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by "ByteByteGo" <bytebytego@substack.com> - 01:30 - 19 Jul 2024 -
What’s on your summer reading list?
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McKinsey’s 2024 annual book recommendations
by "Only McKinsey" <publishing@email.mckinsey.com> - 01:37 - 19 Jul 2024 -
RE: Consumer Electronics Show - CES 2024 (Post Show)
Hi,
I am just following-up regarding my previous email.
If you are looking for pricing offer, please do let me know.
Thanks,
Becky
From: becky.walters@moreofdata.com <becky.walters@moreofdata.com>
Sent: Tuesday, July 16, 2024 2:41 PM
To: 'info@learn.odoo.com' <info@learn.odoo.com>
Subject: Consumer Electronics Show - CES 2024 (Post Show)I had a chance to review your company profile and thought you might be interested in acquiring an updated Attendee list of Consumer Electronics Show - CES 2024?
Attendees are: - Analyst, Content Developer, Distributor, Buyer, Engineer, Manager/Store Manager/Product Manager, Manufacturer’s Representative, Service Technician, Systems Installer/Integrator & More.
List Contains: Name, Title, Email, Phone, Company Name, Physical Address, City, State, Zip Code, Country, Web Address, Employee Size, Revenue Size and Industry.
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by becky.walters@moreofdata.com - 04:43 - 18 Jul 2024 -
A Crash Course on Relational Database Design
A Crash Course on Relational Database Design
In today's data-driven world, efficient storage and management of information are critical requirements for businesses and organizations of all sizes. Relational databases provide a robust framework for storing and retrieving data based on well-defined relationships between entities. They offer a structured approach to data management, enabling users to:͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ Forwarded this email? Subscribe here for moreLatest articles
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In today's data-driven world, efficient storage and management of information are critical requirements for businesses and organizations of all sizes.
Relational databases provide a robust framework for storing and retrieving data based on well-defined relationships between entities. They offer a structured approach to data management, enabling users to:
Define tables
Establish relationships
Perform complex queries to extract meaningful insights from the stored information
However, just using a relational database is not enough to gain its benefits.
Effective database design is crucial for optimizing performance, ensuring data integrity, and facilitating efficient data retrieval. The principles of database design, such as normalization, indexing, joins, and relationships, play a vital role in creating a well-structured and performant database.
In this post, we’ll look at the fundamentals of relational databases, exploring their key concepts, management systems, and the principles that underpin effective database design.
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by "ByteByteGo" <bytebytego@substack.com> - 11:36 - 18 Jul 2024 -
Don’t forget to book your spot to accelerate your API platform maturity!
Don’t forget to book your spot to accelerate your API platform maturity!
Join our Scaling API platforms webinar on July 25th to assess and accelerate your API platform maturity.Hi Md Abul,
Would you like to assess and accelerate your API platform maturity? Then join us on July 25th for an action-packed webinar.
Tyk’s Developer Advocate Budha Bhattacharya will join a platform architect and product owner from ACME FinCorp International, a Fortune 500 financial services company at an early stage of its API journey. ACME FinCorp International is exploring ways to mature its platform initiative, delving into Tyk’s API platform maturity model and prioritization framework.
During the webinar, we will:- Provide a brief overview of the API platform maturity model
- Identify the current platform maturity of ACME FinCorp
- Discuss KPIs and what success looks like over 6-12 months
- Choose key strategic and implementation maturity pillars to prioritize
- Define the next steps to achieve desired platform maturity over 6-12 months
If you’re ready to create a playbook for API platform maturity, don’t miss this webinar. Speakers will include Tyk’s Budha Bhattacharya (Developer Advocate), Tamara Evans (Account Director) and Andy Smith (Solution Architect).
We’ll kick off at 10 am EDT/3 pm BST on July 25th. Click the link below to join us.See you soon,
Budha & teamTyk, Huckletree 199 Bishopsgate, Broadgate, London, City of London EC2M 3TY, United Kingdom, +44 (0)20 3409 1911
by "Budhaditya Bhattacharya" <budha@tyk.io> - 06:00 - 18 Jul 2024 -
How tight are advanced economies’ labor markets?
