professionals to build and benefit from predictive models. Through Data Science Automation, you can embed models into applications seamlessly, while freeing up the time of your data science team to be more productive.
BI & Data Analysts
Unlike traditional AutoML systems that require users with in-depth knowledge of data wrangling and constructing feature tables, dotData automates 100% of the data science process. With dotData and minimal training, your BI team and data analysts can quickly learn to contribute to your ML and Enterprise AI initiatives, freeing precious resources and accelerating time to market for your AI & ML initiatives.
Data scientists spend 80% of their time in wrangling with data and constructing complex feature tables. By automating the entire workflow, you can liberate your data science team from the mundane tasks associated with data science and give them the power to provide tangible value to your business in a scalable, seamless manner that is not possible with hand-coded approaches or traditional AutoML platforms.
IT & Software
Integrating your AI and Machine Learning models into production environments is a crucial step in deriving value from your Enterprise AI projects. dotData gives your IT and engineering teams a seamless API-based integration model that enables Continuous Deployment and makes deploying and maintaining models fast and straightforward.
Executives and Line of Business
Giving executives and line of business leaders insights into the Machine Learning and Enterprise AI process provides the transparency and line of sight needed to keep projects moving and provide discernible ROI and value for the organization.