The Ravit Show

Host of "The Ravit Show" I love to interview folks on my LinkedIn and YouTube Live. Anything and everything that's trending can be a topic on the show.

Founder and Host

AtScale

I look after social media, content, podcast production and community!

Head Community Evangelist

Gartner

Gartner Ambassadors are active leaders who are eager to drive the discussion, share their knowledge, make new connections, and build their personal brand.

Gartner Ambassador

EM360

Board Advisor

Story by Data

Affiliate Member

Open Data Science Conference (ODSC)

Packt

Senior Content Marketing Editor

As a Content Marketing Editor in the Expert Network team and look after content marketing materials produced by and for Expert Network authors and their titles. I am responsible for a portfolio of content marketing assets for each title, liaising with Producers, Technical Editors, and the wider EN team, and individual authors and technical reviewers as necessary. I am also responsible for liaising with other teams in the business to make appropriate marketing content available, on a title by title basis, as requirements arise, and sourcing further material where there are gaps.

- Create a repository of content marketing assets for each title, visible and available to other stakeholders
- Maintain and develop further content marketing assets as required in support of the individual title marketing for each EN author
- Ensure the completeness, accuracy, and availability of all promotional title information and materials for Expert Network titles.
- Create and maintain the Expert Network forthcoming catalogue to share with internal and external partners.
- Create, source, and curate content for the Outreach team, PTN team, Direct marketing team, and social channels.

Responsibilities:

I work with the Producer and Technical Editor of a title, and other team stakeholders, to pre-emptively produce or curate the following assets for each title across the lifecycle of the project:

- Content articles for the Hub and other partner channels in phases right from pre-pub to post-pub.
- Generate podcasts for authors to talk about the topic and promote the book.
- Providing a suggested social content plan across Expert Network titles, for integration and execution by the Packt social team
- An early pre-release extract, a “sneak preview” in the month before launch, and a highly shareable article immediately post-launch.
- An article based on an author interview with questions provided by the editor.
- The content and images for an Amazon A+ page, based on the author interview.

Content Marketing Editor

- Primary and secondary research on new development in the field of tech
- Getting insights from an industry expert on your projects to give an edge and value to your project making it unique
- Preparing questionnaires for the authors and the clients on our product line
- In-depth market forecasting and analysis of various sectors
- Creating roadmaps while working closely with multiple stakeholders on different projects in accordance with the TAT
- Prepared market engineering sheets at regular intervals that help you visualize complex data via MS Office Tools
- Internal and external stakeholder management including presentation to CXOs
- Understanding the business need, evaluating various processes and controls present in the organization, communicating control deficiencies and other issues encountered during the research process
- Keeping a tap on the commercial viability of the products

Associate Category Manager

S K Patodia & Associates

- Look after different domains of the business with accordance to the clients
- Managing team and preparing questionnaires for the training of research
- Handling accounts and budgets related to the projects
- In-depth primary research related to the projects
- Handling clients PAN India, while closely associating with internal stakeholders
- Forecast new areas in terms of commercial viability

Assistant Manager Research 

Hexa Research Pvt. Ltd.

- Prepare reports on different domains such as media, ICT, Chemical, Tech, FMCG and consumer insights
- Handling client reports while closely working with different internal and external stakeholders 
- To prepare reports with accordance with the client requirements from across the United States, Europe, APAC, Middle East
- Market forecasting considering the factors affecting the industries by Primary Research
- Understand the budget aspects as per the client's requirement
- Training the team and finishing the projects in given turn around time

Research Associate

Reliance Industries Limited

Working for Indirect Taxation on CENVAT credit input.

Finance Intern

Founder & Host of "The Ravit Show". I love to interview Data/AI Leaders on The Ravit Show. Hosted 200+ Data Science/AI leaders on The Ravit Show.

Ravit Jain is well known for --

- Hosting live events
- Data Community Building
- Community Evangelism
- Product Marketer
- Share FREE resources with Community
- Helping Data Science/AI Community

You can reach out to me at Ravitjain0909@gmail.com for The Ravit Show queries :)

Important Data Terms Explained! What would you like to add?