Only McKinsey
Scenarios for labor force participation •
Labor market squeeze. Labor markets in advanced economies remain tighter than at any other time over the past two decades, new McKinsey Global Institute (MGI) research finds. This is a long-term trend that may intensify as workforces age. Without concerted efforts to boost productivity or increases in the workforce through higher participation or immigration, many advanced economies will struggle to maintain GDP growth (to match or exceed the relatively muted economic growth of the past decade), MGI chair Sven Smit and coauthors explain.
—Edited by Belinda Yu, editor, Atlanta
This email contains information about McKinsey's research, insights, services, or events. By opening our emails or clicking on links, you agree to our use of cookies and web tracking technology. For more information on how we use and protect your information, please review our privacy policy.
You received this newsletter because you subscribed to the Only McKinsey newsletter, formerly called On Point.
Copyright © 2024 | McKinsey & Company, 3 World Trade Center, 175 Greenwich Street, New York, NY 10007
by "Only McKinsey" <publishing@email.mckinsey.com> - 01:12 - 18 Jul 2024 -
RE: Seeking for Indonesian agent
Dear agent,
Greeting from Celia and GLA family. Trust you are doing great!
I sincerely invite you to join GLA platform and cover the increasing request from/to Indonesia from our GLA members.
Above 75% of GLA Members are first-class agents with wide range of primary resources. GLA fulfills all your needs in terms of finding reliable agents or getting huge volume of inquiries from/to your area. Have you ever tried to experience GLA Global Logistics Network for your business development and resources integration?
I am looking forward to hearing you back.
Best regards,
Celia Lee | GLA Overseas Department
Mobile: +86 181 2623 2027 (WhatsApp | WeChat)
Email: member358@glafamily.com
Website: www.glafamily.com
Conference Website: www.glafamily.com/GLA-Conference
Address: No. 2109, 21st Floor, HongChang, Plaza, No. 2001, Road Shenzhen, China
The 11th GLA Conference - Bangkok Thailand on 22th November 2024 – click here for registration
Ø The 10th GLA Conference in Dubai UAE, online album
Ø The 9th GLA Conference in Hainan China, online album
【Notice Agreement No 7】
7. GLA president reserves the right to cancel or reject membership or application. Company shall cease to be a member of GLA if:
a) the Member does not adhere to GLA terms and conditions.
b) the Member gives notice of resignation in writing to the GLA.
c) No good reputation in the market.
d) Have bad debt records in the GLA platform or in the market.
by "Celia Lee" <member358@glafamily.com> - 10:04 - 17 Jul 2024 -
JASA PENAWARAN PENERBITAN JAMINAN BANK GARANSI DAN ASURANSI TANPA AGUNAN
Kepada Yth,
Perusahaan Kontraktor BUMN & Swasta PT, LTD, TBKUp : Bagian Keuangan/Finance Manager
Perihal : Penawaran Jasa Penerbitan Bank Garansi & Surety Bond Tanpa Agunan
Dengan Hormat,
Kami dari PT. BERDIKARI BINA GARANSI (BBG) dengan NO NPWP : 92.067.813.2-001.000 dimana perusahaan kami telah resmi ditunjuk untuk memasarkan penerbitan BANK GARANSI dan SURETY BOND, dan Bank Garansi maupun Surety Bond yang Kami terbitkan telah diterima di lingkungan instansi BUMN, BUMD, BUMS, KPS, OIL& GAS,MABES TNI-POLRI,PERTAMINA,PLN, CHEVRON, CONOCO PHILIPS, STAR ENERGY DLL, Kami juga memberikan prosedur yang relative mudah yaitu TANPA AGUNAN (NON COLLATERAL) untuk semua jenis jaminan, serta proses cepat dan polis jaminan kami antar langsung ke perusahaaan bapak/ibu, dan dibawah ini kami informasikan tabel untuk rate dan Bank penerbit adalah sebagai berikut.
