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Learn more about our upcoming Large Language Models Bootcamp, starting on the 29th of January in Seattle, in a free info session by our CEO and Chief Data Scientist, Raja Iqbal.

We will discuss the rise and future of LLMs and their applications and provide an overview of our comprehensive bootcamp curriculum which covers embeddings, prompt engineering, orchestration frameworks, and much more.

Stay tuned for the forthcoming announcement of our online version of the bootcamp!

Link to webpage Link to advisor call

Learn more about our upcoming Large Language Models Bootcamp, starting on the 29th of January in Seattle, in a free info session by our CEO and Chief Data Scientist, Raja Iqbal.

We will discuss the rise and future of LLMs and their applications and provide an overview of our comprehensive bootcamp curriculum which covers embeddings, prompt engineering, orchestration frameworks, and much more.

Stay tuned for the forthcoming announcement of our online version of the bootcamp!

Link to webpage Link to advisor call

Learn more about our upcoming Large Language Models Bootcamp, starting on the 29th of January in Seattle, in a free info session by our CEO and Chief Data Scientist, Raja Iqbal.

We will discuss the rise and future of LLMs and their applications and provide an overview of our comprehensive bootcamp curriculum which covers embeddings, prompt engineering, orchestration frameworks, and much more.

Stay tuned for the forthcoming announcement of our online version of the bootcamp!

Link to webpage Link to advisor call

Join us for a Fireside Chat featuring Alessya Visnjic, CEO and Co-founder of WhyLabs and Raja Iqbal, CEO and Chief Data Scientist at Data Science Dojo, as we delve into the world of AI Observability and Large Language Models.

In this conversation, we’ll explore the critical role of AI Observability in ensuring the safety and security of large language models (LLMs). Our speakers will share their expertise and unique insights into monitoring and understanding these advanced models, emphasizing the significance of specialized analytics tools and observability platforms.

This discussion aims to offer fresh insights and practical approaches for technical architects, engineers, and organizational leaders seeking to leverage LLMs and generative AI through AI Observability.

Join us for a Fireside Chat featuring Alessya Visnjic, CEO and Co-founder of WhyLabs and Raja Iqbal, CEO and Chief Data Scientist at Data Science Dojo, as we delve into the world of AI Observability and Large Language Models.

In this conversation, we’ll explore the critical role of AI Observability in ensuring the safety and security of large language models (LLMs). Our speakers will share their expertise and unique insights into monitoring and understanding these advanced models, emphasizing the significance of specialized analytics tools and observability platforms.

This discussion aims to offer fresh insights and practical approaches for technical architects, engineers, and organizational leaders seeking to leverage LLMs and generative AI through AI Observability.

Join us for an engaging conversation about Large Language Models (LLMs) with a panel of industry leaders: Raja Iqbal (Chief Data Scientist, Data Science Dojo), Taimur Rashid (Chief Business Officer, Redis), Sam Partee (Principal Applied AI Engineer, Redis), and Daniel Svonava (CEO, Superlinked).

In this era where generative AI and LLMs are reshaping industries, businesses must be equipped to harness their potential fully. This discussion will focus on the common design patterns for LLM applications, especially the Retrieval-Augmented Generation (RAG) framework. The speakers will discuss various strategies for embedding knowledge into these models, using vector databases and knowledge graphs in fetching domain-specific data.

This discussion aims to not only inspire organizational leaders to reimagine their data strategies in the face of LLMs and generative AI but also to empower technical architects and engineers with practical insights and methodologies.

Join us for an engaging conversation about Large Language Models (LLMs) with a panel of industry leaders: Raja Iqbal (Chief Data Scientist, Data Science Dojo), Taimur Rashid (Chief Business Officer, Redis), Sam Partee (Principal Applied AI Engineer, Redis), and Daniel Svonava (CEO, Superlinked).

In this era where generative AI and LLMs are reshaping industries, businesses must be equipped to harness their potential fully. This discussion will focus on the common design patterns for LLM applications, especially the Retrieval-Augmented Generation (RAG) framework. The speakers will discuss various strategies for embedding knowledge into these models, using vector databases and knowledge graphs in fetching domain-specific data.

This discussion aims to not only inspire organizational leaders to reimagine their data strategies in the face of LLMs and generative AI but also to empower technical architects and engineers with practical insights and methodologies.

Join us for an engaging conversation about Large Language Models (LLMs) with a panel of industry leaders: Raja Iqbal (Chief Data Scientist, Data Science Dojo), Taimur Rashid (Chief Business Officer, Redis), Sam Partee (Principal Applied AI Engineer, Redis), and Daniel Svonava (CEO, Superlinked).