1. Data Pipeline: Manages the flow of data from collection to storage destinations like data lakes or warehouses.
2. Database, Schema, Table:
- Database: Stores and manages structured data.
- Schema: Defines the structure and rules of a database.
- Table: Organizes data in rows and columns within a database.
3. ETL vs. ELT:
- ETL: Extract, transform, then load data into a system.
- ELT: Extract, load data into a system, then transform it.
4. Data Lake vs. Data Warehouse vs. Data Mart:
- Data Lake: Stores large volumes of raw data.
- Data Warehouse: Optimized for querying structured data.
- Data Mart: Focused subset of a data warehouse for specific functions.
5. Batch vs. Stream Processing:
- Batch: Processes data in scheduled chunks.
- Stream: Processes data in real-time as it arrives.
6. Data Quality: Ensures data meets standards for its intended use.
7. Data Modeling: Designs data organization for efficient analysis.
8. Data Orchestration: Coordinates data movement and integration across systems.
9. Data Lineage: Tracks data’s journey and transformations throughout its lifecycle.
10. Git: Manages code collaboration and tracks changes.

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#data #ai #dataengineering #theravitshow

Ravit Jain

Data Engineering Terms

How well does your organization manage its data?

Let's walk through each part:

1. These foundational elements ensure that the data within the organization is well-managed, discoverable, and reliable:

-- Policies: Define the rules and guidelines for data usage and management.
-- Data Quality: Ensures the accuracy, consistency, and reliability of data.
-- Catalog: A centralized repository where all data assets are listed and described.
-- Glossary: Provides standardized definitions for terms across the organization.
-- Marketplace: A platform where data products and services can be shared or exchanged.
-- Lineage: Tracks the data's journey from its origin to its final destination, ensuring traceability.

2. Data Source: This is where it all begins. Data originates from various sources such as:

-- Operational Databases: Traditional databases that store day-to-day transactional data.
-- Files: Data stored in various file formats.

3. Data Lake
A data lake is a storage repository that holds a vast amount of raw data in its native format:

-- Landing Zone: The first stop for incoming data, where it is collected and held temporarily.
-- Persistent Staging Area: A more stable storage area for data before it is processed further.

4. Lake House
Combining the best of data lakes and data warehouses:

-- Business Data Vault: A structured environment that organizes and stores business-critical data.
-- Raw Data Vault: Stores raw, unprocessed data for future use or processing.

5. Data Warehouse: A more structured environment where data is refined and organized for analysis:

-- Data Marts: Subsets of the data warehouse that focus on specific business areas or needs.

6. Data Consumers: These are the end-users of the data:

-- Business Intelligence Tools: Help in analyzing the data and making informed decisions.
-- Apps: Use the data to provide various services and functionalities.
Business Users: Leverage the insights generated from the data to drive business strategies.

7. Event Bus: Acts as a communication system, allowing different parts of the platform to exchange information in real-time.

8. Stream Analytics & Machine Learning

-- Stream Analytics: Processes data streams in real-time to provide immediate insights.
-- Machine Learning: Uses historical data to train models that can predict future trends or behaviors.

9. Supporting Components: These elements ensure that the platform runs smoothly and securely:

-- Access Management: Controls who can access what data.
-- Encryption: Secures data from unauthorized access.
-- Backup/Restore: Protects data from loss by regularly backing it up.
-- Compute: Provides the necessary processing power.
-- Storage: Where the data is physically stored.
-- Networking: Ensures all components can communicate with each other.
-- Monitoring: Keeps track of the platform's health and performance.

What do you add?

How does the inner workings of a Machine Learning model work?

INCOME and LOAN are the two essential variables we're exploring with this model. 

The goal is to predict the income of individuals based on various input features, including age, sex, and a score, along with their loan status.

Here's a step-by-step breakdown of how we build the model:

Initial Dataset: We start with a dataset containing information about individuals, including their income, loan status, age, sex, and score.

Exploratory Data Analysis (EDA): We perform thorough EDA to gain insights into the data, spot patterns, and identify potential challenges.

Data Cleaning: We ensure the dataset is free from errors, inconsistencies, and missing values.

Data Curation: Redundant features are removed to streamline the dataset and enhance model performance.

Pre-Processed Dataset: We preprocess the data using techniques like PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) to reduce dimensionality and extract meaningful features.