Tabel.1. LIST PENERBIT BANK GARANSI & PENERBIT JAMINAN ASURANSI
NAMA BANK
NAMA ASURANSI
BANK BTN
BANK BRI
Asuransi Jamsyar
Asuransi Askrindo
BANK BNI
BANK BCA
Asuransi Ramayana
Asuransi Jasindo
BANK MANDIRI
BANK JATIM
Asuransi bumida Asuransi Jasa Raharjaputra
BANK EXIM
BANK SINARMAS
Asuransi Vidie
Asuransi Jamkrindo
BANK BUKOPIN
BANK DLL
Asuransi Bosowa
Asuransi Aswata Dll
Tabel 2 . RATE (Service Charge)
Jenis Jaminan
Rate Bank Garansi
Rate Asuransi(Surety Bond)
Bid Bond
2,50 % / Tiga Bulan
0.25 % Pertiga Bulan
Performance Bond
3, % / Tahun
0.30% Pertiga Bulan
Advancepayment Bond
4. % / Tahun
0.35 % Pertiga Bulan
Maintenance Bond
3.50 % / Tahun
0.40 % Pertiga Bulan
Payment Bond
1.30% Pertiga Bulan
Kami juga dapat membantu proses penerbitan Asuransi Umum Lainnya dengan Rate negosiasi, adapun Produk Asuransi dimaksud adalah sebagai berikut :
a
Contaktor All Risk (CAR) + Third Party Liability (TPL)
b
Liability Insurance : Comprehensive General Liability (CGL)
Automobile Liability Insurance (AL)
Employer’s Liability Insurance (EL)
Workman Compensation Insurance(WCI)
c
Erection All Risk (EAR) Insurance
d
Personal Accident (PA) Insurance
Kami berharap penawaran ini dapat berakhir dengan kerjasama yang baik yang dapat memberikan keuntungan terhadap kedua belah pihak.Demikian penawaran ini kami buat, atas perhatian dan kerjasamanya kami ucapkan terimakasih.Hormat Kami.
PT. BERDIKARI BINA GARANSI
Jasa Bank Garansi & Surety Bond
Jl. Rawamangun MukaTimur No.43 Pulo Gadung Jakarta Timur
Kantor : +62 21. 22483622
HP/wa : 0878 3478 3854Email : dadangpramanaberdikari@gmail.com
by "dadangpramana berdikari" <dadangpramanaberdikari@gmail.com> - 09:48 - 17 Jul 2024 -
SoSafe Unifies Security, Compliance and Observability
Sumo Logic
Better Security with Sumo Logic
The strongest analytics deliver the strongest app.
With 100GB of daily log ingest, Standard Chartered needed hyperscalable observability with the highest security. They got it - and more - including:
- Millisecond responsiveness
- Delivered a unified platform for security, DevOps and customer experience
- Improved customer onboarding in just five months
Check out the full case study to learn more.
Learn more about Sumo Logic's Cloud Infrastructure Security.
Sumo Logic, Aviation House, 125 Kingsway, London WC2B 6NH, UK
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by "Sumo Logic" <marketing-info@sumologic.com> - 09:00 - 17 Jul 2024 -
What could it take to boost global productivity?
Only McKinsey
Fast-lane economies for productivity •
A ‘productivity miracle.’ In the latest McKinsey Global Institute report on productivity, McKinsey has taken a longitudinal look at global labor productivity going back 25 years. The big-picture view of productivity is that the world has grown much more productive and, as a result, much wealthier overall, McKinsey senior partners Chris Bradley and Olivia White reflect on an episode of The McKinsey Podcast. Globally, median country productivity is now six times more productive than it was 25 years ago.
—Edited by Belinda Yu, editor, Atlanta
This email contains information about McKinsey's research, insights, services, or events. By opening our emails or clicking on links, you agree to our use of cookies and web tracking technology. For more information on how we use and protect your information, please review our privacy policy.
You received this newsletter because you subscribed to the Only McKinsey newsletter, formerly called On Point.
Copyright © 2024 | McKinsey & Company, 3 World Trade Center, 175 Greenwich Street, New York, NY 10007
by "Only McKinsey" <publishing@email.mckinsey.com> - 01:25 - 17 Jul 2024 -
Consumer Electronics Show - CES 2024 (Post Show)
I had a chance to review your company profile and thought you might be interested in acquiring an updated Attendee list of Consumer Electronics Show - CES 2024?
Attendees are: - Analyst, Content Developer, Distributor, Buyer, Engineer, Manager/Store Manager/Product Manager, Manufacturer’s Representative, Service Technician, Systems Installer/Integrator & More.
