Top 3 enhancement by DataRobot to improve Enterprise AI Platform
Recent years have given huge impetus to Artificial INtelligence fueled technologies to be more adaptive and acceptable for enterprises and organizations. There are several AI technologies like machine learning, deep learning, natural language programming, computer vision, etc. that are pre-dominant in enterprise adoption. So, businesses started providing enterprise AI platforms that integrate various AI technologies to provide enterprises with the facilities of cognitive technologies.
According to a survey conducted by Deloitte in 2018, more than 37% corporates says that they have invested more than US $5 million on cognitive technologies based on AI. This is one more reason to acquire cognitive capabilities for your enterprise. Another astounding figure is of 59% organizations choosing Enterprise Software with AI integrated into it. More enterprises gain cognitive capabilities through enterprise software, such as CRM or ERP systems than by any other method. These systems have the advantage of access to BigData, and can often be used easily by employees with no specialized knowledge.
DataRobot: Enterprise AI Platform
DataRobot has been a leader in providing automated machine learning platforms to enterprises worldwide. It is an Enterprise AI platform vendor that has been popular for innovative technologies. Organizations worldwide use DataRobot to empower the teams they already have in place to?rapidly build and deploy machine learning models and create advanced AI applications.?With a library of hundreds of the most powerful open source machine learning algorithms, the DataRobot platform encapsulates every best practice and safeguard to accelerate and scale data science capabilities while maximizing transparency, accuracy, and collaboration.
Automated machine learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Applying traditional machine learning methods to real-world business problems is time-consuming, resource-intensive, and challenging. It requires experts in several disciplines, including data scientists – some of the most sought-after professionals in the job market right now.
With its newly enhanced platform, DataRobot further empowers citizen data scientists to successfully create advanced AI applications while making expert data scientists even more productive. The DataRobot Enterprise AI Platform provides automation across the entire AI lifecycle -- organizing, building, deploying, running, and managing AI assets -- to accelerate and streamline a user’s journey from data to value.
Advantages of AI Platform:- Enhanced current products.
- Optimization of internal operations.
- Ability to make better decisions.
- Optimization of external operations.
- More Employee time for higher creative work
- Creation of new products with robust designs.
- Capturing and application of scarce knowledge.
- Reduce headcount through automation
- Pursue new markets.
DataRobot has added an AI catalog to its AI platform based on DataRobot's acquisition of Cursor, which was a data collaboration platform founded to help organizations detect, learn and analyze various data efficiently. Users can search any datasets, share new sources and comments or tag assets to promote learning and reusability.by using the collaborative environment of enterprise AI. AI catalog can help enterprises prepare and manage feature lists that can be shared and used in future projects.
Integration of search and collaborative capabilities render users with the ability to have secure access to trusted sources and data assets from a governed and controlled AI environment. It enforces sharing permissions and provides lineage for safety and trustworthy machine learning operations. Other data management benefits are connection to data location, whether it is in a data pool, database and cloud.
2. Automated Feature Engineering:DataRobot has been the pioneers of Automated Feature Engineering by extensive use of Automated Machine Learning and Automated Time Series. Organizations can hire developers that can use automated feature engineering which is better than manual feature engineering which can be laborious, time-consuming and costly method.
With, Automated Feature Engineering, users can now discover, new features from multiple datasets by use of the AI catalog. Users can use the datasets to improve machine learning models. This feature enables users to accumulate data from various sources and automate the creation of a large number of features for specific algorithms.
3. Machine Learning OPs(MLOps):Organizations are spending huge money on AI development with data science teams and infrastructure development for AI applications. But, only a fraction of machine learning models make it into production. The few models that do make it into production do not have the necessary monitoring and governance that are required to ensure they are accurate and consistent throughout changing market or environmental conditions. Effective and responsible use of AI requires a modern and centralized system to automate the deployment, monitoring, management, and governance of both models and projects through every step of the AI production lifecycle.
Solution to this was introduced by DataRobot MLOps, which provides enterprises with a single dashboard that deploys models, governs them and monitors their status along with all the production models, irrespective of the origin or deployment location.
Concluding thought:With the advancement in enterprise AI tools, the productivity of many organizations have seen a reasonable spike. Over the years, companies have been looking for a broad range of skillset to build AI infrastructure and develop AI models for their projects. Among them, AI researchers and AI software developers are in demand with 30% and 28% demand among organizations according to a report by Deloitte This shows that AI-driven software and Applications have gained an edge over other application of AI tools.
There has been significant progress in the field of cognitive technologies, due to the advent of enterprise AI platforms. With cloud-computing capabilities and cognitive approach, AI-based solutions has the momentum and edge over other enterprise solutions,