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SAI Notes #05: Building efficient Experimentation Environments for ML Projects.
Let's look into what it takes for an Experimentation Environment to be efficient in Machine Learning Projects.
Jun 4
•
Aurimas Griciūnas
8
May 2023
SAI Notes #04: CI/CD for Machine Learning.
Let's look into how CI/CD is different for ML projects compared to regular software.
May 28
•
Aurimas Griciūnas
21
3
SAI Notes #03: Apache Flink - Architecture.
Let's take a closer look into internals of Apache Flink.
May 20
•
Aurimas Griciūnas
9
3
SAI Notes #02: Encoding in Parquet.
Let's look into the types of Encoding in Parquet Files.
May 14
•
Aurimas Griciūnas
10
SAI Notes #01: Watermarks in Stream Processing, SQL Query order of Execution.
Watermarks in Stream Processing, SQL Execution order.
May 7
•
Aurimas Griciūnas
13
April 2023
SAI #28: Organisational structure for effective MLOps.
Let's look into how we should structure our organisations for effective MLOps practice implementation.
Apr 29
•
Aurimas Griciūnas
18
2
SAI #27: Event Latency in Data Systems
A look into how event latency builds up as data travels through the Data System
Apr 22
•
Aurimas Griciūnas
13
SAI #26: Partitioning and Bucketing in Spark (Part 1)
Let's look into the difference between Partitioning and Bucketing in Spark.
Apr 15
•
Aurimas Griciūnas
19
SAI #25: Twitter's Recommender System (The Algorithm)
Review of recently open-sourced Twitter's recommender system.
Apr 8
•
Aurimas Griciūnas
9
SAI #24: Feedback Loops in Machine Learning System.
I cover 3 main areas of feedback required in effective Machine Learning System.
Apr 1
•
Aurimas Griciūnas
12
March 2023
SAI #23: Deconstructing a Feature Store.
This week we look into ins and outs of a Feature Store.
Mar 26
•
Aurimas Griciūnas
8
2
SAI #22: Decomposing the Data System.
This week we look into how to decompose the Data System for better reasoning.
Mar 18
•
Aurimas Griciūnas
22
9
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