Use as Training Set: After preprocessing, we split the dataset into a training set and a test set. The training set is used to teach the model patterns and relationships in the data.

Learning Algorithms & Hyperparameter Optimization: We apply various learning algorithms, such as SVM (Support Vector Machine), LR (Logistic Regression), KNN (K-Nearest Neighbors), DT (Decision Trees), and RF (Random Forest). The hyperparameters of these models are fine-tuned using grid search to achieve optimal performance.

Feature Selection: We select the most relevant features that significantly impact the outcome to avoid overfitting and improve interpretability.

Cross-Validation Model: To validate the model's robustness, we employ cross-validation techniques to measure its generalization performance.

Trained Model & Predicted Y values: Once the model is trained, we use the test set to make predictions on unseen data.

Evaluation Metrics: We evaluate the model's performance using various metrics like classification accuracy, sensitivity, specificity, MCC (Matthews Correlation Coefficient), RMSE (Root Mean Squared Error), MSE (Mean Squared Error), and R² (R-squared) for regression tasks.

Regression: We explore the relationship between input features and income through regression analysis.

Evaluate Model Performances: We assess how well our model performs on the given dataset and make necessary adjustments if needed.

Additional Models: We also experiment with Random Search and Gradient Boosting (GB) to compare their performances with RF.

The final model is ready to make predictions on new data, helping us gain insights into the income levels of individuals and their loan status.

Machine Learning offers limitless opportunities, and we are thrilled to leverage this model for meaningful real-world applications!

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Why is RAG hot?

Let's understand RAG Indexing in simple terms!

Think of RAG Indexing like organizing a huge library. With so many books, how do you find the one answer you need? Indexing helps the AI sort through this vast information quickly and efficiently.

How Indexing Operates in RAG

Breaking Down Data (Chunking): This is like taking each book in our library and marking important sections or pages. We make the data smaller and more manageable.

Turning Data into Codes (Embeddings): Next, we convert these sections into special codes. These codes capture the essence of the text, making it easier for AI to understand and search.

Various Indexing Techniques:

- Rule-Based Indexing: Using simple rules like spacing or punctuation to break the text.
- Recursive Structure-Aware Indexing: Combining fixed-length and structure-aware approaches for more contextual chunking.
- Content-Aware Indexing: Tailoring the method based on the type of text, like for web pages or PDF documents.
- Different Forms of Representing Data: Sometimes, instead of indexing the whole text, we might use a summary or an abstract for quicker access.

Advanced Techniques (Specialized Embeddings): Enhancing AI’s understanding by fine-tuning it for specific types of information.

Layered Indexing Approach (Hierarchical Indexing): Imagine creating a map of the library, where each section is summarized. This helps in finding the right answer even faster.

Why Does Indexing Matter?
Just like a well-organized library lets you find a book faster, good indexing helps AI swiftly pinpoint the right information. It's all about efficiency and accuracy in the vast world of data.

What’s Next?
After setting up our 'library' with indexing, the next adventure is Retrieval - how our AI librarian finds the exact book (or answer) we need. More on this soon!

I hope this analogy sheds light on the role of RAG Indexing in making AI more capable and resourceful.

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#data #ai #vector #rag #theravitshow

Learn about the 25 AI Terms!

— Artificial Intelligence is the simulation of human intelligence in machines, enabling them to perform tasks like learning, reasoning, decision-making, and problem-solving autonomously

— Machine Learning is a subset of AI focused on teaching machines to learn from data and improve performance over time without explicit programming

— Deep Learning is a type of machine learning that uses neural networks with many layers to analyze complex patterns in large datasets

— Neural Networks mimic the human brain's structure and function, recognizing patterns and solving complex problems in data

— NLP is a field of AI that enables machines to understand, interpret, and generate human language, facilitating human-computer interactions

— Computer Vision trains machines to interpret and understand visual information from the world, such as images and videos

— Supervised Learning is a type of machine learning where models are trained on labeled data, meaning input data is paired with the correct output

— Unsupervised Learning involves training models on unlabeled data, allowing them to find patterns and relationships independently

— Reinforcement Learning involves an agent learning to make decisions by receiving rewards or penalties for its actions in an environment