List Contains: Name, Title, Email, Phone, Company Name, Physical Address, City, State, Zip Code, Country, Web Address, Employee Size, Revenue Size and Industry.
Please do let me know your interest. I’d be happy to send over our counts, accuracy, quality, pricing structure and more details on your request.
Waiting for your response
Thank you
Becky Walters - Event Manager
If you don’t want to receive further emails please revert with “Take Out” in the subject
by becky.walters@moreofdata.com - 02:40 - 16 Jul 2024 -
Re: Co-operation Offers
Hello , We are interested in your products and partnering with your company. We are a large wholesale and retail distributor with several stores around the world, our shops are well patronized. Kindly send us your catalogue to learn more about your products and price list along with stock availability. We hope that your products are of good quality as we look forward to a long-term partnership. Kindly contact our representative Mr. Kenny Lawson at kennyylawsonn@representative.com Best regards. Kind regards, Ethan Dexter Deputy Head of Purchase and Marketing Department www.selfridges.com Designer Fashion, Accessories & More - Shop Online at Selfridges Voted the best department store in the world, Selfridges has all the latest designer collections, must-have toys & gifts for all the family. www.selfridges.com
by "SELFRIDGES" <thndexter@gmail.com> - 02:26 - 16 Jul 2024 -
Where to get started with GenAI
Where to get started with GenAI
How to monitor AWS container environments at scale (Sponsored) In this eBook, Datadog and AWS share insights into the changing state of containers in the cloud and explore why orchestration technologies are an essential part of managing ever-changing containerized workloads.͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ Forwarded this email? Subscribe here for moreHow to monitor AWS container environments at scale (Sponsored)
In this eBook, Datadog and AWS share insights into the changing state of containers in the cloud and explore why orchestration technologies are an essential part of managing ever-changing containerized workloads.
Learn more about:Strategies for successfully tracking containerized AWS applications at scale
Key metrics to monitor for Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS)
Enabling comprehensive monitoring for AWS container environments with Datadog
Introduction to Generative AI
The world of Generative AI (GenAI) is moving at a breakneck pace.
New models, techniques, and applications emerge every day, pushing the boundaries of what's possible with artificial intelligence.
Considering this fast-evolving landscape, developers and technology professionals need to keep their skills sharp and stay ahead of the curve.
To help you get started with GenAI, Priyanka Vergadia and I have put together a concise guide covering essential steps, including:
Understanding the terminologies
Using the Model APIs
Building applications using the AI Models
Making models your own:
RAG
Fine-Tuning AI models
Here’s a sneak peek at all the cool topics we will cover.
Let’s start with the first step.
Also, don't forget to follow Priyanka Vergadia’s LinkedIn, which is a must-read for anyone working on cloud and GenAI.
Understanding the GenAI Terminologies
One of the biggest obstacles to getting started with GenAI is not understanding the basic terminologies.
Let’s cover the most important things to know about.
Artificial Intelligence
AI refers to the development of computer systems that can perform tasks that typically require human intelligence. It is a discipline like Physics.
It encompasses various subfields, such as Machine Learning, Natural Language Processing, Computer Vision, etc.
AI systems can be narrow (focused on specific tasks) or general (able to perform a wide range of tasks).
Machine Learning
Machine Learning is a subset of AI that focuses on enabling computers to learn and improve from experience without being explicitly programmed.
It involves training models on data to recognize patterns, make predictions, or take actions. There are three main types of ML:
Supervised Learning
Unsupervised Learning
Reinforcement Learning.
Lastly, there is Deep Learning, which uses artificial neural networks and is a subfield of Machine Learning.
The diagram below shows the key difference between a typical machine learning workflow and Deep Learning.
Natural Language Processing (NLP)
NLP is a subfield of AI that focuses on enabling computers to understand, interpret, and generate human language.
It involves tasks such as text classification, sentiment analysis, entity recognition, machine translation, and text generation.
Deep learning models, particularly Transformer models, have revolutionized NLP in recent years.
Transformer Models
Transformer models are a type of deep learning model architecture introduced in the famous paper “Attention is All You Need” in 2017.
They rely on self-attention mechanisms to process and generate sequential data, such as text.
