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

TechnipFMC

• Created automated visual data flows from scratch for Vice President of OneFleet (All Aberdeen managed vessels) and other key management personnel to gain insights on varied personnel data using R and Python based data pipelines.
• Implemented an employee attrition early detection model that led to the successful interventions resulting in retention of key personnel. 
• Developed a  cluster analysis of the employee profiles to streamline reward management.
• Cleaned and curated in-house datasets to be ingested to databases and used to generate actionable insights for top management. 
• Pioneered the creation of a happiness index based on psychometric profiles to measure wellbeing of Offshore employees thereby making employee welfare programmes 80% data driven compared to the erstwhile non data driven approaches  . 
• Converted the excel based , manual tasks through SharePoint ,MS Power Automate and PowerBi based solutions reducing the need for human intervention resulting in significant reduction of cost and employee effort. 
• Training staff on Data Driven best practices contributing to the in house data community keep abreast with latest developments in people analytics arena.

• Featured in the company Lean Project Competition under the Data Analytics category. Our team's entry resulted in the cost saving of over a million pounds fleet operation costs. 
• Featured in the Fleet News( In-house magazine ) for exceptional academic achievement.

Data Scientist - People Analytics

Freelance (Self employed)

• Created data analytics pipelines and customer analytics Dashboards focusing on prediction of customer segmentation and analysis of customer engagement and profitability for finance companies.
• Data anonymization projects for  mid sized healthcare companies in the UK and SriLanka. 
• Developed a google cloud based data pipeline and  automated dashboards for self service insights for optimizing a healthcare practices based in the UK. 
• Developed a data analytics projects  for  Industrial companies in the UK focusing on operational analytics, performance analysis and employee health and safety dashboards. 
• Delivered multiple training programmes to corporates and government officials on leveraging visual analytics for better Data Driven Decision making within organisation's .

Data Scientist

Nations Trust Bank PLC

• Visualized and analysed Consumer Loan and SME Portfolios to give actionable insights automating the same utilizing Tableau Server based solutions.
• Automated the modeling of customer repayment patterns resulting in proactive flagging of potential defaulters thereby reducing the NPA volume.
•Employed spatial analytics to deliver insights about customer behavior driving market penetration in underserved areas in SriLanka resulting in exponential business growth.
• Eliminated manual work in operational departments by integrating MS Power Automate with Tableau.
•Imparted training to junior analysts on various Bi Tools, techniques, and concepts.

Data Science Manager

•	Successfully completed the Nations Management Trainee Programme, gaining experience by working within many key departments (Branches, Operations, Trade, Lean Transformation) gaining a broad understanding of the bank and its functions, whilst also advising on cases where data analytics/Python-based automation could help. Whilst on rotation I provided advice on use cases where data analytics and python-based automation could help in various departments.

Management  Trainee

A brief Description of the my role in this posting  

•	Management information (MI) Reporting, Monitoring Performance of projects, Marketing promotions on a Regular basis 
•	Inactive Activation, Early engagement (customer life cycle management) data management and related Management information (MI) Reporting and related analytics 
•	Consumer Finance, Retail & SME Related Management Information (MI) Reporting at product, Segment and Business levels 
•	Database management – Consumer Finance, Retail & SME. Selection of Bases for Cross selling opportunities and Related Analytics. 
•	Gathering  intelligence on competitor performance, strategies and growth

Senior MIS assistant ( Consumer Banking )

Account Reconciliation Assistant

Financial Trainee / Payments facilitator

I am a Data Scientist / Analytics Engineer with an FCCA Membership with more than eight years of hands-on expertise in Data Science and Business Intelligence in  banking , healthcare and human resources  domains. I have a strong background in Tableau and Python-based Machine Learning solutions. I am also comfortable in R programming based implementations for statistical and machine learning use cases as well. I can operate efficiently in an agile start-up scenario and a process-driven enterprise environment. I am a passionate self-learner who leverages emerging technologies. I am also capable of strategically incorporating team member feedback for producing the highest quality data products. My expertise in the data domain are building  financial analytics, spatial analytics and people analytics solutions.

In my spare time I am a CELTA qualified English teacher who is passionate about teaching English to non-native speakers and thus far have coached many students to become successful in their chosen English language exams like IELTS, PTE, Cambridge C1 and Cambridge C2 to name a few. 

As hobbies I read Science fiction and psychology books. I also enjoy reading the latest behavioural economics publications as well.

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Chathura M Perera FCCA

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Chathura M Perera FCCA

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