Senior Manager - Data Science and Engineering at Apple

I lead the data science and engineering team at Apple Finance - Services and Infra. The team focuses on translating business problems and requirements into data products using advanced statistical and machine learning models to support and help Apple leadership make data-driven decisions.

I am also an open-source contributor and the author of Hands-On Time Series Analysis with R (Packt publication) and several R packages for time series analysis and machine learning applications. Currently working on my next book, Applied Time Series Analysis and Forecasting, which focuses on forecasting at scale. 

Also available on:
Github: https://github.com/RamiKrispin
Medium: https://medium.com/@rami.krispin
Mastodon: https://mstdn.social/@ramikrispin

Rami Krispin

Imagine going to a restaurant and starting with dessert—then ordering the main course.

That’s roughly how university degrees are structured today, in my opinion: a heavy focus on theory, with far less emphasis on the practical tools students actually need in the job market.

Personally, I believe the very first course in any STEM track should be tooling—Git, the command line, shell, Bash, and related fundamentals. Having this foundation early would allow students to gain real, hands-on experience throughout their degree, not just after graduation.

A few years ago, three MIT PhD students recognized this gap and created a course called “The Missing Semester.” The course focuses on essential practical skills like the CLI, Git, Vim, and shell tools. It first launched in 2019, ran for two years, and last week the original instructors returned to MIT to teach an updated version.

The full course is available on YouTube:
https://lnkd.in/gh4KRiD8

Highly recommended—especially if you’re at the beginning of your journey.

#datascience #ai #mlops

Senior Manager - Data Science and Engineering at Apple | Docker Captain | LinkedIn Learning Instructor

Hi Bay Area data folks! 👋

Bay Area Data & AI is hosting an in-person, casual networking event focused on data, AI, careers, and products.

I’m excited to co-host this event with Manisha Arora, Devshree Bharatia, Vishakha Sadhwani,  Venkata Naga Sai Kumar Bysani, and  Sriram Arumelli.

📅 When: Jan 24 (Sat) at 10:00 AM PST
 📍 Where: Sunnyvale Baylands Park

👉 More details & RSVP: https://luma.com/kgsx6o61

Hope to see you there!

#ai #data #datascience

How to make your Python Docker container slimmer 🐳👇🏼
 
If you’ve ever deployed Python containers only to watch them balloon to several GBs, you’re not alone — I once hit a 5 GB Python image (thank you, PyTorch dependencies 😅). But with a few simple optimizations, you can dramatically reduce image sizes, speed up deployments, and lower cloud costs.

Here are practical tips that have worked well:
1️⃣ Choose a Lean Base Image
Start smaller:
 ✔️ python:3.x-slim instead of the full Python image
 ✔️ python:3.x-alpine if compatible with your dependencies (and you are a brave developer 😆) 
 Smaller base = smaller final image and faster pulls. 
2️⃣ Use Multi-Stage Builds (Big Win!)
Multi-stage builds let you install tools and build dependencies in one stage, then only copy what’s needed into a clean final image. This can reduce the size by 60-90% compared to single-stage builds. 
3️⃣ Minimize Layers
Every RUN / COPY / ADD adds a layer.
 ➡️ Combine related commands with && to reduce layers and eliminate intermediate files (although you won't gain much size reduction).
 Example:

RUN apt-get update && \
    apt-get install -y build-deps && \
    pip install -r requirements.txt && \
    apt-get purge -y build-deps && \
    rm -rf /var/lib/apt/lists/*

Fewer layers = smaller image. 

4️⃣ Use .dockerignore to Cut Build Context
Exclude unnecessary files (.git, logs, IDE folders, virtual environments) so Docker doesn’t send them into the build context — this keeps images lean and speeds up builds. 
5️⃣ Install Only What You Need
Separate dev dependencies from production deps.
 Use flags like --no-dev or careful requirements files so you install only what your app actually needs at runtime. 

💡 Why it matters: smaller images → faster CI/CD → less storage & network cost → reduced attack surface → happier developers.

If you want to learn more, please check my tutorial about multi-stage build:

https://lnkd.in/gHTpdt2S

#mlops #docker #devops #python

Open article: Introduction to Multi-Stage Image Build for Python by medium.com

Introduction to Multi-Stage Image Build for Python

Introduction to JAX and Flax NNX - the framework behind Google Gemini 🚀👇🏼

JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. Flax NNX is a neural network library and ecosystem built on top of JAX.