In this era where generative AI and LLMs are reshaping industries, businesses must be equipped to harness their potential fully. This discussion will focus on the common design patterns for LLM applications, especially the Retrieval-Augmented Generation (RAG) framework. The speakers will discuss various strategies for embedding knowledge into these models, using vector databases and knowledge graphs in fetching domain-specific data.

This discussion aims to not only inspire organizational leaders to reimagine their data strategies in the face of LLMs and generative AI but also to empower technical architects and engineers with practical insights and methodologies.

Join us for a Fireside Chat featuring Alessya Visnjic, CEO and Co-founder of WhyLabs and Raja Iqbal, CEO and Chief Data Scientist at Data Science Dojo, as we delve into the world of AI Observability and Large Language Models.

In this conversation, we’ll explore the critical role of AI Observability in ensuring the safety and security of large language models (LLMs). Our speakers will share their expertise and unique insights into monitoring and understanding these advanced models, emphasizing the significance of specialized analytics tools and observability platforms.

This discussion aims to offer fresh insights and practical approaches for technical architects, engineers, and organizational leaders seeking to leverage LLMs and generative AI through AI Observability.

This live event is intended for data scientists, platform engineers, and finance operations managers who are responsible for AI-based applications and need to manage their public cloud infrastructure costs closely. Artificial Intelligence and Machine Learning are the new frontiers of software development. Massive market demand for AI is pushing up cloud infrastructure costs. Profitable AI products require collaborative management between data science, platform engineering, and increasingly FinOps teams. An 80% public cloud cost contribution to the cost of goods sold is no longer viable when launching new digital products. Join industry leaders, Raja Iqbal of Data Science Dojo and Asim Razzaq of Yotascale as they discuss how to future-proof your AI initiatives. Cloud cost management is an essential element of any modern data stack AI project. Raja and Asim will cover the challenges of tracking AI cloud expenses and how to move from just managing to planning and forecasting your cloud costs for profitable AI-based products and services.

This live event is intended for data scientists, platform engineers, and finance operations managers who are responsible for AI-based applications and need to manage their public cloud infrastructure costs closely. Artificial Intelligence and Machine Learning are the new frontiers of software development. Massive market demand for AI is pushing up cloud infrastructure costs. Profitable AI products require collaborative management between data science, platform engineering, and increasingly FinOps teams. An 80% public cloud cost contribution to the cost of goods sold is no longer viable when launching new digital products. Join industry leaders, Raja Iqbal of Data Science Dojo and Asim Razzaq of Yotascale as they discuss how to future-proof your AI initiatives. Cloud cost management is an essential element of any modern data stack AI project. Raja and Asim will cover the challenges of tracking AI cloud expenses and how to move from just managing to planning and forecasting your cloud costs for profitable AI-based products and services.

This live event is intended for data scientists, platform engineers, and finance operations managers who are responsible for AI-based applications and need to manage their public cloud infrastructure costs closely. Artificial Intelligence and Machine Learning are the new frontiers of software development. Massive market demand for AI is pushing up cloud infrastructure costs. Profitable AI products require collaborative management between data science, platform engineering, and increasingly FinOps teams. An 80% public cloud cost contribution to the cost of goods sold is no longer viable when launching new digital products. Join industry leaders, Raja Iqbal of Data Science Dojo and Asim Razzaq of Yotascale as they discuss how to future-proof your AI initiatives. Cloud cost management is an essential element of any modern data stack AI project. Raja and Asim will cover the challenges of tracking AI cloud expenses and how to move from just managing to planning and forecasting your cloud costs for profitable AI-based products and services.

As enterprises move beyond ChatGPT, Bard, and ‘demo applications’ of large language models, product leaders, and engineers are running into challenges. The magical experience we observe on content generation and summarization tasks using ChatGPT is not replicated on custom LLM applications built on enterprise data. This frustration can lead to one (or a mix) of the following reactions:

– LLMs is yet another hype, or a fad at best. – LLMs are good only for summarization and content generation tasks; They are not suitable for enterprise applications. – Building LLM applications is significantly harder than just using an off-the-shelf foundation model.

The third point is the closest to reality. Enterprise LLM applications are easy to imagine and build a demo out of, but somewhat challenging to turn into a business application. The complexity of datasets, training costs, cost of token usage, response latency, context limit, fragility of prompts, and repeatability are some of the problems faced during product development.

As the Chief Data Scientist at Data Science Dojo, Raja Iqbal has led a team of extremely talented data scientists and software engineers to deliver multiple enterprise applications of large language models. In this live talk, Raja will go over the challenges faced by his team while building these mission-critical applications and how the team overcame those challenges.