In order to reliably and efficiently deploy and maintain machine learning models in production, Informatica is updating its Data Management Cloud platform. Data integration, management and processing processes have also been improved.
A historic player in middleware, ETL and data management (Master Data Management), Informatica has jumped on the AI data management bandwagon using its Intelligent Data Management Cloud (IDMC) platform. The American provider has announced its update to tools and applications. They were announced Tuesday at the Informatica World conference and include: InfaCore, ModeleServe, a self-service iPaaS, and applications that incorporate the SaaS 360 product line. They are expected to leverage the capabilities and efficiencies of IDMC. in data management, integration and engineering, the company says. Additionally, following the launch in March of a data management platform targeted at retail, Informatica also announced the release of two more industry-specific versions of IDMC, one for financial services and one for in the health and life sciences.
To help businesses address the continuing talent shortage, the company introduced the plug-and-play InfaCore framework, designed to support data science and application development activities. It aims to simplify the creation of data pipelines by making large code into a single function, and accelerate the ability of data scientists and data engineers to consume, modify and prepare data from any source in their development environment. , says the publisher. The feature is also designed to deploy to apps, using their native user interfaces and speeding up time to market.
Increase the efficiency of data engineering and ML model automation
More often than not, developers and data scientists, despite being tech-savvy, can be put off by the task of integrating and modifying data and in such cases a framework like InfaCore can help, says Doug Henschen, principal analyst at Constellation. To research. “The announcement outlines a way to invoke data integration pipelines and components into third-party development and datascience environments, freeing these professionals from the need to recreate the work. that. ” According to the analyst, there are other iPaaS platforms that can help with data integration by calling APIs. Informatica said a major hurdle for companies implementing machine learning solutions is the challenge of integrating them into existing infrastructure. To help solve this problem, the company is offering a new service, called ModelServe, which is currently in private beta.
Through it, customers can run any machine learning model from their pipeline with one click, the company says, adding that the MLOps tool provides end-to-end visibility and control of the models. of machine learning. machine learning for data engineers. The company also introduced a machine learning model registry to upload, deploy, and manage machine learning models, and then run those models heavily in data pipelines using its serverless infrastructure. . The goal? Save time and effort. While Informatica may address a common model operation challenge that many data science and model operation target vendors also address, it is not uncommon to see a registry machine learning model in an iPaaS environment, Doug said. Henschen. “Machine learning model registers are typically found in model operations and data science environments. But the new tool gives customers, and especially Informatica customers, another option, “aniya.
Supports no-code increment
While IDMC’s Data Marketplace, launched last year, aims to reduce the need – through model sharing – to code for connectors, APIs and models, the publisher is now providing a code -free platform within its software suite so that developers can create their own connectors in cases where they can find nothing. Other latest features include predictive data intelligence and SaaS applications in the 360 d product line. ‘Informatica.
While predictive data intelligence will make recommendations to maximize the potential of a company’s data by suggesting actions to be taken for data management and engineering tasks, domain-specific 360 solutions can help to implement faster master data management solutions, Informatica says. Examples of these applications are Supplier 360 SaaS and Product 360 SaaS. Supplier 360 SaaS is an AI -powered application for supplier and contact management, supplier relationships and hierarchy, business rules, and supplier onboarding workflows. Product 360 SaaS, on the other hand, is an application for product data, product interactions and hierarchies, content enrichment and business rules on data quality, and workflows management.