Rania ZYANE

Senior Data Eng.| TechLead | Big Data | Chatbots 💗🤖 | MLOps | writer at @chatbotsmag @Medium | Travel Lover

[HHS @HHSGov]

Real food is BACK.

America is facing a full-scale health emergency because junk food became the foundation of the standard American diet. That ends now.

📲 https://t.co/vNe7oMQgrE

[Rania ZYANE @raniazy]

🍂 September 1st - La Rentrée
A new year starts over.
Forget January. For many of us, especially the ones in tech, September is the real new year.
Asking myself:
- What’s worth restarting?
- What’s worth leaving behind?
I, then sip my coffee, and build forward.

[Rania ZYANE @raniazy]

1st of September feels like new year more than new year itself.
Happy new year 🎍#September

[محمد أبوعبيد @mobeid]

مجزرة رفح قبل ساعات..
كانوا في الخيام
ينتظرون الفرج وسط الظلام
كانوا يحتضنون أطفالهم
يحاولون تصبيرهم على الجوع في المنام.
قيل لهم هذا مكان آمن، ولا أمن في غزة من هذا القصف والإجرام.
أما آن لهذا الظلم أن ينتهي!
أما آن لهذه الصرخات أن تهدأ وتنعم بالسلام!

**مجزرة رفح تأتي بعد أن أمرت محكمة العدل الدولية إسرائيل بوقف الهجوم على رفح، فهكذا ترد إسرائيل على كل القوانين والمطالب الدولية.
#مجزره_رفح

[Jackson Hinkle 🇺🇸 @jacksonhinkle]

🚨🇮🇱 ISRAEL IS A TERRORIST STATE

[Rania ZYANE @raniazy]

Zwin 💎

[DeeKay @deekaymotion]

Verified! ☑️

[Min Choi @minchoi]

OpenAI broke the Internet with mind blowing GPT-4o demos yesterday.

And people are already doing wild use cases with it.

10 examples:

[Rania ZYANE @raniazy]

He knows
@ChatGPTapp

[Rania ZYANE @raniazy]

Pov : your work is on @globe_engineer 🫶🤖

[Rania ZYANE @raniazy]

@elazzabi_ You literally can build a new skill : graphic design, sketching, Crochetting, solve Rubik’s cube .. or maybe just learn some Time Management techniques in order to plan your 13-hour return flight 😅

[Rania ZYANE @raniazy]

@X The future

[Rania ZYANE @raniazy]

@nahylothmane Totally !

[Rania ZYANE @raniazy]

"The courage to be disliked"

[Rania ZYANE @raniazy]

We all encounter tough moments in life's ever-twisting journey. It is important to take a step back and grant ourselves a moment of compassion and understanding. Here are the actions recommended by @OpenAI paired with my personal choices.
https://t.co/jOjh2Koi3M

[Rania ZYANE @raniazy]

Dolce far niente.

[Guillermo Rauch @rauchg]

RIP Bram Moolenaar – 1961-2023
https://t.co/2R95NMLF8B

[Eden Marco @EdenEmarco177]

Generative AI application development AS A BURGER (list is not closed)

Inspired by @milan_milanovic Devops burger🙂

What did I forget?😎😎😎😎

[Rania ZYANE @raniazy]

@Aurimas_Gr We need : Spark Executor Cores 🤒

[Aurimas Griciūnas @Aurimas_Gr]

What is the 𝗦𝗤𝗟 𝗤𝘂𝗲𝗿𝘆 𝗼𝗿𝗱𝗲𝗿 𝗼𝗳 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻?

There are many steps involved in optimising your SQL Queries. It is helpful to understand the order of SQL Query Execution as we might have constructed a different picture mentally.

The actual order is as follows:

𝟭. 𝗙𝗥𝗢𝗠 𝗮𝗻𝗱 𝗝𝗢𝗜𝗡: determine the base data of interest.

𝟮. 𝗪𝗛𝗘𝗥𝗘: filter base data of interest to retain only the data that meets the where clause.

𝟯. 𝗚𝗥𝗢𝗨𝗣 𝗕𝗬: group the filtered data by a specific column or multiple columns. Groups are used to calculate aggregates for selected columns.

𝟰. 𝗛𝗔𝗩𝗜𝗡𝗚: filter data again by defining constraints on the columns that we grouped by.

𝟱. 𝗦𝗘𝗟𝗘𝗖𝗧: select a subset of columns from the filtered grouped result. This is also where 𝗪𝗜𝗡𝗗𝗢𝗪 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻𝘀 happen.

𝟲. 𝗢𝗥𝗗𝗘𝗥 𝗕𝗬: order the result by one or multiple columns.

𝟳. 𝗟𝗜𝗠𝗜𝗧: only retain the top n rows from the ordered result.

Below is a nice way to visualise it.

What are your thoughts? Let me know in the comment section 👇

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Follow me to upskill in #MLOps, #MachineLearning, #DataEngineering, #DataScience and overall #Data space.

Also hit 🔔to stay notified about new content.
𝗗𝗼𝗻’𝘁 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗼 𝗹𝗶𝗸𝗲 💙, 𝘀𝗵𝗮𝗿𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝗲𝗻𝘁!

Join a growing community of Data Professionals by subscribing to my 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://t.co/qgNCnGtF4A

[Aurimas Griciūnas @Aurimas_Gr]

What are the four 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗧𝘆𝗽𝗲𝘀?

Even if you will not work with them day to day,  the following are the four ways to deploy a ML Model you should know and understand as a MLOps/ML Engineer.

➡️ 𝗕𝗮𝘁𝗰𝗵: 

👉 You apply your trained models as a part of ETL/ELT Process on a given schedule.
👉 You load the required Features from a batch storage, apply inference and save the results to a batch storage.
👉 It is sometimes falsely thought that you can’t use this method for Real Time Predictions.
👉 Inference results can be loaded into a real time storage and used for real time applications.

➡️ 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗶𝗻 𝗮 𝗦𝘁𝗿𝗲𝗮𝗺 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: 

👉 You apply your trained models as a part of Stream Processing Pipeline.
👉 While Data is continuously piped through your Streaming Data Pipelines, an application with a loaded model continuously applies inference on the data and returns it to the system - most likely another Streaming Storage.
👉 This deployment type is likely to involve a real time Feature Store Serving API to retrieve additional Static Features for inference purposes.
👉 Predictions can be consumed by multiple applications subscribing to the Inference Stream.

➡️ 𝗥𝗲𝗮𝗹 𝗧𝗶𝗺𝗲:

👉 You expose your model as a Backend Service (REST or gRPC).
👉 This ML Service retrieves Features needed for inference from a Real Time Feature Store Serving API.
👉 Inference can be requested by any application in real time as long as it is able to form a correct request that conforms API Contract.

➡️ 𝗘𝗱𝗴𝗲: 

👉 You embed your trained model directly into the application that runs on a user device.
👉 This method provides the lowest latency and improves privacy.
👉 Data in most cases is generated and lives inside of device significantly improving the security.

What types of deployments are you mostly working on? Let me know in the comments!  👇

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Follow me to upskill in #MLOps, #MachineLearning, #DataEngineering, #DataScience and overall #Data space.

Also hit 🔔to stay notified about new content.
𝗗𝗼𝗻’𝘁 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗼 𝗹𝗶𝗸𝗲 💙, 𝘀𝗵𝗮𝗿𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝗲𝗻𝘁!

Join a growing community of Data Professionals by subscribing to my 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://t.co/qgNCnGtF4A