Prepare Data for ML APIs on Google Cloud
From Raw Data to ML Insights—Your Toolkit for Smarter AI Pipelines.
- Build expertise in Google Cloud's data processing tools
- Learn to call and utilize Google's ML APIs for text, speech, and video analysis
- Streamline data workflows to ensure compatibility with ML models and APIs
- Align with industry
- Build a base for specialized roles in AI development or cloud engineering
- Prepare for more advanced Google Cloud certifications in AI
What you will learn
This course, available via Google Cloud Skills Boost, provides a comprehensive introduction to Google Cloud's AI and machine learning (ML) technologies. Designed for data scientists, AI developers, and ML engineers, it explores the end-to-end data-to-AI lifecycle, covering foundational concepts, development tools, and generative AI solutions. Through hands-on labs and real-world examples, learners gain practical experience with Google Cloud's infrastructure (e.g., Vertex AI, BigQuery ML), pre-trained APIs, AutoML, and generative AI toolkits like Gemini. The course emphasizes building predictive and generative AI projects while navigating Google Cloud's ecosystem.
Google's Beginner-friendly Prepare Data for ML APIs on Google Cloud course Outline
Learn the Prepare Data for ML APIs on Google Cloud
Ready to optimize data for machine learning on Google Cloud? The Prepare Data for ML APIs course equips you with hands-on skills to clean, process, and integrate data with powerful ML APIs like Natural Language and Video Intelligence. From Dataprep to Dataflow and Dataproc, master the tools that turn raw data into ML-ready inputs. Validate your expertise with a Challenge Lab and earn a skill badge. Start your journey today—click here to dive into the labs!
Start your journey today:
Course Structure Includes:
Dataprep Essentials: Clean and prepare data using Trifacta.
Dataflow Pipelines: Create streaming/batch pipelines with templates or Python (Apache Beam SDK).
Dataproc Clusters: Run and manage Apache Spark jobs via console or CLI.
ML API Integration: Using ML API
Natural Language API: Extract entities and analyze sentiment.
Speech-to-Text API: Convert audio to text.
Video Intelligence API: Extract metadata from video files.
Challenge Lab: Apply all skills in a timed, scenario-based assessment.
Training Options
Self Paced Learning
- Lifelong access to high-quality content
- Curated by industry experts
- Customized learning progress
- 24/7 learner assistance and support
- Follow the latest technology trends
Exam Dump
- 100% Real Exam Practice Tests
- 100% Verified Exam Questions & Answers
- 100% Guarantee Passing Rate
- Average 7 Days to Practice & Pass
Description
Learn to clean, process, and integrate data for ML APIs using Dataprep, Dataflow, and Dataproc. Earn a skill badge by completing labs and a Challenge Lab.
Pre-requisites
No Experience needed.
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Generative AI in Real World Workplaces PDF Free Download | SPOTO
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Governing AI- A Blueprint for the Future PDF Free Download | SPOTO
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SPOTO Empowers You to Earn Your Certification.
Benefits of Google Professional Machine Learning Engineer Certification
The Google Professional Machine Learning Engineer certification validates expertise in designing, building, and deploying machine learning models using Google Cloud technologies. It demonstrates proficiency in critical areas such as framing ML problems, architecting scalable solutions, data preparation, model development, and productionization. This certification is highly regarded in the industry, enhancing career prospects by signaling advanced technical skills and practical experience in solving real-world business challenges. Certified professionals often gain a competitive edge in roles like machine learning engineer, data scientist, or AI developer, with opportunities at leading global companies. Additionally, Google recommends at least three years of ML experience for the exam, ensuring that certified individuals possess both theoretical knowledge and hands-on capabilities.
Advantages of Using SPOTO's Exam Preparation Materials
SPOTO's study resources provide comprehensive coverage of the exam syllabus, aligning with key topics like ML problem framing, data processing, model optimization, and pipeline automation. Their materials likely include structured learning paths, practice exams, and real-world case studies that mirror the certification's focus on Google Cloud tools and ML workflows. By leveraging SPOTO's targeted content, candidates can efficiently bridge knowledge gaps, reinforce practical skills, and gain familiarity with the exam format. This focused preparation increases confidence and readiness, particularly for complex tasks such as deploying CI/CD pipelines or optimizing model performance—areas emphasized in the certification. Combined with hands-on experience, SPOTO's resources offer a strategic advantage for achieving certification success.
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