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 Friday, Sept. 21).
Good luck, buckle up!
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. Learn how with the newest Qwiklabs Quest, Developing Data and Machine Learning Apps with C#. Use this link to enroll this weekend, and get 25 Qwiklabs credits free of charge, good towards labs in the new Quest.
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 25 credits free of charge to help you get started. Good luck!
What kind of cloud is this?
I don’t know either. Can you think of other types of images that would be useful to classify? For example, if you have an online tire shop, wouldn’t it be cool if your customers could upload a photo of their current tire to find a matching replacement?
AutoML can help your tire shop decrease the number of returns and increase customer delight. And it’s easier than you think – learn how to take advantage of this powerful new tool with a new lab, AutoML Vision API.
Here’s some of what you’ll do:
- You’ll be prompted to log into the AutoML UI. Open the link in a new tab to make it easy to hop between AutoML and your lab manual. Then, you’ll log into AutoML with your Qwiklabs-provided credentials.
- In the AutoML UI, you will see a button for “Billing”. You do not need to worry about billing for this lab, as Qwiklabs covers the cost of this lab for you.
- When you load data into your storage bucket, you’ll set your project name with one of the first commands you run. Find your project name on the left of your lab manual, just below your GCP credentials:
AutoML is currently in beta, so we expect to see constant changes to the user interface. Send any questions to Support@Qwiklabs.com and we’ll be happy to help. Plus you’ll help us keep the lab up to date as the product continues to improve!
Curious about what type of clouds were spotted during our trip to Muir Woods? Here are our model’s predictions:
The model is 99% certain these are cumulus clouds! Prediction certainty and accuracy would be better if we trained our model with more than 20 images per category. When you’re helping customers order tires, you will probably want to use 100+ photos per category.
Big things are happening in Google Cloud. BigQuery ML, for example, just announced at Next ‘18. Even if you weren’t there, you can still take advantage of 33+ new labs released at the conference. And if you haven’t yet seen the new BigQuery interface, you can check it out with a lab.
Here’s what’s new on Qwiklabs:
- Predict Visitor Purchases with a Classification Model in BQML: Use the latest and greatest technology to answer the top question in online retail. Click here to take the lab free of charge, first 25 readers only.
- Classify Images of Clouds in the Cloud with AutoML Vision: AutoML Vision helps developers with limited ML expertise train high quality image recognition models. Take AutoML on a test drive with this lab.
- Managing Cloud Infrastructure with Terraform: #MakeGoogleDoIt – launch your cloud resources, that is. Learn how Terraform can boost your team’s efficiency, by creating configuration files that can be shared, treated as code, edited, reviewed, and versioned.
- Kubernetes Solutions: Work hard, play harder. Use Kubernetes to run dedicated gaming servers, plus 7 more advanced Kubernetes use cases in this Quest.
- Network Performance and Optimization: If you’re the one they call when the network is down, these labs are for you. Learn how GCP can help you sleep at night with better network speed, performance, and reliability.
- Challenge Quest: Your company is ready to launch a brand new website, but the person who built the new site left the company before they could deploy it. Your challenge is to deploy the site in the public cloud. This and six other scenarios make up the Challenge Quest, to test your skills in a simulated crisis. Good luck!
If you have an Advantage subscription, all of this new content is included in your subscription, no extra charge. Not a subscriber yet? Use promo code NEW33 for 33% off your first month. Promo code is valid through August 1o (Friday).
Good luck on your next Quest!
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!
Have you seen the Make Google do it commercials? Learn how to make Google tackle the tough questions you want to answer for your job or business with 15+ Machine Learning and Big Data labs that can help you answer questions like:
How do self-driving cars know the speed limit? What SKUs will see the most traffic on my retail website when it’s raining? How can I analyze sentiment of my customers’ conversations with my team? And how can I prepare for Google’s Professional Data Engineer certification exam?
- Start with the Baseline: Big Data, ML, AI Quest if you’re new to Google Cloud. Get a basic understanding of GCP tools like Cloud SQL, BigQuery, APIs, Bigtable, Dataproc, Dataprep, and Cloud ML Engine, and explore public datasets like worldwide genomic data. With about five hours of lab work, you can earn your badge!
- Next, tackle more advanced concepts with the Machine Learning APIs Quest. This is where you’ll learn how to teach a self-driving car to recognize speed limit signs, analyze blocks of text for customer satisfaction, and five more use cases. More fun with APIs, plus experience with Awwvision and Dialogflow.
- You’re ready for the next step, the Data Engineering exam practice Quest. These nine labs, designed by Google’s training team, help you practice critical job skills for the role of a Data Engineer. You’ll practice use cases with TensorFlow, an IoT pipeline, BigQuery and Datalab together, and you’ll learn how to correspond your sales data with the weather!
- Thinking about the Data Engineer certification? Try the practice exam to assess your readiness, and don’t “make Google do it” this time (: Set a timer and see how far you can get. How did it go?
- Extra credit: Challenge yourself with some of the most advanced labs on Qwiklabs today. The Google Cloud Solutions II Quest works through complex scenarios to knit multiple services together to solve a problem. The labs in this Quest come directly from engineers who developed these solutions in real-time. Not for the faint of heart! Dig deep into TensorFlow use cases, use BigQuery to explore NCAA data and build custom interactive dashboards with Boke, and more.
Why not try for a ML or Big Data Quest badge? A badge shows your “flight time” with Google Cloud, and differentiates you as someone with hands-on GCP experience. Would one (or more) of these badges look good on your resume?
You want to get the most out of your data. But when you hear words like “public datasets”, “training data models”, and “correlation percentage”, your eyes glaze over. So how do you start taking advantage of Google Cloud’s data tools?
Here are some easy ways to get started.
Watch and learn. Join Priyanka and Heather to learn how to get meaningful insights with Google BigQuery (4:39).
Then, get hands-on practice with a hands-on lab for yourself. Just click the link below the YouTube video and follow instructions. Did you get your free Qwiklabs credits, to take your first BigQuery lab on us? Just be sure to click before April 2. And if you want to double-check your work, go back to the video and follow along with the demo, starting at 3:45.
Ready for a little more of a challenge? Try the Weather Data and BigQuery lab, and practice correlating diverse data sets. And today only, this lab is featured for #1creditwednesday, which means this lab is available for 1 credit (normally priced at 5 credits).
In this lab, you will take a look at analyzing public data with BigQuery – starting with weather data from NOAA and citizen complaints data from New York City.
Using public datasets, you will find what types of municipal complaints are correlated with weather. For example, you will find (not surprisingly) that complaints about residential furnaces are most common when it is cold outside:
What types of big data do you think you might correlate?
Weather and traffic volume on your retail website
Daily temperature in your town and foot traffic on your business’s street
NCAA winning percentages and uniform colors (there’s a lab for that!)
You can find the Weather Data and BigQuery lab here. Special today, the lab is available for 1 credit only (usually priced at 5 credits), so now’s the perfect time to check it out. Click the green button to start the lab. You’ll see a lower price than the screenshot below, today only.
See how the left panel changes? Those are your credentials to log into the Google console. Use these credentials (not your personal credentials) so Qwiklabs covers the cost of all your lab activities.
Next, you’ll need to accept the terms of service for the student account. Don’t worry about account recovery or emails – you won’t ever need to use this account again.
Then you’re off on your lab! Follow the lab manual and at the end of the lab, you will have correlated two massive public datasets using BigQuery. What will you correlate next? We’d love to hear how you applied what you learned in the lab to your real-world data questions, let us know @Qwiklabs!