Lynn discusses recent innovations in ML and relates these new libraries, TensorFlow and MXNet to Hadoop and Spark.
- [Instructor] In previous movies, we looked at running…Spark ML, which is something I run into…with my customers frequently.…In addition to that, there have been…quite a few advancements in…machine learning algorithms recently.…You might be wondering how these work on Spark.…Now, if you're not doing machine learning,…this is pretty deep stuff,…but for some of you, you will be doing machine learning…so I wanted to include it here.…So, around deep learning, there is advancements…around advanced neural networks,…and these manifest as open-source libraries.…There are many different configurations,…but these are the ones that I work with most frequently…or have customers want me to work with them on.…
One is TensorFlow, and it's created from Google…and open sourced, now has an open-source community.…And the other one is MXNet.…This is from Amazon Open Source community.…As we saw, even with the Spark ML in the last example,…you really are going to want to leverage more powerful machines…underlying Amazon EC2 instances.…
Author
Lynn Langit
Released
7/5/2017- Relate which file system is typically used with Hadoop.
- Explain the differences between Apache and commercial Hadoop distributions.
- Cite how to set up IDE - VS Code + Python extension.
- Relate the value of Databricks community edition.
- Compare YARN vs. Standalone.
- Review various streaming options.
- Recall how to select your programming language.
- Describe the Databricks environment.
Skill Level Intermediate
Duration
Views
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Introduction
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Welcome53s
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1. Hadoop Core Fundamentals
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Modern Hadoop1m 53s
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Hadoop libraries1m 23s
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Run Hadoop job on GCP1m 52s
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Databricks on AWS2m 32s
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2. Setting Up a Hadoop Dev Environment
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Load data into tables1m 51s
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3. Hadoop Batch Processing
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Processing options1m 2s
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Resource coordinators1m 30s
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Compare YARN vs. Standalone1m 30s
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4. Fast Hadoop Options
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Big data streaming1m 57s
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Streaming options1m 10s
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Apache Spark basics1m 46s
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Spark use cases1m 2s
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5. Spark Basics
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Apache Spark libraries3m 24s
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Spark shell1m 53s
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6. Using Spark
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Tour the notebook5m 29s
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Import and export notebooks2m 56s
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Calculate pi on Spark8m 19s
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Import data2m 50s
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Transformations and actions4m 43s
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Caching and the DAG6m 49s
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7. Spark Libraries
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Spark SQL8m 34s
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SparkR6m 11s
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Spark ML: Preparing data4m 21s
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Spark ML: Building the model3m 50s
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MXNet or TensorFlow2m 30s
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Spark with GraphX2m 12s
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8. Spark Streaming
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Spark streaming4m 21s
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9. Hadoop Streaming
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Pub/Sub on GCP3m 59s
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Apache Kafka1m 26s
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Kafka architecture1m 6s
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Apache Storm1m 30s
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Storm architecture1m 36s
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10. Modern Hadoop Architectures
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Conclusion
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Next steps26s
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Video: Advanced machine learning on Spark