Transformers have become the foundation for state-of-the-art models in NLP, such as BERT, GPT, and T5. They have also been adapted for other domains, like computer vision and audio processing.
GenAI
GenAI, short for Generative Artificial Intelligence, refers to AI systems that can generate new content, such as text, images, or music. It can be considered a subset of Deep Learning.
GenAI models can generate novel and coherent outputs that resemble the training data. They use machine learning models, particularly deep learning models, to learn patterns and representations from existing data.
NLP is a key area of focus within GenAI, as it deals with generating and understanding human language. Transformer models have become the backbone of many GenAI systems, particularly language models.
The ability of Transformers to learn rich representations and generate coherent text has made them well-suited for GenAI applications. For reference, a transformer model is a type of neural network that excels at understanding the context of sequential data, such as text or speech, and generating new data. It uses a mechanism called “attention” to weigh the importance of different parts of the input sequence and better understand the overall context.
There are various types of GenAI Models:
Language models that specialize in processing and generating text data. Examples include Google’s Gemini, GPT-4, Claude Opus, Llama3
Multimodal Models that can handle multiple modalities, like text, images, and audio. Examples include DALL-E, Midjourney, Stable Diffusion.
Audio Models that can generate and process speech, music, and other audio data. Examples: Google’s Imagen, Wavenet.
Prompt Engineering
Prompt engineering is the practice of designing effective prompts to get desired outputs from GenAI models. It involves understanding the model’s capabilities, limitations, and biases.
Effective prompts provide clear instructions, relevant examples, and context to guide the model’s output.
Prompt engineering is a crucial skill for getting the most out of GenAI models.
Using the Model APIs
Most Generative AI (GenAI) models are accessible through REST APIs, which allow developers to integrate these powerful models seamlessly into their applications.
To get started, you'll need to obtain API access from the desired platform, such as Google’s Vertex AI, OpenAI, Anthropic, or Hugging Face.
Each platform has its process for granting API access, typically involving
Signing up for an account
Creating an API key
Completing a verification or approval process.
Once you have your API key, you can authenticate your requests to the GenAI model endpoints.
Authentication usually involves providing the API key in the request headers or as a parameter. It's crucial to keep your API key secure and avoid sharing it publicly.
It’s also important to follow best practices to ensure reliability and efficiency. Here are a couple of important best practices:
Handle API errors gracefully by checking the response status code.
Optimize API usage by carefully selecting the model parameters, such as the maximum number of tokens. This is necessary to balance the desired output quality with costs.
When making API requests, be mindful of the rate limits imposed by the platform. Rate limits determine the maximum number of requests you can make within a specific time frame. Exceeding the rate limits may result in API errors or temporary access restrictions.
Use frameworks and libraries like Langchain to simplify the API interactions. These frameworks offer high-level abstractions and utilities for working with GenAI model APIs.
Building Application using the AI Model
There are several use cases for GenAI-powered applications across various domains:
Content Creation and Marketing: GenAI applications can help create outlines for articles, ad copy generation, and product descriptions.
Customer Support: AI-powered chatbots can understand user queries and provide accurate, context-aware responses.
Business and Finance: GenAI applications can help generate financial reports, summaries, or analyses based on company data.
Education and Learning: GenAI applications can generate customized learning material and explanations based on a student’s learning style.
Let’s say we want to build a chatbot application that uses an LLM to provide personalized book recommendations based on user preferences.
Here are the high-level steps involved.
1 - Choose an LLM Provider
Research and compare different LLM providers, such as Google AI, Open AI, or a Hugging Face.
Before choosing, you can consider multiple factors such as pricing, availability, API documentation, and community support.
2 - Set up the Development Environment
Typically, the LLM providers give access to their LLM via APIs.
You must sign up for an API key from the chosen provider and install the necessary libraries and frameworks.
For example, if you build your application using Python, you should set up a Python project and configure the API credentials according to the best practices.
3 - Design the Chatbot Conversation Flow
Plan out the conversation flow for the book recommendation chatbot. Define the key questions the chatbot will ask users to gather preferences, such as favorite genres, authors, or book themes.
Determine the structure and format of the chatbot’s responses, including the recommended books and any additional information to provide.
4 - Implement the Chatbot Application
Use a web framework like Flask or Django to build the chatbot application.
