Thursdays, May 19th and 26th

Engineer Data in Google Cloud

Join Google Software Engineer Thomas, and Customer Engineer Sonakshi, for hands-on workshops showcasing how to:

• Build data pipelines using Cloud Dataprep by Trifacta, Pub/Sub, and Dataflow.
• Use Cloud IoT Core to collect and manage MQTT-based devices.
• Use Cloud Storage, Dataflow, and BigQuery to perform ETL.
• Build a machine learning model using BigQuery ML.
• Use Cloud Composer to copy data across multiple locations.

These labs are great resources for understanding topics that will appear in the Google Cloud Certified Professional Data Engineer Certification.

Tuesday, May 31st

Career Journey and Pathway with Ameya

Join Googler Ameya Deshpande and learn about his career journey.

Thursday, June 2nd

Perform Foundational Data, ML, and AI tasks in Google Cloud

Join a Google Software Engineer Paul Ruiz for a hands-on workshop showcasing her experiences with Google Cloud as she guides you in learning the basic features for the following machine learning and AI technologies:

• BigQuery
• Cloud Speech AI
• Cloud Natural Language API
• AI Platform, Dataflow
• Cloud Dataprep by Trifacta
• Dataproc
• Video Intelligence API

These labs are great resources for understanding topics that appear in the Machine Learning Engineer learning pathway.

Thursdays, June 9th & 16th

Explore Machine Learning Models with Explainable AI

Join a Google Cloud Engineer and learn how to do the following using Explainable AI:

• Build and deploy a model to an AI platform for serving (prediction)
• Use the What-If Tool with an image recognition model
• Identify bias in mortgage data using the What-If Tool
• Compare models using the What-If Tool to identify potential bias

These labs are great resources for understanding topics that appear in the Machine Learning Engineer learning pathway.

Thursdays, June 23rd & 30th

Build and Deploy Machine Learning Solutions on Vertex AI

Join a Google Cloud Engineer and learn how to use Google Cloud’s unified Vertex AI platform and its AutoML and custom training services to train, evaluate, tune, explain, and deploy machine learning solutions.

These labs and their lab series are for those interested in becoming a professional Data Scientists and Machine Learning Engineer. The datasets and labs are built around high business impact enterprise machine learning use cases; these include retail customer lifetime value prediction, mobile game churn prediction, visual car part defection identification, and fine tuning BERT for review sentiment classification. Learners who complete this skill badge will gain hands-on experience with Vertex AI for new and existing ML workloads and be able to leverage AutoML, custom training, and new MLOps services to significantly enhance development productivity and accelerate time to value.

These labs are great resources for understanding topics that appear in the Machine Learning Engineer learning pathway.