We offer Machine Learning as a Service (MLaaS) through our innovative Software as a Service (SaaS) tool. Harness the power of artificial intelligence and machine learning without the need for extensive technical knowledge or infrastructure. Our MLaaS solution empowers businesses of all sizes to leverage the potential of advanced analytics and predictive modeling.
What is Machine Learning as a Service?
Machine Learning as a Service (MLaaS) is a cloud-based model that enables organizations to access and utilize machine learning algorithms and models without the need for significant investment in hardware, software, or data science expertise. With MLaaS, you can leverage the capabilities of machine learning to extract valuable insights, automate processes, and make data-driven decisions.
Leverage AutoML Capabilities
Integrate AutoML into your software system, data workflows, and business operations and experience true automation.
The Advantages of MLaaS
By choosing our MLaaS solution, you unlock a range of benefits that can propel your business forward:
Simplified Implementation
Our SaaS tool eliminates the complexity of setting up and managing a machine learning infrastructure. You don’t need to worry about configuring servers, installing software, or maintaining hardware. We handle all the technical aspects, allowing you to focus on utilising machine learning for your specific business needs.
Cost-Effectiveness
Traditional machine learning implementation often requires substantial investment in infrastructure, software licenses, and skilled data scientists. With MLaaS, you only pay for the resources you use, making it a cost-effective option for businesses of all sizes. The cloud-based nature of our service means you can scale up or down as needed, optimizing costs and maximizing efficiency.
Rapid Development and Deployment
Our MLaaS tool provides a user-friendly interface and pre-built models, enabling you to quickly develop and deploy machine learning solutions. You can leverage a range of algorithms and techniques tailored to various use cases, from classification and regression to clustering and anomaly detection. This expedites the development process and helps you derive insights from your data in a timely manner.
Flexibility and Customization
While our MLaaS tool provides pre-built models, it also offers flexibility for customization. You can incorporate your own data, fine-tune models, and adapt them to your specific requirements. Whether you need predictive analytics, natural language processing, image recognition, or any other machine learning capability, our tool empowers you to create solutions that align with your business objectives.
Scalability and Reliability
As your business grows, our MLaaS tool scales effortlessly to accommodate increased data volumes and processing demands. We ensure high availability and reliability, leveraging the power of cloud computing infrastructure. You can rely on our robust and secure platform to handle your machine learning workloads without interruptions.
Finance and Banking.
Healthcare and Life Sciences
E-commerce and Retail
Manufacturing and Supply Chain
Marketing and Advertising
Energy and Utilities.
Industries We Serve
Our MLaaS solution caters to a wide range of industries, including but not limited to:
Engineering
Everyone, from amateurs to professionals in the field of data science, can find useful feature sets in Coredata AI’s feature store and incorporate them into their own feature engineering efforts. Our AutoML allows for the automatic generation and reduction of features, as well as the application of data type-specific handling strategies for things like feature selection, missing value handling, variable encoding, and rescaling. You can use the defaults as-is or easily adjust anything to suit your needs.
Auto Generated Models
To help data scientists and analysts construct and compare models, Coredata AI provides a guided methodology, in-built guardrails, and white-box explainability. When it comes to forecasting, clustering, and data-related tasks, our AutoML provides algorithms from top frameworks in an intuitive interface to help teams across the company produce the best possible results.
Customizable AutoML Capabilities
A custom Python algorithm can be added to the visual ML interface, or data scientists can develop models in Python, R, Scala, Julia, Pyspark, and many different languages using the APIs provided. Coredata AI records the specifics of these experiments and auto-generates project comparisons and detailed reports to guarantee that the team’s efforts outside of it are recorded and can be understood by the rest of the team. Coredata AI is the primary platform for model implementation, tracking, and control, regardless of where the model was created.
Verify & Evaluate the Project
Our AutoML has many tools for checking and rechecking models at every stage of the process. During the testing phase, data scientists can use tools like k-fold cross tests, automated diagnostics, and model assertions to ensure the validity of their work. Teams have the resources they need to adequately explain results and deliver dependable, accurate models through the use of a battery of online performance and explanation reports that include fairness and what-if analysis and stress tests.
Predictive Analysis
Coredata AI offers a set of advanced tools and solutions for in-depth analysis and forecasting, as well as tasks for data preparation such as resampling, imputations, and the extraction of extrema and intervals. Using the visual machine learning interface that we provide, business analysts and data scientists are able to quickly develop, deploy, and keep statistical or deep learning forecasting models maintained.
Deep Learning Architectures
Because Coredata AI already has extensive experience in model design, deployment, and governance, incorporating deep learning into data projects and business applications is a simple task with their platform. For computer vision tasks, such as image classification and object detection, you can categorize tailored deep learning architectures, or you can capitalize pre-trained models, transfer learning, and no-code interfaces to ensure fast and best results.
Scale as You Improve
The combination of Spark and Kubernetes allows for the automatic and efficient scaling of workloads across a company’s preferred cloud platform, making it ideal for handling large processing or model building jobs.
If you’re a data scientist, you can spend more time doing what you love and less time configuring backend resources thanks to pre-configured and managed clusters.
Start Your Journey
Lets Coredata AI guide your steps in the right direction
Get in Touch
Contact us today and find out how Coredata AI can help your business grow.
Start Your Journey
Lets Coredata AI guide your steps in the right direction
Get in Touch
Contact us today and find out how Coredata AI can help your business grow.