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Throughout my career, I’ve consistently delivered high-quality results, successfully navigating complex requirements across various domains. My commitment to mentoring teams and ongoing learning has yielded remarkable achievements, driving business progress for employers and clients alike.
Although I’ve faced challenges in corporate politics and managerial relationships, I’ve chosen to focus on developing my technical expertise and leadership skills. This perseverance has enabled me to build a robust foundation of knowledge and expertise.
Today, I’m proud to offer employers and clients access to powerful solutions, backed by my extensive experience and dedication to continuous learning. I’m excited to collaborate with like-minded professionals and organizations, driving success and progress through innovative and effective solutions.
Therefore my suggestions to all of you to achieve success despite office politics and corporate challenges:
Building Resilience
Focus on your strengths: Concentrate on your core skills and expertise, and continuously improve them.
Develop a growth mindset: Believe that your abilities can be developed through dedication and hard work.
Set clear goals: Establish specific, measurable, and achievable objectives, and prioritize them.
Navigating Office Politics
Stay professional: Maintain a professional demeanour, even in challenging situations.
Build relationships: Foster positive relationships with colleagues, and seek mentors who can offer guidance and support.
Communicate effectively: Clearly articulate your ideas, and actively listen to others.
Protecting Yourself
Document everything: Keep a record of your accomplishments, challenges, and interactions with colleagues and managers.
Seek support: Build a network of peers, mentors, and professionals outside your organization who can offer advice and support.
Stay adaptable: Be prepared to pivot and adjust to changing circumstances.
Continuing Learning and Growth
Pursue ongoing education: Engage in courses, training, and certifications that enhance your skills and knowledge.
Seek feedback: Request constructive feedback from trusted colleagues and mentors to identify areas for improvement.
Stay industry-aware: Remain up-to-date with industry trends, best practices, and emerging technologies.
Staying Positive and Focused
Prioritize self-care: Maintain a healthy work-life balance, and engage in activities that promote physical and mental well-being.
Celebrate successes: Acknowledge and celebrate your achievements, no matter how small they may seem.
Stay focused on your goals: Remind yourself of your objectives, and let them guide your decisions and actions.
Do you have any more suggestions for all of us? Kindly write in the comments. Thank you.
Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. In Vertex AI, you can now easily train and compare models usingAutoMLor custom code training and all your models are stored in one central model repository. These models can now be deployed to the same endpoints on Vertex AI.
AutoML Vision helps anyone with limited Machine Learning (ML) expertise train high quality image classification models. In this hands-on lab, you will learn how to produce a custom ML model that automatically recognizes damaged car parts. Since the time it takes to train the model is above the time limit of the lab, you will interact and request predictions from a hosted model in a different project trained on the same dataset. You will then tweak the values of the data for the prediction request and examine how it changes the resulting prediction from the model.
Screen Shots from Google Cloud
Objectives
In this lab, you learn how to:
Upload a labeled dataset to Cloud Storage using a CSV file and connect it to Vertex AI as a Managed Dataset.
Inspect uploaded images to ensure there are no errors in your dataset.
Kick off an AutoML Vision model training job.
Request predictions from a hosted model trained on the same dataset.
Setup and requirements
For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.
Sign in to Qwiklabs using an incognito window.
Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time. There is no pause feature. You can restart if needed, but you have to start at the beginning.
When ready, click Start lab.
Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.
Click Open Google Console.
Click Use another account and copy/paste credentials for this lab into the prompts. If you use other credentials, you'll receive errors or incur charges.
Accept the terms and skip the recovery resource page.
Activate Cloud Shell
Cloud Shell is a virtual machine that contains development tools. It offers a persistent 5-GB home directory and runs on Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources. gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab completion.
Click the Activate Cloud Shell button () at the top right of the console.
Click Continue. It takes a few moments to provision and connect to the environment. When you are connected, you are also authenticated, and the project is set to your PROJECT_ID.
In this task you will upload the training images you want to use to Cloud Storage. This will make it easier to import the data into Vertex AI later.
To train a model to classify images of damaged car parts, you need to provide the machine with labeled training data. The model will use the data to develop an understanding of each image, differentiating between car parts and those with damages on them.
In this example, your model will learn to classify five different damaged car parts: bumper, engine compartment, hood, lateral, and windshield.
Create a Cloud Storage bucket
To start, open a new Cloud Shell window and execute the following commands to set some environment variables:
Next, to create a Cloud Storage bucket, execute the following command:
gsutil mb -p $PROJECT_ID \
-c standard \
-l REGION \
gs://${BUCKET}
Copied!
Upload car images to your Storage Bucket
The training images are publicly available in a Cloud Storage bucket. Again, copy and paste the script template below into Cloud Shell to copy the images into your own bucket.
To copy images into your Cloud Storage bucket, execute the following command:
In the navigation pane, click Cloud Storage > Buckets.
Click the Refresh button at the top of the Cloud Storage browser.
Click on your bucket name. You should see five folders of photos for each of the five different damaged car parts to be classified:
Optionally, you can click one of the folders and check out the images inside.
Great! Your car images are now organized and ready for training.
Click Check my progress to verify the objective.
Upload car images to your Storage Bucket
Task 2. Create a dataset
In this task you create a new dataset and connect your dataset to your training images to allow Vertex AI to access them.
