The business value of AI is projected to reach $1.2 trillion this year according to Gartner. Yet 40% of enterprise companies are not adopting AI. Why not?
3 simple reasons:
- Data Scientists are hard to find and hire. Want to become one? There’s a lab for that. Enroll in the Data Science on GCP quest by Friday, October 12th and you’ll get 40 credits (free of charge, no CC required). The labs in this quest are derived from the book Data Science on Google Cloud Platform.
- Data analysts with SQL skills (and other programming languages) are also rare. Get practice running SQL queries in the BigQuery console in the Introduction to SQL for BigQuery and Cloud SQL lab. Then experiment with SQL and ML in the Ingest data into the Cloud Using Google App Engine lab. You’ll run Python scripts to download, automatically fetch, and clean data using Google App Engine. You’ll also create a new application and deploy it to the Google App Engine:
Then use a Flask framework to ingest data and invoke it using cron:3. Infrastructure AI is resource-intensive, in terms of both staff, and compute power. Many on-prem setups simply cannot handle the demands of AI. You can learn to take advantage of Google Cloud’s compute power to run your advanced AI jobs. The Google Cloud Dataflow to process data lab shows you how to configure BigQuery and install Python packages to use Apache Beam:
Then you will monitor the progress of your Cloud Dataflow job and inspect the processed data:
Need more practice with the GCP infrastructure? Visualize data with Google Data Studio by running the query to get the IP address to connect to the Cloud SQL:
Then create table views to look at flights that are delayed by 10, 15, 20 minutes:
After connecting with the Data Studio, you’ll create a data visualization for flight delays:
Tackle the quest to practice ingestion, preparation, processing, querying, exploring and visualizing of data sets using GCP. When you earn the badge, let us know. You can find us at @Qwiklabs – and we’re always happy to hear about your accomplishments!
How do you learn something new? Do you read a book, watch a video, take a course? Maybe the right answer is poetry.
When you’re learning to use data science tools you need, well, big data to practice. And that’s where Shakespeare comes in. Billy S. wrote 37 plays, 154 sonnets and invented 1700 words (like eyeball, bedazzled, and arch-villain). His works are publicly available and perfect for data manipulation. With the help of Shakespeare, learn how data science tools like BigQuery and TensorFlow can help you take advantage of powerful Google Cloud machine learning technology. And for a limited time, use the links below and get 37 free Qwiklabs credits (as many credits as plays) to take the bard’s favorite labs.
– BigQuery: Qwik Start – Command Line: Storing and querying massive datasets can be expensive. Learn how Google Cloud can help you save, after all, “if money go before, all ways do lie open” (The Merry Wives of Windsor). Learn how to move your data into BigQuery and take advantage of the processing power of Google’s infrastructure.
– Big Data Analysis to Slide Presentation: Did you know that Shakespeare invented the word “manager”? Impress yours by learning how to gather and crunch your data, then generate a slide and spreadsheet presentation to blow away management and stakeholders with your breathtaking analytical capabilities and intelligent conclusions.
– Run a Big Data Text Processing Pipeline in Cloud Dataflow: Learn how to run a Dataflow pipeline that counts the occurrences of unique words in a text file. Not only will you learn how many times the word “alas” appears in King Lear, you’ll learn to use a powerful tool for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation.
– TensorFlow For Poets: Finally, while we don’t know if a rose by any other name smells as sweet, we can use TensorFlow to identify a rose among a floral lineup. Think about what else you could use this technology for – scanning for free parking spaces? Locating your company’s discarded scooters?
Have you heard of IIoT, Industrial Internet of Things? Enterprise companies are discovering and experimenting with IoT in industries from manufacturing to energy to automobiles. As companies recognize more consumer and industrial uses for IoT, demand for IoT expertise is rising.
Set yourself apart by earning the new Google Cloud IoT badge. Click the link below to enroll by Friday, October 12, and you will get a free 30-day pass to the entire Qwiklabs lab catalog, including the new IoT labs. Think you can earn the badge in 30 days?
You get 8 labs to help you learn about Google Cloud’s IoT Core service and its integration with other services like GCS, Dataprep, Stackdriver and Firestore. Use simulator code to mimic IoT devices, then use what you learn to implement the same streaming pipeline with real world IoT devices. Here’s some of what you will do:
Complete all 8 labs and add the IoT on Google Cloud badge to your resume. Good luck!