The following tutorial from Google for Developers provides an introduction to the JAX AI stack, and it covers topics such as:
✅ JAX’s functional transformations
✅ Debugging techniques
✅ Scaling models across distributed hardware
✅ Optax for optimization and checkpoints

📽️: https://lnkd.in/g8kfrFmg

#ai #llm #machinelearning

My weekly newsletter is out! 

This week’s agenda:
🔹 Open Source of the Week - Pandas 3 release
🔹 New learning resources - Introduction to JAX and Flax NNX, OpenCode crash course, introduction to FastAPI
🔹 Book of the week - Introduction to Machine Learning Systems by Prof. Vijay Janapa Reddi

📌 Subscribe to receive weekly updates: https://lnkd.in/gb3P8YdE
🎯 Learn how to build an SQL AI agent: https://lnkd.in/g4_W8na8

#python #datascience #machinelearning #ai

This week's agenda: Open Source of the Week - Pandas 3 release New learning resources - Introduction to JAX and Flax NNX, OpenCode crash course, intro

Open article: Pandas 3 is Out, Introduction to Machine Learning Systems, New Tutorials | Issue 72 by Rami Krispin • 3 min read

Pandas 3 is Out, Introduction to Machine Learning Systems, New Tutorials | Issue 72

My weekly newsletter is out! 

This week’s agenda:
🔹 Open Source of the Week - The SQLGlot project
🔹 New learning resources - Setting Memory for AI application, MIT deep learning course, Statistical Rethinking 2026, introduction to AI engineering, LlamaIndex crash course
🔹 Book of the week - Statistical Analysis with Python For Dummies by Joseph Schmuller

📌 Subscribe to receive weekly updates: https://lnkd.in/gb3P8YdE
🎯 Learn how to build an SQL AI agent: https://lnkd.in/g4_W8na8

#ai #python #sql

This week's agenda: Open Source of the Week - The SQLGlot project New learning resources - Setting Memory for AI application, MIT deep learning course

Open article: Statistical Analysis with Python, SQLGlot & MIT Deep Learning | Issue 71 by Rami Krispin • 5 min read

Statistical Analysis with Python, SQLGlot & MIT Deep Learning | Issue 71

Many of the AI concepts are simpler than they seem. Adding to an AI application's "memory" is one of those things. 

By default, LLMs are stateless, meaning each conversion is independent from the previous ones. Here is how you turn your AI application with less than five lines of code:
1️⃣ Set an empty list to store the previous conversions
2️⃣ Add to the prompt template this list as an argument
3️⃣ After every response of the LLM update the list with the user question and LLM response

This is, of course, not a production-ready method. There are a few limitations to this method, such as an increase in the number of tokens used and a potential drop in the context as the conversion takes a different direction.

The slides below, generated with NotebookLM, summarise this process and are based on a tutorial I created this week. The next tutorial will focus on efficient management of the token size.

This tutorial and others are available in the AIOps Newsletter: https://lnkd.in/gghdpeEF

#ai #llm #datascience

Building Memory to LLM Applications

I share an 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞 project every week in my 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫. This week’s focus is on the SQLGlot project 👇🏼

SQLGlot is a versatile library for reading, manipulating, formatting, and translating SQL across many dialects. It ingests SQL text, generates an abstract syntax tree (AST), and produces syntactically correct SQL for a target dialect such as DuckDB, Spark, Snowflake, BigQuery, Postgres, MySQL, and many others. It also includes features for query analysis, syntax error detection, and basic execution.

Why It’s Useful 🎯
✅ Dialect Translation: Simplifies migrating SQL across engines by automatically transpiling queries.
✅ Parser & Formatter: Can parse diverse SQL inputs, ensure correct syntax, and produce clean, standardized output.
✅ AST-Level Manipulation: Lets developers programmatically inspect and modify query structures, enabling tooling such as linters, analyzers, and automated refactors.
✅ Optimizer & Analyzer: Includes basic optimization rules and expression tree traversal utilities.
✅ Execution Engine: Although not designed for performance, SQLGlot can execute queries against Python data structures, useful for testing or in-memory analytics.

Key Features 🛠️ 
🔹Transpile between 30+ SQL dialects (including Postgres, Spark, Hive, Snowflake, BigQuery, SQLite, TSQL, and more).
🔹Detailed parser errors & warnings with context.
🔹Extensible dialect system, so you can plug in support for custom SQL syntaxes.
🔹 AST diffing, helping compare and transform queries semantically.
Benchmarks show performance that’s competitive with other Python SQL parsers.

More details are available on the project repo: https://lnkd.in/gb9UxetB

📌 Join 34k subscribers to receive weekly updates: https://lnkd.in/gb3P8YdE

VScode Dev Containers Extension 🐳👇🏼 

One of the main reasons I started to use VScode was the Dev Containers extension. This extension enables the seamless launch and development of a project inside a container.

