Amazon Bedrock is a fully managed service that makes foundation models (FMs) from Amazon and leading AI companies available on AWS available through an API.
You have to sign in with a Google, Apple or Amazon account -
In the next step you can choose a username -
After I tried out some of the featured apps on the home page, I was able to build my own in minutes and publish it publicly - GeoFlix, check it out!
All I did was to provide the prompt that "the app should identify and suggest movies that prominently feature specified locations or place names within their storylines."
I edited the pre-created app name to GeoFlix and clicked on the "Make public and Share" button.
AWS is offering new PartyRock users a free trial without the need to provide a credit card or sign up for an AWS account for a limited time. The percentage balance reflecting the amount of free trial usage remaining for your account is displayed in real time within PartyRock on your Backstage profile page as PartyRock credit. PartyRock credit is calculated by your input tokens, output tokens, and generated images. PartyRock Credit usage varies by model to help users build intuition on cost consideration when using generative AI.
The relative consumption of each model is expressed in a scale of (1-3 token stacks) on the model selection dropdown. A (3 token stack) will consume the most credit, a (2 token stack) will consume moderate credit, and a (1 token stack) will consume the least credit.
While the free trial usage limit seems liberal, you have to be watchful of your consumption quota, which model you use and how may tokens are used while interacting with your apps or those created by others.
With some prompting, the Claude model also generated this description for the app -
Discover the magic of movie locations with GeoFlix. Our innovative app allows you to explore the world through the lens of cinema by identifying films that prominently feature real-world places. Just search for a city, country, or other geographic location, and GeoFlix will suggest relevant titles and provide fascinating details about how the setting is used in each movie's narrative. Whether you're planning a trip, studying a region, or just want to be transported somewhere new, GeoFlix makes it easy to find films that authentically showcase destinations worldwide.
PartyRock is indeed a great way to experiment with prompt engineering techniques, review generated responses, and develop an intuition for generative AI while creating and exploring fun apps!
Check this YouTube video if you need help in getting started with building your own app
In the Maven Crash Course " Learn Power Query, Power Pivot & DAX in 15 Minutes ", Chris Dutton walks through a hypothetical business case while showcasing the power of modern Excel tools like Power Query, Power Pivot and DAX. Power Query is used to extract, transform, and load data from external sources like SQL databases, PDF and CSV files. A relational model is created to join the tables without writing a single formula. Exploratory analysis is done using Power Pivot and Data Analysis Expressions (DAX). Data Analysis Expressions is the language that we can use to define new calculated columns and measures to enhance our Data Model instead of the traditional cell formulas that we'd typically use if our data was stored in worksheets. Pivot Charts and slicers are used to design a simple interactive report that the sales managers can use to analyze revenue trends and product performance.
The Maven Analytics course on " Data Prep & Exploratory Data Analysis " by Alice Zhao is a beginner-friendly introduction to machine learning with Python. It covers the key steps of the data science workflow, including: Gathering Data Scoping a Project Modeling Data Sharing Insights Cleaning Data Exploring Data Modeling Data Sharing Insights The presentation deck which is available as a download crisply summarizes the essential details and is a great reference to keep going back to. The course is well-suited for aspiring data scientists looking to get hands-on experience with the data prep and EDA stages of the machine learning workflow using Python and Jupyter Notebook.
I was happy to learn about the Oracle Cloud Infrastructure 2024 Generative AI Professional Course with video tutorials and the Certification Exam (1Z0-1127-24) from their newsletter. I found it extremely useful, timely, and relevant. The course, with about 6 hours of videos, provided me with a comprehensive view of what it takes to build a Generative AI application. Although some parts reference OCI services, much of the course remains vendor neutral. Rohit Rahi and his team at Oracle have done a great job of structuring the beginner-friendly yet comprehensive course so well, covering all the essentials across four modules. The curriculum strikes an excellent balance between depth and accessibility. While Gen AI output is typically non-deterministic, I was surprised to learn that they can be made completely deterministic under certain conditions. Specifically, in the OCI Gen AI service, setting the temperature parameter to 0 produces consistent, deterministic output for a g...
Comments
Post a Comment