Interested in self-driving cars? Learn the technology behind the magic – then sit back, relax, and let the car do it for you.
- How do autonomous cars recognize speed limit signs? “See” for yourself with the Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API lab (bonus, follow that link and get the lab free of charge). You’ll build a model that recognizes landmarks and items in photos with a simple REST API.
- Then, how do cars know the speed limit? The Extract, Analyze, and Translate Text from Images with the Cloud ML APIs lab shows you how to translate signs – not just read them – which means machine learning can improve your commute no matter what side of the road you drive on.
- Some cars know what’s around you – trucks, cars, motorcycles, even reindeer. Learn how to use Google’s Machine Learning tools to Classify Images of Clouds in the Cloud with AutoML Vision.
- Even when conditions aren’t perfect, cars can predict what’s next. Start making predictions with the Cloud ML Engine: Qwik Start lab.
With these labs, you’re well on your way to your next badge, Machine Learning APIs. Enroll here, and you’ll get 55 Qwiklabs credits to start your next Quest (limited offer, ends January 31, 2019).
Good luck, buckle up!
Jobs in the tech industry (machine learning, AI, Cloud) will grow between 12%–37% through 2022. Your computer science curriculum can unlock possibilities for students’ career advancement. Take advantage of free Google Cloud Platform training resources to help your students grow computer science skills:
- If you’re associated with a non-profit university, complete this form to request free training on Qwiklabs and Coursera.
Next, use your benefit to have your students enroll in a few machine learning Quests:
- Baseline: Data, ML, AI Quest: Since machine learning is growing rapidly in the tech industry, help your students get started with data tools like Cloud SQL, BigQuery, APIs, Bigtable, Dataproc, Dataprep, and Cloud ML Engine. If your students are interactive learners, they can follow along Mark as he guides their path.
- Machine Learning APIs Quest: Give your students the opportunity to work with a handful of machine learning APIs. For example, they’ll work with Translation API and Natural Language API to translate text into other languages. They’ll also work with Cloud Vision API to capture text from images and Google Cloud Dialogflow to build a responsive chat bot.
- Data Science on GCP Quest. Challenge your students to use Tensorflow, Dataflow and Data Studio to practice all aspects of datasets, such as ingestion, exploration, visualization, etc.
Fun fact: This Quest is derived from the book, Data Science on Google Cloud Platform by Valliappa Lakshmanan. Lak is on a mission to democratize machine learning. If your students were to read the book, they will learn how to apply statistical and machine learning methods to real-world problems.
If your class is interested in learning about the fundamentals of Google Cloud in-person, register for a study jam. Your students will get free access to the GCP Essentials Quest during the session and you’ll get two days of free lesson plans. If they complete the Quest within 1 month of their session, they’ll get a 30-day pass to Qwiklabs (free of charge, no CC required). They can use the pass to tackle any of the machine learning Quests mentioned above.
Why wait? Take advantage of these programs and set your students up for success by helping them add these badges to their resumes:
Democratizing machine learning continues, this time for .NET. Google Cloud is committed to supporting developers getting their .NET workloads up and running on the GCP. Enroll in the Developing Data and Machine Learning Apps with C# and you’ll get 35 Qwiklabs credits, good towards labs in the new Quest (free of charge, no CC required).
Each lab demonstrates how to hook up your data to Google’s ML tools with just a few simple commands. You even get app templates you can re-use when you work on your own projects.
- Using BigQuery with C# (60 mins): To or not to , that is the question. Use BigQuery to analyze Shakespeare, a GitHub dataset and data you upload yourself (could be anything).
- Using the Natural Language API with C# (45 mins): Analyze how awesome Yukihiro Matsumoto is, with documentSentiment. What did you get? The overall sentiment analysis consists of two fields: sentiment and magnitude. Sentiment ranges between -1.0 (negative) and 1.0 (positive). Magnitude indicates the overall strength of emotion (both positive and negative) within the given text, between 0.0 and +inf. Pop quiz: who remembers how to diagram a sentence? Now you can #makegoogledoit, one more talent of the Natural Language API.
