113  0 Kommentare Confluent Unveils New Capabilities to Apache Flink Offering to Simplify AI and Bring Stream Processing to Workloads Everywhere

Generative AI helps organizations innovate faster and deliver more tailored customer experiences. AI workloads need fresh, context-rich data to ensure the underlying models generate accurate output and results for businesses to make informed decisions based on the most current information available.

However, developers often have to use several tools and languages to work with AI models and data processing pipelines, leading to complex and fragmented workloads. This can make it challenging to leverage the most current and relevant data for decision-making, leading to errors or inconsistencies and compromising the accuracy and reliability of AI-driven insights. These issues can cause increased development time and difficulties in maintaining and scaling AI applications.

With AI Model Inference in Confluent Cloud for Apache Flink, organizations can use simple SQL statements from within Apache Flink to make calls to AI engines, including OpenAI, Amazon SageMaker, GCP Vertex, and Microsoft Azure. Now enterprises can orchestrate data cleaning and processing tasks on a single platform.

“Apache Kafka and Flink are the critical links to fuel machine learning and artificial intelligence applications with the most timely and accurate data,” said Shaun Clowes, Chief Product Officer at Confluent. “Confluent’s AI Model Inference removes the complexity involved when using streaming data for AI development by enabling organizations to innovate faster and deliver powerful customer experiences.”

AI Model Inference enables companies to:

• Simplify AI development by using familiar SQL syntax to work directly with AI/ML models, reducing the need for specialized tools and languages.
• Establish seamless coordination between data processing and AI workflows to improve efficiency and reduce operational complexity.
• Enable accurate, real-time AI-driven decision-making by leveraging fresh, contextual streaming data.

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"Leveraging fresh, contextual data is paramount for training and refining AI models, and for use at time of inference to improve the accuracy and relevancy of outcomes,” said Stewart Bond, Vice President, Data Intelligence and Integration Software, IDC. “Organizations need to improve efficiencies of AI processing by unifying data integration and processing pipelines with AI models. Flink can now treat foundational models as first class resources, enabling the unification of real-time data processing with AI tasks to streamline workflows, enhance efficiency, and reduce operational complexity. These capabilities empower organizations to make accurate, real-time AI-driven decisions based on the most current and relevant streaming data while enhancing performance and value."