Create a user interface for the chatbot, either as a web page or a messaging interface. Implement the necessary routes and views to handle user interactions and generate chatbot responses.
5 - Integrate the LLM
Most LLM providers have released libraries to talk to their model APIs. Initialize the model with the appropriate parameters, such as the model name, version, and temperature.
Define the prompts and instructions for the LLM to generate personalized book recommendations based on user preferences.
For example, you can create prompts like: “Recommend a science fiction book for a user who enjoys fast-paced plots and space exploration.”
Pass the user’s preferences and the prompts to the LLM using the API and retrieve the generated book recommendations.
6 - Process and Display the Recommendations
Process the LLM-generated book recommendations to extract the relevant information, such as book titles, authors, and descriptions.
Display the recommended books in a clear and visually appealing format. Provide options for users to interact with the recommendations, such as saving them for later or requesting more details about a specific book.
7 - Refine and Expand
Test the chatbot application with various user preferences and prompts to ensure it generates relevant and diverse book recommendations.
Gather user feedback and iterate on the chatbot’s conversation flow, prompts, and recommendation formatting based on suggestions.
Integrate additional features, such as providing book reviews, suggesting similar authors, and so on, to expand the chatbot's capabilities.
8 - Deploy and Monitor
Deploy the chatbot application to a hosting platform or cloud service provider, making it accessible to users via a web URL.
Set up monitoring and analytics to track user interactions, chatbot performance, and any errors or issues.
Regularly update the LLM prompts and application logic based on user feedback and new book releases.
Making Models Your Own
There is significant interest in making models more adaptable and customizable to suit the specific needs of the domain.
Let’s look at the main techniques to achieve this goal.
Retrieval-Augmented Generation (RAG)
RAG is a technique that helps improve the accuracy and relevance of the generated responses based on your use case.
It allows your LLM to have external information sources like your databases, documents, and even the Internet in real time. This way the LLM can get the most up-to-date and relevant information to answer the queries specific to your business.
Here’s a high-level overview of how a RAG system works:
The user poses a question to the RAG system.
The retrieval component searches the knowledge corpus using the question as a query and retrieves the most relevant passages or documents.
The retrieved passages go through the augmentation step where this information is fed as input to the large language model. This step is crucial as it augments the model’s knowledge with relevant context from external sources.
The language model processes the input and generates an answer by combining the information from the retrieved passages and its base knowledge.
The generated answer is returned to the user.
RAG has shown promising results in improving the accuracy and relevance of generated responses, especially in scenarios where the answer requires synthesizing information from multiple sources. It leverages the strengths of both information retrieval and language generation to provide better answers.
Fine-Tuning AI Models
Fine-tuning a base model on domain-specific data is a powerful technique to improve the performance and accuracy of AI models for specific tasks or industries.
Let’s understand how it’s done.
1 - Understanding Base Models
Base models, also known as pre-trained models, are AI models that have been trained on large, general-purpose datasets.
These models have learned general knowledge and patterns from the training data, making them versatile and applicable to a wide range of tasks.
Examples of base models include Google’s BERT and GPT, which have been trained on massive amounts of text or image data.
2 - The Need for Fine-Tuning
While base models are powerful, they may not always perform optimally for specific domains or tasks.
The reasons for fine-tuning a foundation model are as follows:
Adding a specific task (such as code generation or content generation) to the foundation model.
Generating responses based on your company’s proprietary dataset.
Adapting to the unique vocabularies, writing styles, or data distribution that might differ in your specific use case.
Reducing hallucination, which is output that is not factually correct or reasonable.
Fine-tuning allows us to adapt the base model to better understand and generate content specific to a particular domain.
3 - Fine-Tuning Process
The fine-tuning process consists of several steps such as:
Data Preparation: Collect a dataset that is representative of the target domain or task while ensuring that it is of sufficient size and quality. Preprocess the data to match the input requirements of the base model.
Model Initialization: Start with the pre-trained base model that is most suitable for the target task. Load the pre-trained weights of the base model.
Training: Feed the domain-specific dataset into the modified base model and train the model using techniques like transfer learning. Fine-tune the model’s parameters by backpropagating the errors and updating the weights based on the domain-specific data.