- Using the Speech-to-Text API with C# (45 mins): How many languages do you speak? The Google Cloud Speech-to-Text API has 120 different languages and variants. Even French or a Brooklyn accent. For extra credit, see if the API can transcribe a clip of your speech when you take the lab. Check it out on TechCrunch (:
- Using the Video Intelligence API with C# (45 mins): You’ll work with a shot-change detection example in this lab. What other types of changes would you want to identify? Maybe a model that detects activity in wildlife monitoring cameras or security footage…
- Using the Vision API with C# (45 mins): What do otter crossing signs have in common with the Eiffel Tower? The Google Cloud Vision API can recognize them both. Learn how to protect wildlife and organize your vacation photos with a lab. Then apply the skills you learned next time you build a license plate recognition app, or rank the most popular landmarks #teampixel is sharing.
- Using the Translation API with C# (45 mins): How do you say “hello world” in Turkish? I don’t know – but we can use the Translation API to do it for us. Text is translated using the Neural Machine Translation (NMT) model. If the NMT model is not supported for the requested language translation pair, then the Phrase-Based Machine Translation (PBMT) model is used. And “hello world” in Turkish? Selam Dünya!
- Cloud Spanner: Create a Gaming Leaderboard with C# (60 mins): Ready player one – wait, who won the last round? Learn to create a leaderboard with C# – this one is the most advanced lab in the Quest so prepare to be challenged!
Hurry, click here to enroll by the weekend and get 35 credits to help you get started. Good luck!
Infrastructure Architect is the fourth highest-paying job in the tech industry. Add the Cloud Architecture Quest badge to your resume and set your sights on the Cloud ACE certification (and maybe one of the 1,097 open architect jobs). The Quest maps to the topics covered in the new ACE certification exam guide and puts your GCP knowledge to test. Here’s how:
- In the Cloud IAM: Qwik Start lab you’ll assign a role to a second user and remove assigned roles associated with Identity and Access Management (IAM). This maps to section 5 from the exam guide, Configuring Access and Security.
Explore the IAM console in the lab and you’ll see:
- Practice creating Google Compute Engine VM instance in the Stackdriver: Qwik Start lab. It will help you with section 3.1 from the exam guide, deploying and implementing Compute Engine resources.
- Tackle the Orchestrating the Cloud with Kubernetes lab to provision a Kubernetes cluster and deploy and manage Docker containers using kubectl. This helps you practice for section 3.2, Deploying and implementing Kubernetes Engine resources.
Here’s a brain teaser! Nginx container is running, expose it using kubectl expose command in the lab.
What happened? Share your results @Qwiklabs
- Conquer the Deployment Manager – Full Production lab to set up black box monitoring with Stackdriver Dashboard and establish uptime check alerts to trigger incident responses. This maps to section 3.7 from the exam guide, Deploying an Application using Deployment Manager.
Why wait? Enroll in the Cloud Architecture Quest by Friday, August 24th and get a 1-month pass to help you prepare for the Associate Cloud Engineer Certification exam.
Are you ready for Next week? Join Google Cloud for the event of the year. Grow your network, grow your skills, and learn about the growth of Google Cloud – what’s happening now, and what’s next. And even if you’re not in San Francisco, join a Next event near you. Find one here.
Get a head start today by familiarizing yourself with key cloud concepts. Cloud training is available online, on-demand and on the real cloud console (not a video, lecture, or slideshow). Check out these hands-on labs, and follow any of the links below in the next 24 hours and you’ll get 25 Qwiklabs credits free of charge to help you start your Google Cloud training journey:
- Kubernetes training – get an intro to GCP tools with the GCP Essentials Quest, then learn how to make container orchestration part of your own workflow with Kubernetes in the Google Cloud. Want a challenge? Deploy game servers on clusters and run the Cloud Vision API with the Kubernetes Solutions Quest.
- Data Engineer training – Did you know there are over 1,500 data engineering jobs on LinkedIn right now? Get your first experience with data tools like Cloud SQL, BigQuery, APIs, Bigtable, Dataproc, Dataprep, and Cloud ML Engine with the Baseline: Data Quest (Mark can help guide your path!). Then work with Dataproc, Tensorflow and more with the Data Engineering Certification Practice Quest. And if you’re looking for a real resume boost, consider getting certified.
- Machine Learning – Familiarize yourself with key concepts with the ML APIs Quest. And aspiring data scientists, there are labs for you too. The Data Science on the Google Cloud Platform Quest, just released, gives you hands-on experience with Tensorflow, Dataflow, and Data Studio.