— Data Mining is the process of discovering patterns and insights from large datasets using techniques from statistics, machine learning, and database systems

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#data #ai #dataanalytics #theravitshow

25 AI Terms

Are you looking to optimize your database for better performance and scalability? Here's a quick cheat sheet to get you started:

- Vertical scaling: Boost server capacity with more CPU, RAM, or storage to manage increased load

- Materialized views: Speed up data retrieval by storing precomputed results of complex queries

- Replication: Distribute read traffic and enhance performance by creating copies of your primary database on multiple servers

- Database caching: Improve application response times by storing frequently accessed data in a faster storage layer

- Indexing: Optimize search and retrieval by analyzing query patterns and creating the right indexes

- Denormalization: Increase query performance by simplifying complex joins and storing redundant data

- Sharding: Balance load and improve scalability by distributing data across multiple servers or databases

Each of these techniques plays a crucial role in ensuring your database can handle growing demands efficiently. 

What strategies have you found most effective in scaling your databases?

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#data #ai #databases #theravitshow

How well do you know your machine learning hyperparameters? Here’s a quick guide to key ones:

1. Linear Regression: Important hyperparameters include L1/L2 Penalty, Solver, and Fit Intercept

2. Logistic Regression: Key hyperparameters are L1/L2 Penalty, Class Weight, and Solver

3. Naive Bayes: Adjust hyperparameters such as Alpha, Binarize, and Fit Prior

4. Dense Neural Networks: Focus on Hidden Layer Sizes, Activation Functions, Dropout Rate, and Solver

5. Decision Tree: Tune hyperparameters like Criterion, Max Depth, Min Sample Split

6. Gradient Boosted Trees: Key hyperparameters include Criterion, Max Depth, N Estimators, and Learning Rate

7. K-Means: Important hyperparameters are N Clusters, Max Iter, and Init

8. K-Nearest Neighbor: Key hyperparameters include N Neighbors, Weights, and Algorithm

9. Principal Component: Tune hyperparameters like N Component and Iterated Power

10. Random Forest: Important hyperparameters are Criterion, Max Depth, N Estimators, and Max Features

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#data #ai #databases #theravitshow

How can I leverage Snowpark with Python to design and deploy efficient data pipelines, mature machine learning models, and secure data applications? Your answer is "The Ultimate Guide to Snowpark". My friend  Shankar Narayanan SGS and Vivekanandan Srinivasan

This book is an excellent resource for anyone looking to master Snowpark, especially when using Python to build and deploy data workloads within the Snowflake Data Cloud. 

The authors have done a remarkable job of making complex topics accessible, with clear explanations and practical examples that guide you through using Snowpark effectively in real-world scenarios.

The book starts with the basics of Snowpark and gradually introduces more advanced features. It covers a wide range of topics, from data manipulation and engineering to deploying machine learning models and building data applications. 

The content is written in a straightforward way, making it easy to follow, even if you're relatively new to Snowpark. By the time you finish the book, you’ll have a solid understanding of how to use Snowpark to enhance your data projects and take full advantage of Snowflake's capabilities.

One of the book’s strengths is its focus on practical knowledge. You’ll learn how to leverage Python within Snowpark to perform a variety of tasks, develop robust data pipelines, and deploy reliable machine-learning models in production environments. 

The authors also show you how to build native data applications within Snowflake, which can be a valuable skill for monetizing your expertise. Additionally, the book covers deploying and managing containers in Snowpark, which is crucial for modern data practitioners.

If you’re a data engineer, data scientist, or developer looking to deepen your understanding of Snowpark, this book is worth reading. The authors assume that you have a basic knowledge of SQL, are proficient in Python, and have some familiarity with Snowflake, which helps in grasping the concepts quickly. 

Overall, "The Ultimate Guide to Snowpark" is an invaluable resource for anyone serious about advancing their skills in data engineering, data science, and application development using Snowpark. 

The authors have put together a detailed, practical guide that is sure to become a go-to reference for professionals working with Snowflake. If you're looking to elevate your Snowpark skills, this book is an excellent place to start.

