The document describes a community event called Study Jams that provides training on various Google Cloud technologies including BigQuery, Natural Language API, Speech API, and Cloud ML Engine. The event includes several hands-on labs that teach skills like introductory SQL for BigQuery, using the BigQuery and Cloud SQL consoles, and the Cloud Natural Language API and Cloud Speech API. Attendees will learn how to get started with these Google Cloud services and tools.

1. Cloud Study Jams
Shiv Prakash
@shivp0616

2. A Community Event
Study Jams are community-run study groups
for developers. This is a great opportunity to
bring members of community together to learn
something in person.
These trainings will give community members
a specific skill they can use for personal
development or career advancement.
What is a Study Jam?

3. • BigQuery
• Lab-1 : Introduction to SQL for BigQuery
and Cloud SQL
• Lab-2 : BigQuery: Qwik Start – Console
• Natural Language API/Speech API
• Lab-3 : Cloud Natural Language API: Qwik
Start
• Lab-4 : Google Cloud Speech API: Qwik
Start
• Cloud ML
• Lab-5 : Cloud ML Engine
Agenda

4. BigQuery

5. BigQuery is Google's serverless, highly scalable, enterprise data
warehouse designed to make all your data analysts productive at an
unmatched price-performance.
Because there is no infrastructure to manage, you can focus on
analyzing data to find meaningful insights without the need for a
database administrator.
https://www.youtube.com/watch?time_continue=2&v=eyBK9nj-7AA

6. ●Get up and running fast
●Scale seamlessly
●Accelerate your insights with powerful analysis
●Protect your business data and investments
●Automatic Backup and Easy Restore
●Real-time Analytics
●Standard SQL

7. The Basics of SQL
●SELECT and FROM
SELECT USER FROM example_table ;

8. ●WHERE
SELECT USER FROM example_table WHERE SHIPPED='YES‘
●GROUP BY
SELECT start_station_name FROM `bigquery-public-data.london_bicycles.cycle_hire` GROUP BY
start_station_name;
●COUNT
SELECT start_station_name, COUNT(*) FROM `bigquery-public-data.london_bicycles.cycle_hire` GROUP BY
start_station_name;
●AS
SELECT start_station_name, COUNT(*) AS num_starts FROM `bigquery-public-data.london_bicycles.cycle_hire`
GROUP BY start_station_name;

9. Natural
Language API /
Speech API
@shivp0616

10. Google Cloud Natural Language API lets you extract information
about people, places, events, (and more) mentioned in text
documents, news articles, or blog posts. You can use it to understand
sentiment about your product on social media, or parse intent from
customer conversations happening in a call center or a messaging
app. You can even upload text documents for analysis.
https://cloud.google.com/natural-language/docs/basics
https://cloud.google.com/natural-language/
Google Cloud Natural Language API

11. • Syntax Analysis: Extract tokens and sentences, identify parts of speech (PoS) and create dependency parse
trees for each sentence.
• Entity Recognition: Identify entities and label by types such as person, organization, location, events, products
and media.
• Sentiment Analysis: Understand the overall sentiment expressed in a block of text.
• Content Classification: Classify documents in predefined 700+ categories.
• Multi-Language: English, Spanish, Japanese, Chinese , French, German, Italian, Korean and Portuguese.
• Integrated REST API: Access via REST API. Text can be uploaded in the request or integrated with Google Cloud
Storage.
Cloud Natural Language API features

12. The Google Cloud Speech API enables easy integration of Google
speech recognition technologies into developer applications. The
Speech API allows you to send audio and receive a text transcription
from the service
https://www.youtube.com/watch?v=k9xLdXNriEo&feature=youtu.be
Google Cloud Speech API

13. • Syntax Analysis: Extract tokens and sentences, identify parts of speech (PoS) and create dependency parse
trees for each sentence.
• Entity Recognition: Identify entities and label by types such as person, organization, location, events, products
and media.
• Sentiment Analysis: Understand the overall sentiment expressed in a block of text.
• Content Classification: Classify documents in predefined 700+ categories.
• Multi-Language: English, Spanish, Japanese, Chinese , French, German, Italian, Korean and Portuguese.
• Integrated REST API: Access via REST API. Text can be uploaded in the request or integrated with Google Cloud
Storage.
Cloud Natural Language API features

14. Cloud ML Engine
@shivp0616

15. Cloud Machine Learning Engine brings the power and flexibility of
TensorFlow, scikit-learn and XGBoost to the cloud.
You can use Cloud ML Engine to train your machine learning models
using the resources of Google Cloud Platform.
Cloud ML Engine offers training and prediction services, which can be
used together or individually.

16. Automatic Resource Provisioning:
Focus on model development and deployment without worrying about infrastructure. The managed service
automates all resource provisioning and monitoring. Build models using managed distributed training
infrastructure that supports CPUs, GPUs, and TPUs. Accelerate model development by training across many
nodes or running multiple experiments in parallel.
HyperTune:
Achieve superior results faster by automatically tuning deep learning hyperparameters with HyperTune. Data
scientists can manage thousands of tuning experiments on the cloud. This saves many hours of tedious and
error-prone work.
Cloud ML Engine features

17. Server-Side Preprocessing:
Push deployment preprocessing to Google Cloud with scikit-learn pipelines and tf.transform. This means that
you can send raw data to models in production and reduce local computation. This also prevents data skew
being introduced through different preprocessing in training and prediction.
Integrated:
Google services are designed to work together. Cloud ML Engine works with Cloud Dataflow for feature
processing and Cloud Storage for data storage.
Multiple Frameworks:
Training and Online Prediction support multiple frameworks to train and serve classification, regression,
clustering, and dimensionality reduction models.
scikit-learn, XGBoost, Keras, TensorFlow.

18. • https://cloud.google.com/speech-to-text/docs/
• https://cloud.google.com/ml-engine/
• https://cloud.google.com/bigquery/
• https://cloud.google.com/sdk/gcloud/reference/ml/language/
Resources

19. Thank you