Developer Voices

How Do You Get AI Into Production?

May 16, 2023
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Chapters
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Episode notes
1
Introduction
00:00 • 2min
2
Scaling Machine Learning With Apache Spark
01:57 • 2min
3
How to Build a Machine Learning Pipeline Workflow
03:35 • 2min
4
Apache Spark: The Backbone of Distributed Computing
06:04 • 4min
5
Spark and Hadoop: How Spark Connects to Hadoop
10:07 • 2min
6
The Future of Storage in Hadoop
11:40 • 2min
7
Spark and Kafka: A Pipeline for IoT
13:26 • 2min
8
The Future of Autonomous Driving
15:01 • 2min
9
How to Extract Machine Learning Models From Data
16:38 • 2min
10
The Importance of Feature Engineering in Machine Learning
18:36 • 2min
11
How to Scale a Machine Learning Pipeline
20:50 • 2min
12
Spark ML and Machine Learning
23:14 • 2min
13
Spark ML and TensorFlow
25:03 • 2min
14
Spark and TensorFlow: The Future of Machine Learning
26:38 • 3min
15
Spark: The Universal Architecture to Grow From
29:52 • 2min
16
How to Deploy a Machine Learning Model With Spark
32:10 • 3min
17
How to Monitor Production and Deploy ML Models
34:58 • 3min
18
How to Automate a Recommendation Engine
37:59 • 2min
19
The Importance of Monitoring in Machine Learning
39:41 • 2min
20
The Future of Machine Learning
41:32 • 2min
21
Spark: A Good Place to Start With Machine Learning
43:10 • 2min
22
How to Take Spark Out Into Production Without Massive Code Changes
45:22 • 2min