#data #ai #snowpark #snowflake #theravitshow

How can real-time data transform your business? I’m super excited to share an incredible book "Building Real-Time Analytics Systems" Download it here -- https://stree.ai/4bbBbJ5. Having the ability to process and analyze data in real time is a game-changer. It’s all about making informed decisions, staying ahead of the competition, and driving innovation.

Why should you care about this? 🤔

-- Timely Insights: Real-time analytics means you get immediate insights, helping you make quicker and smarter decisions

-- Enhanced Customer Experience: Imagine being able to offer personalized experiences to your customers in real time. This book shows you how

-- Operational Efficiency: Real-time data processing helps you identify and solve issues on the fly, keeping operations smooth and efficient

-- Competitive Edge: Leveraging real-time analytics keeps you ahead of the curve, ready to adapt to market trends swiftly

And the best part? You can now get this comprehensive guide for FREE! 🎉

I highly recommend grabbing your copy and diving into the world of real-time data. It’s a fantastic opportunity to boost your data strategy and stay ahead in the game.


#data #ai #realtime #analytics #theravitshow

How do you generate TRUST in Generative AI?

I had the pleasure of interviewing Amr Awadallah, and Amin Ahmad, Co-Founders of Vectara on The Ravit Show at Incorta's NoLimit Conference yesterday. Complete the episode link in the comments!!!!

Here are some key insights from our conversation:

- We discussed how Vectara defines trust in generative AI systems. They emphasized the importance of reliability, transparency, and accountability in building trustworthy AI solutions.

- Amr and Amin highlighted the challenges that bias, copyright issues, and hallucinations pose for generative AI. They provided insights into how these challenges can impact the effectiveness and ethical considerations of AI applications.

- We explored how control enhances the utility and adaptability of generative AI. They explained the mechanisms Vectara employs to ensure AI systems remain flexible and responsive to user needs.

- Automating hallucination detection is crucial for improving real-world applications of generative AI. Amr and Amin shared how Vectara’s technologies help identify and mitigate hallucinations in AI outputs.

- The focus on trust and control at Vectara complements Incorta’s solutions perfectly. They discussed how their partnership leverages these strengths to deliver robust and trustworthy AI-driven insights.

A big thank you to Amr and Amin for sharing these valuable insights and to the Incorta team for hosting such an engaging event!

Stay tuned for more updates from the NoLimits event!

#data #ai #nolimits #incorta #theravitshow

- Are you ready to turn your AI ambitions into reality? 
- How does AI adoption work? 
- How do enterprise leaders implement AI?
- Is AI hyped?

These are some common questions we have heard so many times.

I had the pleasure of hosting an insightful conversation with AI expert Ben Dooley from InfoCepts on The Ravit Show. 

Ben shared invaluable insights and practical tips on how to turn your AI ambitions into reality. Whether you’re just starting or looking to elevate your existing AI strategy, the episode is packed with actionable advice to help you achieve real, tangible results.

Catch the replay now and take your AI journey to the next level --  https://lnkd.in/dQt86Eaj

#data #ai #infocepts #theravitshow

How important is Data Quality? Learn from the best enterprise leaders in the space!

I’m thrilled to announce the launch of a brand-new Data Quality series on The Ravit Show! This series has been created in response to the growing demand from our community, Register here for the event -- https://lnkd.in/dcw-bzK2

Joining me as my co-host is the incredible Mona Rakibe, Co-Founder & CEO of Telmai, who has been a passionate advocate for data quality and is at the forefront of helping enterprises achieve excellence in this space. Together, we’ll be diving deep into the challenges and opportunities that data quality presents in the age of AI.

Episode 1 – What to Expect:

We have an all-star lineup of guests, including:

- Ellen Nielsen - Chief Data Officer, Top 100 Influential People in Data, USA
- Miguel Navarro - Innovator, Patented Inventor, Seasoned Digital Leader
- Appal Badi - Head of Enterprise Data Products, First Citizen Bank, Silicon Valley (Formerly)

We’ll discuss everything from data reliability and observability to the impact of AI on foundational data needs. And of course, there will be plenty of actionable insights and thought-provoking questions to keep the conversation dynamic and unscripted.

👉 Mark your calendars and join us for the live broadcast on August 28th!

