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In this session, we present our experimental approach to creating DPK pipelines using agentic workflows. We will begin with a brief introduction to agentic workflows, followed by a walkthrough of two notebooks developed to support this work:

The first notebook shows a planner agent for Data-Prep-Kit tasks with code generation. The agent builds DPK pipeline that performs required tasks defined by a natural language.

The second notebook demonstrates how DPK transformers can be wrapped as tools within LangChain and LlamaIndex, along with examples of executing the transforms directly.

In this session, we present our experimental approach to creating DPK pipelines using agentic workflows. We will begin with a brief introduction to agentic workflows, followed by a walkthrough of two notebooks developed to support this work: The first notebook shows a planner agent for Data-Prep-Kit tasks with code generation. The agent builds DPK pipeline that performs required tasks defined by a natural language. The second notebook demonstrates how DPK transformers can be wrapped as tools within LangChain and LlamaIndex, along with examples of executing the transforms directly.

Overview: In this session, we present our experimental approach to creating DPK pipelines using agentic workflows. We will begin with a brief introduction to agentic workflows, followed by a walkthrough of two notebooks developed to support this work: the first notebook shows a planner agent for Data-Prep-Kit tasks with code generation, building DPK pipelines from natural language tasks; the second notebook demonstrates wrapping DPK transformers as tools within LangChain and LlamaIndex, with examples of executing the transforms directly.

Agenda

• Welcome, housekeeping, etc.
• Quick intro about AI Alliance (3 min)
• Simplifying DPK Pipeline Creation with Agentic Workflows (40 mins)
• Q&A (10 mins)
• Wrap-up

Overview In this session, we present our experimental approach to creating DPK pipelines using agentic workflows. We will begin with a brief introduction to agentic workflows, followed by a walkthrough of two notebooks developed to support this work:

The first notebook shows a planner agent for Data-Prep-Kit tasks with code generation. The agent builds DPK pipeline that performs required tasks defined by a natural language.

The second notebook demonstrates how DPK transformers can be wrapped as tools within LangChain and LlamaIndex, along with examples of executing the transforms directly.

Session Type Talk and Demo

Audience LLM app developers, data scientists, data engineers

Technical Level Beginner - Intermediate

Prerequisites None

Duration 45 mins

Resources

• first notebook
• second notebook

Speakers

Mohammad Nassar Mohammad Nassar, a Cloud Research Engineer at IBM Haifa, specializes in AI-driven data engineering, automation, and hybrid cloud technologies. With an M.Sc. in Computer Science from Technion, his research focused on coding theory and data systems. His work spans AI-powered data preparation, automation pipelines, and large-scale cloud solutions. Passionate about innovation, he also develops mobile applications, blending AI and user engagement.

Revital Sur Revital Sur is a member of the Cloud Data Platforms group at IBM Haifa Research Lab, where she has been involved in various cloud and middleware projects in recent years. She is a DPK contributor from its first days and recently, she were involved into agentic activities. She holds both a BSc and an MSc in Computer Science from the University of Haifa, Israel.

Agenda

• Welcome, housekeeping, etc.
• Quick intro about AI Alliance (3 min)
• Simplifying DPK Pipeline Creation with Agentic Workflows (40 mins)
• Q&A (10 mins)
• Wrap-up

Overview In this session, we present our experimental approach to creating DPK pipelines using agentic workflows. We will begin with a brief introduction to agentic workflows, followed by a walkthrough of two notebooks developed to support this work:

The first notebook shows a planner agent for Data-Prep-Kit tasks with code generation. The agent builds DPK pipeline that performs required tasks defined by a natural language.

The second notebook demonstrates how DPK transformers can be wrapped as tools within LangChain and LlamaIndex, along with examples of executing the transforms directly.