Evaluation and Iteration: Evaluate the fine-tuned model’s performance on a validation set from the domain-specific data. Based on the metrics, iterate on the fine-tuning process.
4 - Benefits of Fine-Tuning
There are significant benefits to fine-tuning:
It allows the model to capture the nuances and characteristics of the target domain, leading to better accuracy and performance on domain-specific tasks.
Starting with a pre-trained base model, fine-tuning requires less training data and computational resources than training a model from scratch.
Fine-tuning enables the model to leverage the knowledge learned from the general-purpose training data and adapt it to the specific domain.
Conclusion
In conclusion, getting started with Generative AI is an exciting journey that opens up a world of possibilities for developers and businesses alike.
By understanding the key concepts, exploring the available models and APIs, and following best practices, you can harness GenAI's power to build innovative applications and solve complex problems.
Whether you're interested in natural language processing, image generation, or audio synthesis, there are numerous GenAI models and platforms to choose from. You can create highly accurate and efficient AI solutions tailored to your specific needs by leveraging pre-trained models and fine-tuning them on domain-specific data.
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by "New Relic" <emeamarketing@newrelic.com> - 05:06 - 16 Jul 2024 -
How promising are new carbon removal techniques?
Only McKinsey
Rocks, oceans, and other solutions •
Climate interventions. To help combat climate change, scientists are exploring technologies to manipulate the environment, such as preventing some solar radiation from reaching Earth and adding iron to oceans to move carbon dioxide to the seabed faster, although uncertainties remain about effectiveness and safety. In Iceland and Texas, massive machines aim to extract carbon from the air and store it underground—and many more such facilities are planned—the New York Times reports. [NYT]
•
Economic accelerator. Carbon dioxide removal will ideally turn out to be a means of economic acceleration, as well as a critical tool in combating climate change, McKinsey senior partner Mark Patel explains on an episode of The McKinsey Podcast. Removal can play a crucial role in addressing hard-to-abate emissions from sectors reliant on fossil fuels or from activities that create emissions and are essential to daily life and economic growth, such as heating and lighting.
—Edited by Jana Zabkova, senior editor, New York
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by "Only McKinsey" <publishing@email.mckinsey.com> - 11:08 - 15 Jul 2024 -
Introducing our 7-week series to excelling as a leader
Become the most effective leader you can be To further help our readers find the insights they need to thrive in today’s complex business environment, we were delighted last month to introduce McKinsey Publishing Guides, a free email series dedicated to personal and professional development. Since that time, tens of thousands of subscribers have engaged in our inaugural guide, and we’ve incorporated their feedback into our newest offering: “The McKinsey Publishing Guide to excelling as a leader.”
Being a leader is arguably more challenging than it’s ever been. This series features a concise set of emails—one per week, for seven weeks—focusing on the skills you need to become the most effective leader you can be. You’ll explore expert insights and advice on topics that should be at the top of your agenda, including gen AI, digital transformations, the changing talent landscape, growth and resilience, and more. A wrap-up quiz at the end of the series will test your learning as you begin to apply your new skills to your day-to-day challenges.At the end of this seven-week series, you’ll know how to:
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by "McKinsey & Company" <publishing@email.mckinsey.com> - 10:54 - 15 Jul 2024 -
Hiring for tomorrow’s jobs, today: A leader’s guide
Leading Off
Skills for hire The mismatch between an organization’s people needs and the supply of available talent (or the lack thereof) is by no means a new problem for leaders and their companies. But given the rapid rise of generative AI (gen AI) and longer-term macroeconomic trends, it’s not poised to resolve itself anytime soon. In this current labor market and business environment, how can companies find people with the right skills, technical and otherwise, to succeed in the workplace? This week, we look closely at the merits of moving from credentials- to skills-based hiring and how companies can prepare today’s employees for the jobs of the future.
While workers’ preferences and companies’ skill needs seem to be changing at breakneck speed, these issues are not just a pandemic-induced flash in the pan. After analyzing data from 30 advanced economies, McKinsey senior partners Chris Bradley, Olivia White, and Sven Smit and their colleagues explain that the nearly two-decade-long trend of tightening labor markets shows no signs of slowing down. With a dwindling surplus of excess workers, how can employers best address the talent shortages of today and tomorrow? According to the authors, companies would do well to focus on the employees they already have, by creating opportunities for mobility, upskilling, and reskilling and by offering more flexibility. Another just-as-important strategy is hiring based on skills, rather than on a candidate’s bona fides. By focusing on their own underlying skill needs, companies can better source certain skills (especially technical ones) and create opportunities for employees in traditionally untapped pools of talent.