#data #ai #dataquality #theravitshow

I recently had the pleasure of interviewing Neil Carson, CEO of Yellowbrick Data on The Ravit Show at their Mountain View office. Our conversation covered some fascinating insights about the company and its vision for the future of data warehousing.

What did we cover in the interview -- 

- I kicked off by asking Neil to introduce himself and share a bit about Yellowbrick. His passion for driving innovation in the data space was evident as he explained how Yellowbrick is redefining the data warehouse landscape with their unique approach.

- One of the key questions I had was why the world needs another data warehouse vendor. Neil highlighted the significant challenges that Yellowbrick is addressing, particularly with their Private Data Cloud. He emphasized the increasing need for secure, efficient, and scalable data solutions in a world where data volumes are growing exponentially.

- We also discussed the importance of Kubernetes to Yellowbrick's architectural vision. Neil elaborated on how Kubernetes enables their platform to be multi-cloud and hybrid, supporting open platforms and providing unparalleled flexibility and scalability. This approach allows businesses to deploy and manage their data infrastructure seamlessly across different environments.

Finally, I was curious about how a smaller startup like Yellowbrick competes with giants like Redshift, Snowflake, and Google. Neil's response was inspiring. He spoke about the agility, innovation, and customer-centric focus that drives Yellowbrick to deliver exceptional performance and value, setting them apart in a competitive market.

It was a truly enlightening conversation, and I'm excited to see how Yellowbrick continues to innovate and lead in the data warehousing space.

#data #ai #datawarehouse #yellowbrick #theravitshow

What’s driving over a 1,000% increase in ML models going into production this year? 🤔

🚀 Excited to announce an upcoming special episode of The Ravit Show where we’ll dive deep into the State of Data + AI Report with some incredible minds from Databricks! Register now for the event -- https://lnkd.in/dqzDmied

We’ll be unpacking some fascinating trends, including:

- The massive increase in ML model deployment—what’s behind this trend?
- How are companies actually approaching GenAI, and what should they consider as they refine their strategies?
- Industry-specific insights that reveal where data and AI are headed next.

And the best part? You can access the State of Data + AI Report for FREE -- https://lnkd.in/dpK4tJnx

Don’t miss out on this engaging conversation that promises to deliver actionable insights and expert perspectives.

#data #ai #databricks #theravitshow

How does Uber Eats delivery work? Check out my discussion with Kishore Gopalakrishna, Co-Founder and Chinmay Soman, Head of Product from StarTree on The Ravit Show. We delved into the development of Apache Pinot, recent enhancements, and what the future holds for real-time analytics. Also, great announcements made at the RTA Summit. 

- Vector Index Support: Enhancing Apache Pinot with new capabilities for AI-related use cases, such as Generative AI (GenAI) Integration, Chatbots, and Recommendation Systems.
​StarTree ThirdEye General Availability: An advanced real-time metrics monitoring, anomaly detection, and root-cause analysis tool designed to increase operational excellence across industries.

Tune in to gain valuable insights and participate in the discussion. Full episode in comments!

#data #ai #startree #theravitshow

How does Language Model-Based Clinical Prediction work?

- Data Collection: Clinical data is gathered from electronic health records (EHR) and medical notes, forming the basis for model training.

- Pretraining: The language model undergoes pretraining using masked language modeling techniques to learn from clinical text data.

- Fine-tuning: The pre-trained model is fine-tuned with task-specific labels, optimizing it for clinical predictions by adjusting weights based on predicted outcomes.

- Deployment: The fine-tuned model is deployed within healthcare systems, integrated with inference engines to provide real-time predictions and alerts for physicians based on EHR data.

