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Generative AI, and generating text with large language models in particular, is the biggest AI innovation in recent times. Leading this innovation wave are a select few organizations building the most cutting-edge large language models, Cohere being one of them.
Cohere provides an LLM for solving business problems. In this session, you'll learn to use the Cohere API with Python to generate content based on a given prompt, extract information from documents, and build a semantic search engine.
Data science involves disparate skills from statistics to programming to communication. The difficulty of assessing this broad set of technical skills causes pain for both hirers and applicants. Candidates are often asked to answer arcane statistics questions on the spot, or live code solutions to the sort of data structure/algorithm CS problems that even professional programmers resent.
In this webinar, Isaac presents findings from a series of interviews with 20 data science hiring managers at leading organizations across industry (e.g. FAANG, finance, startups). He will discuss common patterns that emerged around both challenges and best practices, and make some actionable recommendations for data science teams looking to improve their hiring processes.
Machine Learning applications are complex and can be difficult to track, hard to reproduce, and problematic to deploy. MLflow is designed to simplify the challenges of managing the machine learning lifecycle.
In this code-along you'll learn to use MLflow to track and package a machine learning model, and see the process for getting models into production. Throughout the code-along, you’ll learn how to get started with MLflow, how to make a reproducible machine learning model, and how to get started with model tracking and packaging.
Best Practices for Developing Generative AI Products
AI Assistants (also known as digital assistants or AI agents) like Apple Siri, Amazon Alexa, and Google Assistant, have been popular for years, but recent advances in generative AI and tools like ChatGPT mean that every business can now have its own AI assistants.
In this webinar, you'll learn about the most important business use cases for AI assistants, how to adopt and manage AI assistants, and how to ensure data privacy and security while using AI assistants.
Despite the rise in excitement about adopting AI within the enterprise, many data teams today still struggle to drive value out of data & AI technologies. To successfully use AI within an enterprise, you need a range of capabilities, from data stewardship to MLOps to AI product management and even storytelling. Understanding these capabilities and how to develop them within your organization is essential to achieving your strategic initiatives around AI.
In this session, Rehgan Avon, Co-founder & CEO at AlignAI, will outline how to build a strategic roadmap any organization can adopt for developing AI capabilities and learn how to avoid common pitfalls with AI development.
Retrieval Augmented Generation with GPT and Milvus
Large language models (LLMs) like GPT are the must-have technology of the year. Unfortunately, LLMs can't accurately answer questions about your business because they lack enough domain knowledge. The solution is to combine the LLM with a vector database like Milvus—a technique known as retrieval augmented generation (RAG).
In this session you'll learn how to get started with Milvus and perform Q&A on some documents using GPT and the RAG technique.
Visual data is becoming increasingly important for business as AI allows you to extract more value from it. However, working with this type of data has a lot of challenges, and mistakes can be expensive. In this webinar, three computer vision executives discuss the best ways to get value from your visual data.
Throughout this session, you’ll learn about high value use-cases for image & video data, best practices for managing and analyzing visual data, and an overview of the latest cutting edge innovations in computer vision.
In this code along, we delve into the world of data analysis and visualization using PivotCharts and PivotTables on a sales dataset. The primary objective is to gain a comprehensive understanding of these powerful Excel tools for the purpose of exploratory data analysis and effective data visualization.
Throughout the code along, we will navigate through a range of Excel concepts, starting from the fundamental basics to intermediate-level techniques. This progressive approach ensures that learners of all skill levels can grasp the material effectively.
Our journey includes creating PivotTables, which allow us to summarize and manipulate data effortlessly. We will explore the art of transforming raw data into actionable insights, employing techniques such as filtering, grouping, and calculating aggregated values.
The real magic unfolds when we dive into PivotCharts, as we discover the diverse range of chart types available and learn how to select the most suitable ones for different data scenarios. The project emphasizes the importance of good visualization practices, teaching us how to create visually appealing and informative charts that convey insights effectively.
By the end of this code project, learners will have a solid foundation in using PivotCharts and PivotTables for exploratory data analysis, and they will possess the skills to present data in a compelling and informative manner—a valuable skillset for anyone working with data in Excel.