Session Type Talk and Demo

Audience LLM app developers, data scientists, data engineers

Technical Level Beginner - Intermediate

Prerequisites None

Duration 45 mins

Resources

• first notebook
• second notebook

Speakers

Mohammad Nassar Mohammad Nassar, a Cloud Research Engineer at IBM Haifa, specializes in AI-driven data engineering, automation, and hybrid cloud technologies. With an M.Sc. in Computer Science from Technion, his research focused on coding theory and data systems. His work spans AI-powered data preparation, automation pipelines, and large-scale cloud solutions. Passionate about innovation, he also develops mobile applications, blending AI and user engagement.

Revital Sur Revital Sur is a member of the Cloud Data Platforms group at IBM Haifa Research Lab, where she has been involved in various cloud and middleware projects in recent years. She is a DPK contributor from its first days and recently, she were involved into agentic activities. She holds both a BSc and an MSc in Computer Science from the University of Haifa, Israel.

Agenda

• Welcome, housekeeping, etc.
• Quick intro about AI Alliance (3 min)
• Simplifying DPK Pipeline Creation with Agentic Workflows (40 mins)
• Q&A (10 mins)
• Wrap-up

Overview In this session, we present our experimental approach to creating DPK pipelines using agentic workflows. We will begin with a brief introduction to agentic workflows, followed by a walkthrough of two notebooks developed to support this work:

The first notebook shows a planner agent for Data-Prep-Kit tasks with code generation. The agent builds DPK pipeline that performs required tasks defined by a natural language.

The second notebook demonstrates how DPK transformers can be wrapped as tools within LangChain and LlamaIndex, along with examples of executing the transforms directly.

Session Type Talk and Demo

Audience LLM app developers, data scientists, data engineers

Technical Level Beginner - Intermediate

Prerequisites None

Duration 45 mins

Resources

• first notebook
• second notebook

Speakers

Mohammad Nassar Mohammad Nassar, a Cloud Research Engineer at IBM Haifa, specializes in AI-driven data engineering, automation, and hybrid cloud technologies. With an M.Sc. in Computer Science from Technion, his research focused on coding theory and data systems. His work spans AI-powered data preparation, automation pipelines, and large-scale cloud solutions. Passionate about innovation, he also develops mobile applications, blending AI and user engagement.

Revital Sur Revital Sur is a member of the Cloud Data Platforms group at IBM Haifa Research Lab, where she has been involved in various cloud and middleware projects in recent years. She is a DPK contributor from its first days and recently, she were involved into agentic activities. She holds both a BSc and an MSc in Computer Science from the University of Haifa, Israel.

Agenda

• Welcome, housekeeping, etc.
• Quick intro about AI Alliance (3 min)
• Simplifying DPK Pipeline Creation with Agentic Workflows (40 mins)
• Q&A (10 mins)
• Wrap-up

Overview In this session, we present our experimental approach to creating DPK pipelines using agentic workflows. We will begin with a brief introduction to agentic workflows, followed by a walkthrough of two notebooks developed to support this work:

The first notebook shows a planner agent for Data-Prep-Kit tasks with code generation. The agent builds DPK pipeline that performs required tasks defined by a natural language.

The second notebook demonstrates how DPK transformers can be wrapped as tools within LangChain and LlamaIndex, along with examples of executing the transforms directly.

Session Type Talk and Demo

Audience LLM app developers, data scientists, data engineers

Technical Level Beginner - Intermediate

Prerequisites None

Duration 45 mins

Resources

• first notebook
• second notebook

Speakers

Mohammad Nassar Mohammad Nassar, a Cloud Research Engineer at IBM Haifa, specializes in AI-driven data engineering, automation, and hybrid cloud technologies. With an M.Sc. in Computer Science from Technion, his research focused on coding theory and data systems. His work spans AI-powered data preparation, automation pipelines, and large-scale cloud solutions. Passionate about innovation, he also develops mobile applications, blending AI and user engagement.

Revital Sur Revital Sur is a member of the Cloud Data Platforms group at IBM Haifa Research Lab, where she has been involved in various cloud and middleware projects in recent years. She is a DPK contributor from its first days and recently, she were involved into agentic activities. She holds both a BSc and an MSc in Computer Science from the University of Haifa, Israel.