That’s the potential number of job transitions that Europe and the United States each could see by 2030. In a recent report from the McKinsey Global Institute, senior partners Dana Maor, Eric Hazan, and Sven Smit and their colleagues predict that changes in labor demand—due to trends ranging from AI and automation to infrastructure spending and aging populations—will require millions of employees to move from one job, or even one career path, to another. These shifts go hand in hand with changing demand for specific skills: namely, the technological, social, and emotional skills that the authors predict workers will need most in the future.
That’s McKinsey’s Bryan Hancock on the philosophy behind skills-based hiring. In a recent episode of McKinsey Talks Talent, he and senior partner Brooke Weddle make the case for hiring based on the skills needed to succeed in a job, rather than on a college degree or work experience. With skills-based hiring at scale, one important place for companies to start is their approach to learning and development (L&D). “People are hiring for the most critical skill or the hardest-to-find skill. They’re not hiring for all of the skills in a given role,” Weddle says. “There’s a real role for L&D to give you all the other skills you need. There are things you haven’t learned since you haven’t been there for five years. That’s a core part of skills-based hiring, as well.”
In a working world that’s constantly evolving, mindset shifts are critical to success—for employers and employees alike. So says Deanna Mulligan, former chair and CEO of Guardian Insurance, in her book about closing the skills gap. On the employer side, she notes the importance of skills-based recruiting and hiring: “We can’t really rely on job titles and what people have done in the past to give us clues about whether they’ll be able to do jobs that don’t exist yet.” For employees, especially those who are starting out, a mindset of flexibility and resilience is essential to job success and satisfaction. “They’re not going to graduate with a degree in one area and be employed in that area for the rest of their lives,” Mulligan says of today’s youngest workers. “We’re all going to be learning, exploring, adding new skills to our portfolios. Changing jobs, changing careers.”
For all the value of skills-based hiring, there’s an important distinction to make between hiring for softer skills and hiring based on personality. With the former, soft skills can be harder to assess than other skills, but they are especially critical to organizations; meanwhile, the latter introduces a range of biases into the hiring process. Sure, personality hires have their charms: they are adept at building relationships and improving workplace culture, according to a recent survey. But they might also embitter more traditional hires who believe that charisma is valued more highly than quality of work, which can be bad for morale and for business.
Lead by closing the skills gap.
— Edited by Daniella Seiler, executive editor, Washington, DC
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by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 04:39 - 15 Jul 2024 -
Do you know which country has the highest level of gender equality?
Only McKinsey
4 myths about women’s careers •
More than a century. Without meaningful change, attaining full gender parity could take another 134 years, according to the World Economic Forum’s 2024 report on the global gender gap, released in June. One of the biggest gaps is in women’s representation and leadership in the workplace. Globally, women trailed men in virtually all sectors and economies studied, representing 42% of the labor force and about 32% of senior leaders. [WEF]
•
Underrepresented. Now in its ninth year, the Women in the Workplace report from McKinsey, in partnership with LeanIn.Org, is the largest study of women in corporate America and Canada. This year’s research reveals some hard-fought gains at the top, with women’s representation in the C-suite at the highest level it has ever been. However, with progress in the middle of the pipeline still lagging behind, coupled with a persistent underrepresentation of women of color, true parity remains painfully out of reach, McKinsey senior partner Lareina Yee and coauthors explain.
•
The broken rung. One common myth is that the glass ceiling represents the biggest barrier to women’s advancement. In reality, the “broken rung” at the first critical step up to manager is women’s greatest obstacle to reaching senior-leadership positions. Progress for early-career Black women remains the furthest behind, with only 54 Black women promoted for every 100 men. Discover four myths about women’s workplace experiences, and for a country-level exploration of the women’s health gap, which greatly affects working-age women, visit this McKinsey Health Institute interactive.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:12 - 15 Jul 2024