Complete a Quest and you’ll earn a badge. A badge shows your “flight time” with Google Cloud, and differentiates you as someone with hands-on GCP experience. Click this link → log into Qwiklabs → you will get 25 Qwiklabs credits to help you tackle your next Quest. Hurry, link expires in 24 hours.
See you next week!
The U.S. will be celebrating its 242nd year of independence on July 4th. Set your own “independence” day – learn new skills and take your career where *you* want to go. Enroll in any of these Quests by Monday, July 8th and get a 30 day pass to earn these badges (free of charge, no credit card required). What skills can you add to your resume?
- Baseline: Infrastructure Quest: Learn to use Cloud Storage, deploy a containerized application with Google Kubernetes Engine, build a Docker image, create and deploy a Cloud Function, publish and consume messages, create a Cloud IoT Core device registry and use Cloud Spanner. Here’s the badge you’ll earn:
- GCP Essential Quest: This Quest has the best of both worlds! While it helps you get more comfortable with the Google Cloud, like spinning up a virtual machine, it also challenges you to configure key infrastructure tools through working with more advanced Stackdriver and Kubernetes concepts. Tackle the Quest to earn this badge:
- Application Development Quest: Think you’re a Java Jedi or a Python powerhouse? Well we’ve got challenging Quests for you! In these two Quests, you’ll either put your Java or Python skills to test by developing cloud-based applications using GCP services such as App Engine, Cloud Datastore and Cloud Spanner. Then integrate these services and deploy to an App Engine and a Kubernetes Engine. Are you excited to earn these badges?
4. Scaling Your Infrastructure Quest: Not for beginners! If you’re ready for an expert level Quest, this one’s for you. Learn how to: set up multiple NAT gateways, deploy an autoscaling Compute Engine, customize Stackdriver logging, set up Jenkins on Google Kubernetes Engine to help orchestrate your software delivery pipeline and use Spinnaker to continuously deploy the application when changes are made. You’ll earn this badge:
Celebrate your own independence day with us. Enroll by Monday, July 8th, get a 30 day pass (free of charge, no credit card required), and take charge of your career!
If you haven’t enrolled in the machine learning Quests mentioned in this blog, you’re missing out! One last chance for free labs, follow any of the Quest links in this post and get a 30 day pass to finish these Quests. Hurry, offer expires Thursday, June 28 (free of charge, no CC required).
Tackle any of the Quests from the roadmap below to become a ML master. If you’re a beginner… begin at the beginning 🙂
Earn a machine learning Quest badge or two, get practice with real-world scenarios, and make the most out of your time at Next ‘18 with some ML rockstars. Who might you meet at Next ’18?
Fei Fei Li is a Chief Scientist of Cloud AI and ML at Google Cloud. She works in the areas of computer vision and cognitive neuroscience. Her publications include Scaling Human-Object Interaction Recognition through Zero-Shot Learning and Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks. Get hands-on practice with Cloud AI at scale with the Machine Learning APIs Quest.
Or you might run into Valliappa Lakshmanan. Lak is on a mission to democratize machine learning so anyone can do it using Google’s infrastructure. Check out his latest book, Data Science on the Google Cloud Platform, explaining how to apply statistical and machine learning methods to real-world problems. And try it for yourself with a lab from the Scientific Data Processing Quest!
Take some labs ahead of time, gain a little experience, and ask better questions at the ML sessions offered at the Next ’18! Check out these sessions:
Can’t get enough of machine learning? When you register for Next ‘18, you will have the option to add some of these ML boot camps:
- End-to-End Machine Learning with TensorFlow on GCP: You’ll go through the process of building a complete machine learning pipeline, covering ingest, exploration, training, evaluation, deployment and prediction.
- Building and updating a Machine Learning Model on Edge Network using Cloud ML: You’ll learn how to process and store IoT or other data using Cloud Pub/Sub, Cloud Dataflow, Cloud Storage, and Google BigQuery.
- Building Chatbots with Machine Learning: You’ll use Dialogflow and Cloud Natural Language API to rapidly convert an HR manual document into a fully functional and conversational chatbot. You’ll also add text and voice interactions to the chatbot and secure and scale it for production.
If you can’t make it to Next in San Francisco, take classes worldwide. Find one here.
To make the most of your time at Next ’18, enroll in these ML Quests by Thursday, June 28 (free of charge, no CC required).