DataSpoof

🔥 BREAKING: OpenAI Launches Operator: The Future of AI Automation

OpenAI has introduced Operator, an AI agent that can complete tasks on its own using a web browser. It’s designed to make work easier by handling tasks for you.

Operator is powered by the new Computer-Using Agent (CUA) model. It combines GPT-4o's vision with advanced reasoning, allowing it to see, click, type, and interact with websites just like a person. No special integrations are needed.

How to make real time stock market data processing pipeline using AWS Lambda and kinesis

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List of 500+AI Agent projects/UseCases

https://github.com/DataSpoof/500-AI-Agents-Projects

AI Agents are about to change everything—and it’s happening now.

Here’s the cheat sheet:
1️⃣ Agentic RAG Routers: Think of them as traffic controllers for your workflows.
2️⃣ Query Planning RAG: Perfect for making tasks super efficient.
3️⃣ Adaptive RAG: Always learning, always improving.
4️⃣ Corrective RAG: Spotting and fixing errors before they derail you.
5️⃣ Self-Reflective RAG: Basically, AI journaling to improve itself.
6️⃣ Speculative RAG: Solving problems before you even know they exist.
7️⃣ Self Route RAG: Dynamic workflow magic.

Everyone knows about LLM aka Large Language model.

Now we will talk about SLM aka Small Language model

As their name implies, SLMs are smaller in scale and scope than large language models.

Some examples of SLM are
- Phi 3.5
- tiny Llama
- mobile Llama
- Gemma2

SLMs can be trained using two main techniques:

Knowledge distillation: A smaller model learns from a larger, already-trained model

Pruning: Extra bits that aren't needed are removed to make the model faster and leaner

Here are some characteristics of SLMs:

Smaller in size: SLMs have fewer parameters than LLMs, often in the tens to hundreds of millions, compared to billions in LLMs.

More efficient: SLMs are more computationally efficient and can run on less powerful hardware.

Faster training: SLMs can be trained and developed faster than LLMs.

Specialized: SLMs are trained on curated data sources and can be specialized in specific tasks.

Fine-tunable: SLMs can be fine-tuned to do exactly what is needed for a specific task.

Cost-effective: SLMs can be more cost-effective than LLMs, making them a good option for integrating intelligent features when resources are limited.

321 real-world gen AI use cases from the world's leading organizations

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DataSpoof pinned «𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐈𝐈 Interview Experience at PayPal. I wanted to share my experience interviewing for the 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐈𝐈 position at PayPal. Here's a breakdown of the process: 𝐎𝐧𝐥𝐢𝐧𝐞 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 (𝐎𝐀): The first step was an online assessment sent by the recruiter.…»

𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐈𝐈 Interview Experience at PayPal.

I wanted to share my experience interviewing for the 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐈𝐈 position at PayPal.

Here's a breakdown of the process:

𝐎𝐧𝐥𝐢𝐧𝐞 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 (𝐎𝐀):
The first step was an online assessment sent by the recruiter. Clearing this assessment led to two technical rounds being scheduled, separated by a gap of five days.

𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐑𝐨𝐮𝐧𝐝 𝟏:
This round was with a Data Engineer III and focused on problem-solving and SQL.

𝐀). 𝐃𝐒𝐀 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬:
1. 𝑇ℎ𝑒 𝑅𝑎𝑖𝑛𝑤𝑎𝑡𝑒𝑟 𝑇𝑟𝑎𝑝 𝑃𝑟𝑜𝑏𝑙𝑒𝑚.
2. 𝐴 𝑃𝑟𝑖𝑜𝑟𝑖𝑡𝑦 𝑄𝑢𝑒𝑢𝑒 𝑃𝑟𝑜𝑏𝑙𝑒𝑚 (I don't recall the exact details but was similar to those dealing with task prioritization).

𝐁). 𝐒𝐐𝐋 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬:
Focused on window functions, their usage, and optimization strategies.

𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐑𝐨𝐮𝐧𝐝 𝟐 (𝐃𝐞𝐬𝐢𝐠𝐧 𝐑𝐨𝐮𝐧𝐝):
This was done with a Staff Data Engineer and had three main parts:

A). 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐃𝐢𝐬𝐜𝐮𝐬𝐬𝐢𝐨𝐧:
Shared details about my past projects. Also discussed best practices for software and data engineering, including how I implemented these in my projects.

B). 𝐃𝐞𝐬𝐢𝐠𝐧 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧:
The scenario involved multiple data sources such as Hadoop, S3, and Oracle DB. I was tasked with designing a solution to migrate data to a final S3 bucket.
Explained my choices for services and tools, including error logging, scalability, and fault tolerance.

C). 𝐒𝐩𝐚𝐫𝐤 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞:
Given two data frames, I had to perform some processing and store the final output in another data frame.

𝐌𝐚𝐧𝐚𝐠𝐞𝐫𝐢𝐚𝐥 𝐑𝐨𝐮𝐧𝐝 (𝐑𝐨𝐮𝐧𝐝 𝟑):
This was with the Senior Engineering Manager, who was also the hiring manager for this role.

𝐓𝐨𝐩𝐢𝐜𝐬 𝐃𝐢𝐬𝐜𝐮𝐬𝐬𝐞𝐝:
A). 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 : A deep dive into my projects, focusing on why specific tools and services were chosen.
B). 𝐑𝐞𝐚𝐥 𝐋𝐢𝐟𝐞 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 :
How I would handle pipeline issues, like overload situations or service downtimes.
Behavioral Questions: Highlighted my problem-solving, teamwork, and adaptability skills.

𝐇𝐑 𝐑𝐨𝐮𝐧𝐝 (𝐑𝐨𝐮𝐧𝐝 𝟒):
The final round was with HR. We discussed the offer details PayPal was providing, covered some standard behavioral questions related to company culture and expectations.

Credit- Shubham shukla

Day 4 is available in our YouTube channel.

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Top 10 GitHub Repositories to Ace Your Next Analytics Interview

These repositories offer an extensive u range of resources, tutorials, and projects to help you excel in data science and analytics interviews:

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DataSpoof pinned «𝗙𝗔𝗔𝗡𝗚 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: How does an ARIMA model work? The most common question if you have a forecasting projects in your resume, or the role requires forecasting experience. To explain this, let's start by breaking down ARIMA, and I mean…»

𝗙𝗔𝗔𝗡𝗚 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻:
How does an ARIMA model work?

The most common question if you have a forecasting projects in your resume, or the role requires forecasting experience.

To explain this, let's start by breaking down ARIMA, and I mean literally -

AR - Auto-regressive component of model.
This assumes the future value depends LINEARLY on past values.

Typically, you use ACF/PACF plot to figure out how many of the past value (or 'p' value of ARIMA).

I - Integrated component of model.
It represents how to difference the values from themselves to make sure mean and variance is constant over time. Typically, you use a statistical test like ADF to figure out how much differencing you need (also called the 'd' value in ARIMA)

MA - Moving Average component of model.
This assumes future values depends LINEARLY on errors in forecasting made in prior time steps. Typically, you use ACF/PACF plot to determine past value (or 'q' values in ARIMA).

Note: You can also use packages like auto_arima in pmdarima in Python to do a grid search over a range of p,d,q parameter to fit your ARIMA model.

ARIMA essentially works by summing the differenced prior values and forecast errors. The reason why this simple formulation is ubiquitous, is because of its effectiveness and adaptability.

✅ It's able to account for stationary and non-stationary time-series.

✅ It can represent future values in terms of the few of the lagged previous values and forecast errors, making it interpretable and less likely to overfit.

✅ It can accommodate seasonality with its seasonal variation SARIMA, and exogenous variable i.e. features that might help predict future values of the time series apart from historical values of the same time series.

Credit- Karun

Follow Abhishek Kumar Singh to learn Python programming, data Science and big data.

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30 days of Python

Day 1- Python is uploaded on our YouTube channel

Do subscribe and like our Videos for daily Python content

https://youtu.be/VBk59upcp94?si=AOLD0Uj7H5K3KHHr