Here are the main advantages of this extension:
✅ It is highly customizable and flexible provide you with complete control over your project settings. 
✅ Issolation - From the environment setting to the fonts and theme, it enables you to isolate your project VScode settings from your regular settings
✅ Reproducability - beyond your container, it enables you to reproduce your complete settings from fonts to extensions. This is great when you are working on multiple machines and cross-collaborating with others 
✅ Consistent environments — All developers use the exact same OS, tools, runtimes, and libraries. No more “works on my machine” problems. 
 ✅ Faster onboarding — New team members can be productive in minutes without manual setup. 
 ✅ Infrastructure as code — Dev container configs live with your project and can be version-controlled. 

Pro tip 🎯
Set GitHub templates for different use cases, for example, I have the following templates:
Python: https://lnkd.in/gSTcBbcK
R: https://lnkd.in/gKzaBC7T

If you want to get started with the Dev Containers extension, please check the following tutorial:
https://lnkd.in/gtCtrBqM 

#docker #mlops #datascience

Open article: Getting Started with the Dev Containers Extension by medium.com

Getting Started with the Dev Containers Extension

Axtria - Ingenious Insights

Associate

- Developed and enabled the validation dashboard which can detect anomalies preventing significant data loss
and has reduced the efforts of manual validation of data by 95% using Python and Tableau
- Enabled high Data Quality data checks and fixed the Data Quality issues while enabling them which reduced
the data issue by 90%
- Optimized the data pipelines and enabled them to refresh three times a day which increased the conformity
of data by 95% using SQL.

Analyst

- Developed and have ownership for the automation scripts using python to pull data from API based source, standardization of files, file movement through SFTP to S3 or within S3, and various other scripts
- Responsible of high-quality data pipelines using SQL and Matillion
- Worked with various pharma data like Veeva, Specialty Pharmacy, IQVIA LAAD, and many other to meet and solve the business requirements raised by the client
- Responsible for creating dynamic dashboards for clients using Tableau

Analyst Trainee

- Responsible orchestration process by using NodeRed and Matillion to process data into reporting tables from staging to provisioning layer
- Developed Various STTMs and performed unit testing over data from staging to provisioning layer using Snowflake
- Worked on defect fixing with respect to data Snowflake

Intern

JUSPAY

- Automated various tasks for Merchants' Emails, Data Interpretation, and Data Visualization using R and Python
- Responsible for the internal dashboard development team using the R shiny framework
- Improved payment success rates which were reduced due to authorization errors using R and Big Query

Technical Product Analyst 

Google Summer of Code

- Developed and Managed the bugRzilla package which helps the user to interact with Bugzilla through an API
- Worked on writing the unit test cases using testthat, raising the code coverage on a package, and used VCR to test API calls and use snapshots to validate the output
Package available here:
https://github.com/llrs/bugRzilla/ 

- Responsible for creating a report on R bugs submitted on Bugzilla which shows my understanding of the R bug tracking system and initiative. As well as my work experiences combining multiple
packages

The report is available here: 
https://llrs.github.io/bugzilla_viz/bugRzilla_review.html

Student Developer

Google Developer Student Clubs- Galgotias University

- Lead a community of 100+ students 
- Mentoring 500+ students
- Share knowledge on Google Technologies
- Organizes workshops, seminars, etc
- Speaker

Community Lead

Core Team Member

Member

Analytics Vidhya

Contributing Writer

OpenMined

An open-source community focused on researching, developing, and elevating tools for secure, privacy, value-aligned artificial intelligence.

Mentor

Technical Mentor

The Sparks Foundation

TechnoJam

Data Science Lead

Widhya

During this internship, I was successfully able to predict and analyze the data.

Data Analytics Intern

Google Code-in

Student Mentor

Dynamic and achievement-oriented Business & Data professional with over two years of hands-on experience in developing and optimizing data pipelines, as well as implementing Master Data Management systems. Proficient in a comprehensive suite of technical tools including Python, R, Databricks, SQL, Redshift, PostgreSQL, R Shiny, ETL, and Git. Skilled in leveraging Python, R, and Tableau for data extraction, manipulation, and visualization to drive informed decision-making. Adept at conducting thorough data analysis, generating insightful reports, and fostering data-driven strategies to enhance business performance. Demonstrates exceptional organizational acumen, meticulous attention to detail, and a nuanced comprehension of data's multifaceted utility. Committed to perpetual learning, fueled by innate curiosity, and dedicated to continual professional advancement and excellence within the realm of Analytics.

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