Rakesh Ranjan

This innovative feature solves the challenges of observing generative AI (GenAI) using IBM Instana. It introduces a new sensor for GenAI Runtimes that can be used to collect traces, metrics, and logs. This sensor can be used to monitor GenAI applications and platforms. In the context of GenAI stack, the "prompt size" and "number of tokens" are important considerations that can affect performance and efficiency.
Prompt Size:
- Impact on Model Input: The prompt size refers to the length of… Show more

This innovative feature solves the challenges of observing generative AI (GenAI) using IBM Instana. It introduces a new sensor for GenAI Runtimes that can be used to collect traces, metrics, and logs. This sensor can be used to monitor GenAI applications and platforms. In the context of GenAI stack, the "prompt size" and "number of tokens" are important considerations that can affect performance and efficiency.
Prompt Size:
- Impact on Model Input: The prompt size refers to the length of the input text provided to the model for generating responses or performing tasks. In the context of LLM, this could correspond to the length of queries, commands, or instructions given to the observability tools or systems.
- Performance Considerations: Larger prompt sizes can increase the computational resources (CPU, memory) required to process each request. This can lead to slower response times, especially if the system is processing a large volume of requests concurrently.
- Optimization: Optimizing prompt size involves balancing the amount of information provided to the system with the need to maintain responsiveness and efficiency. Techniques such as summarization or prioritization of information can help reduce unnecessary verbosity in prompts.
Number of Tokens:
- Model Input Limitation: NLP models have a maximum input limit measured in tokens (where each word or subword is a token). If a prompt exceeds this limit, it may need to be split into multiple segments for processing.
- Resource Usage: Increasing the number of tokens in a prompt increases the computational load on the model. This can affect response times and the ability to handle concurrent requests efficiently.
- Batch Processing: Observability systems often process data in batches. The number of tokens per batch can affect how quickly data can be processed and analyzed, impacting real-time monitoring and responsiveness.
LLM observability helps with:
- Use Efficient Queries
- Monitor and Adjust
- Scale Resources Show less

This feature enables Instana users to use generative AI models from watsonx.ai to create actions and automation that are linked to various events they encounter in their IT infrastructure. After the next best action description is created, Instana uses the code generation models from watsonx.ai to create automation, for example, scripts and Ansible playbooks. Every event that is triggered by Instana undergoes analysis by the automation matching engine. This engine employs diverse algorithms to… Show more

This feature enables Instana users to use generative AI models from watsonx.ai to create actions and automation that are linked to various events they encounter in their IT infrastructure. After the next best action description is created, Instana uses the code generation models from watsonx.ai to create automation, for example, scripts and Ansible playbooks. Every event that is triggered by Instana undergoes analysis by the automation matching engine. This engine employs diverse algorithms to suggest actions from Instana's catalog for debugging, resolving, or debugging and resolving the event.
When you load the Watsonx-powered actions, they will be highly recommended for events that are closely related to a specific action.

For manual actions, use the RAG (Retrieval Augmented Generation) pattern to start the watsonx.ai models. Then, create the "next best action" description Show less

AI powered automation simplifies building application integration flows for business users. I am driving innovation in IBM Automation portfolio in collaboration with IBM research in the following areas:
- Harvesting of metadata changes and create knowledge graph
- Use knowledge to build AI models that automate workflow and continuously learn from usage
- Automated API test generation for cutting API development costs
- API security

IBM Db2 Augmented Data Explorer makes it easy for business users to easily access insights from their enterprise's data and act on them. The explorer is an easy to use, web-based platform that connects to Db2 databases, be it on-prem or the cloud, to instantly pull key insights through automatically generated visualizations and natural language summaries. No need to wait for weekly reports to answer your questions; just ask them yourself and get answers in seconds. With Db2 Augmented Data… Show more

IBM Db2 Augmented Data Explorer makes it easy for business users to easily access insights from their enterprise's data and act on them. The explorer is an easy to use, web-based platform that connects to Db2 databases, be it on-prem or the cloud, to instantly pull key insights through automatically generated visualizations and natural language summaries. No need to wait for weekly reports to answer your questions; just ask them yourself and get answers in seconds. With Db2 Augmented Data Explorer, we put YOU, the business user, at the center, to make better business decisions. Watch the video here: https://www.youtube.com/watch?v=9xrJhMMcCOU&feature=youtu.be Show less

Accelerate your Regulatory compliance with privacy regulations such as CCPA which is coming into effect on Jan 1, 2020. This is an 100% AI powered solution that embeds human expertise and solve a complex regulation and compliance workflow.

We built a platform which would help enterprise developers to deliver products faster, and minimize the gap between development and deployment in production. We built a portal which would reduce the aforementioned bridge by using deployment automation tools and cloud based resource providers. DaaS provides an alternative to the enterprises to deploy or migrate to a public cloud. By using Kubernetes as backend service to deploy Docker containers, we are able to access and read the existing… Show more

We built a platform which would help enterprise developers to deliver products faster, and minimize the gap between development and deployment in production. We built a portal which would reduce the aforementioned bridge by using deployment automation tools and cloud based resource providers. DaaS provides an alternative to the enterprises to deploy or migrate to a public cloud. By using Kubernetes as backend service to deploy Docker containers, we are able to access and read the existing Kubernetes configuration from a currently deployed environment and hence can replicate in a different destination cloud.
We have used Docker containers, as they are lightweight solution for dependencies and configurations while moving the application between different computing environments. We have used Kubernetes in order to schedule deployments and scale up the applications.
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This system automates the process of Disaster Recovery (DR), by detecting disaster alerts and initiating backup and recovery of data from the affected datacenter to backup datacenters, using the best routes available. We have used the industry leading SDN controller OpnDaylight to manage the location/IP mappings and flow management. The system also utilizes google map service for facilitating location related functionalities. The user-friendly GUI provides intuitive user interface to the… Show more

This system automates the process of Disaster Recovery (DR), by detecting disaster alerts and initiating backup and recovery of data from the affected datacenter to backup datacenters, using the best routes available. We have used the industry leading SDN controller OpnDaylight to manage the location/IP mappings and flow management. The system also utilizes google map service for facilitating location related functionalities. The user-friendly GUI provides intuitive user interface to the users, providing detailed information about the disaster 105 alters, affected datacenter, back up datacenter and backup routes. The system also allows manual configuration of DR. We have also evaluated the performance of the system and provided performance benchmarks. Show less

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dashDB is a fully managed data warehouse service in the cloud, it’s an analytics powerhouse at your finger tips. dashDB keeps the infrastructure of data warehousing out of your way! Simple, up and running in minutes with rapid cloud provisioning. No infrastructure investment provides true business agility.Built-in analytics helps you understand your business with greater efficiency and performance.

The Codename: BlueMix Catalog contains everything you need to hit the ground running. We've got everything from IBM provided services to third-party offerings. You can Rapidly build powerful apps with boilerplates. Pre-configured sets of services, runtimes and sample code. Ready for instant deployment.

Develop a robust technical framework that will enable United Way India to digitally capture, compile, and measure program outcomes of Born Learning campaign nationally. Such a framework will enable United Way to provide an empirical and cohesive impact assessment, and enable corporate partners, government, and other stakeholders to evaluate real-time impact on the ground.