#data #ai #datacollection #theravitshow

10 Reasons You Can’t Miss HTAP Summit 2024!

Join HTAP Summit 2024, at the Computer History Museum in Mountain View, CA, on September 11th and September 12th? Check out the details here -- https://bit.ly/htapsummit

Here are 10 compelling reasons to attend:
1. Killer content 
           - Scaling Out Large Data Volumes
           - Enabling Operational Intelligence
           - Boosting AI Innovation

2. Visionary Speaker Lineup -- Check the complete lineup in the link! 

3. Real-World Technical Talks (No Fluff!)

- How six Plaid engineers moved 41 Amazon Aurora services to TiDB in one year
- How Pinterest built a next-generation graph service on top of TiDB to increase developer velocity for customers and service owners 
- Exploring the power of distributed SQL databases for modern SaaS applications through architectural considerations, best implementation practices, and real-world case studies
- Building a graph database that can serve millions of queries per second alongside deploying distributed databases in Azure and AWS 
- How to benchmark database performance with a comprehensive testing methodology, benchmark automation, and actionable telemetry data
- Optimizing user-defined functions (UDFs) with UDF outlining for significantly simpler and faster queries 
- Developing RAG applications with TiDB’s vector storing capabilities and how it can be used in conjunction with Amazon Bedrock

4. Stay on the cutting edge of Data Technology 
Each year, HTAP Summit gathers hundreds of developers, engineers, and tech leaders from over 100 leading companies across FinTech, SaaS, High Tech, Insurance, and other industries to discuss the latest in data architectures and database best practices. 

5. Network with Like-Minded Practitioners
Create new professional connections with other practitioners, share insights and best practices over a drink at happy hour, and even develop innovative ideas that you can bring back to your company. 

6. Gain Hands-On Experience at the TiDB Essentials Workshop
Join us for this free introductory-level workshop designed to swiftly acquaint participants with the fundamentals of HTAP, Distributed SQL, and TiDB while preparing them to pass the complimentary PingCAP Certified TiDB Practitioner certification exam.

7. Inclusive for All Skill Levels 
Whether you're a novice or an experienced database and application developer, you can attend sessions that introduce you to HTAP, Distributed SQL, and TiDB, as well as advanced sessions to elevate your expertise.

8. Explore the Computer History Museum
Enjoy a complimentary self-tour of the Computer History Museum during your Summit visit.

9. Free to Attend!
The event along with the training sessions is entirely free of charge, covering snacks and lunch, with complimentary on-site parking included.

10. Bring Your Colleagues and Teammates!
Feel free to invite a friend, colleague, or boss to share in this one-of-a-kind event.

#data #ai #htap #pingcap #theravitshow

What does the Bias in AI-generated news look like?

Have a deeper look into it below -- 

- Bias in AI-Generated Content: AI-generated news can introduce bias, particularly gender, and racial bias, at various levels (word, sentence, document).

- Role of Language Models: The language models (LLMs) used for generating news content need to be carefully monitored for bias.

- Prompt and Content Generation: Bias can be embedded in the AI's prompt and the content generation process, influencing the final output.

- Levels of Bias Detection: Bias should be detected and addressed at multiple levels, from individual words to entire documents.

- Importance of Scrutiny: To ensure unbiased AI-driven journalism, it is crucial to scrutinize how AI models handle news prompts and content.

- Fostering Trust: Addressing bias in AI-generated news is essential for fostering transparency, trust, and fairness in AI technologies.

Join The Ravit Show Newsletter along with 129k+ subscribers here -- www.theravitshow.com

#data #ai #llm #theravitshow

How important is Data Quality? Learn from the best enterprise leaders in the space!

I’m thrilled to announce the launch of a brand-new Data Quality series on The Ravit Show! This series has been created in response to the growing demand from our community, Register here for the event -- https://lnkd.in/dcw-bzK2

Joining me as my co-host is the incredible Mona Rakibe, Co-Founder & CEO of Telmai, who has been a passionate advocate for data quality and is at the forefront of helping enterprises achieve excellence in this space. Together, we’ll be diving deep into the challenges and opportunities that data quality presents in the age of AI.

Episode 1 – What to Expect:

We have an all-star lineup of guests, including:

- Ellen Nielsen - Chief Data Officer, Top 100 Influential People in Data, USA
- Miguel Navarro - Innovator, Patented Inventor, Seasoned Digital Leader
- Appal Badi - Head of Enterprise Data Products, First Citizen Bank, Silicon Valley (Formerly)

We’ll discuss everything from data reliability and observability to the impact of AI on foundational data needs. And of course, there will be plenty of actionable insights and thought-provoking questions to keep the conversation dynamic and unscripted.

👉 Mark your calendars and join us for the live broadcast on August 28th!

#data#ai#dataquality#theravitshow

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