Normally, you would create a CSV file where each row contains a URL to a training image and the associated label for that image. In this case, the CSV file has been created for you; you just need to update it with your bucket name and upload the CSV file to your Cloud Storage bucket.
Update the CSV file
Copy and paste the script templates below into Cloud Shell and press Enter to update, and upload the CSV file.
To create a copy of the file, execute the following command:
gsutil cp gs://car_damage_lab_metadata/data.csv .
Copied!
To update the CSV with the path to your storage, execute the following command:
sed -i -e "s/car_damage_lab_images/${BUCKET}/g" ./data.csv
Copied!
Verify your bucket name was inserted into the CSV properly:
cat ./data.csv
Copied!
To upload the CSV file to your Cloud Storage bucket, execute the following command:
gsutil cp ./data.csv gs://${BUCKET}
Copied!
Once the command completes, click the Refresh button at the top of the Cloud Storage bucket and open your bucket.
Confirm that the data.csv file is listed in your bucket.
Create a managed dataset
In the Google Cloud Console, on the Navigation menu () click Vertex AI > Dashboard.
Click Enable all recommended API.
From the Vertex AI navigation menu on the left, click Datasets.
At the top of the console, click + Create.
For Dataset name, type damaged_car_parts.
Select Image classification (Single label). (Note: in your own projects, you may want to check the "Multi-label Classification" box if you're doing multi-class classification).
Select region as REGION
Click Create.
Connect your dataset to your training images
In this section, you will choose the location of your training images that you uploaded in the previous step.
In the Select an import method section, click Select import files from Cloud Storage.
In the Select import files from Cloud Storage section, click Browse.
Follow the prompts to navigate to your storage bucket and click your data.csv file. Click Select.
Once you've properly selected your file, a green checkbox appears to the left of the file path. Click Continue to proceed.
Once the import has completed, prepare for the next section by clicking the Browse tab. (Hint: You may need to refresh the page to confirm.)
Click Check my progress to verify the objective.
Create a dataset
Task 3. Inspect images
In this task, you examine the images to ensure there are no errors in your dataset.
Check image labels
If your browser page has refreshed, click Datasets , select your image name, and then click Browse.
Under Filter labels, click any one of the labels to view the specific training images.
If an image is labeled incorrectly, you can click on it to select the correct label or delete the image from your training set:
Next, click on the Analyze tab to view the number of images per label. The Label Stats window appears on the right side of your browser.
Task 4. Train your model
You're ready to start training your model! Vertex AI handles this for you automatically, without requiring you to write any of the model code.
From the right-hand side, click Train New Model.
From the Training method window, leave the default configurations and select AutoML as the training method. Click Continue.
From the Model details window, enter a name for your model, use: damaged_car_parts_model. Click Continue.
From the Training options window, click Continue.
From the Explainability window, click continue and for Compute and pricing window, set your budget to 8 maximum node hours.
Click Start Training.
Click Check my progress to verify the objective.
Train your model
Task 5. Request a prediction from a hosted model
For the purposes of this lab, a model trained on the exact same dataset is hosted in a different project so that you can request predictions from it while your local model finishes training, as it is likely that the local model training will exceed the limit of this lab.
A proxy to the pre-trained model is set up for you so you don't need to run through any extra steps to get it working within your lab environment.
To request predictions from the model, you will send predictions to an endpoint inside of your project that will forward the request to the hosted model and return back the output. Sending a prediction to the AutoML Proxy is very similar to the way that you would interact with your model you just created, so you can use this as practice.
Get the name of AutoML proxy endpoint
In the Google Cloud Console, on the Navigation menu (≡) click Cloud Run.
Click automl-proxy.
Copy the URL to the endpoint. It should look something like: https://automl-proxy-xfpm6c62ta-uc.a.run.app.
You will use this endpoint for the prediction request in the next section.
Create a prediction request
Open a new Cloud Shell window.
On the Cloud Shell toolbar, click Open Editor.
Click File > New File.
Click on the link, copy the file content into the new file you just created:
Save the file and name it payload.json.
For reference, the content you supplied is a Base64 string from the following image.
Next, set the following environment variables. Copy in your AutoML Proxy URL you retrieved in earlier.
For this model, the prediction results are pretty self-explanatory. The displayNames field should correctly predict a bumper with a high confidence threshold. Now, you can change the Base64 encoded image value in the JSON file you created.
Click Check my progress to verify the objective.
Create the prediction request
Right-click on each image below, then select Save image As….
Follow the prompts to save each image with a unique name. (Hint: Assign a simple name like 'Image1' and 'Image2' to assist with uploading).
Open the Base64 Image Encoder follow the instructions to upload and encode an image to a Base64 string.
Replace the Base64 encoded string value in the content field in your JSON payload file, and run the prediction again. Repeat for the other image(s).
How did your model do? Did it predict all three images correctly? You should see the the following outputs, respectively:
In this lab, you learned how to train your own custom machine learning model and generate predictions on hosted model via an API request. You uploaded training images to Cloud Storage and used a CSV file for Vertex AI to find these images. You inspected the labeled images for any discrepancies before finally evaluating a trained model. Now you've got what it takes to train a model on your own image dataset!
) at the top right of the console.
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