Rigorous trainer selection
Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.
- Triple validation: technical, pedagogical, sectoral.
- Minimum rating 4.8/5 over the last 12 sessions.
1 seat held for the next session
30:00Held for another
Request a call back and an advisor reaches you within 24 business hours, Monday to Saturday.
Training Google AutoML - No-Code ML for Professionals in 2026
Trusted by — schools, companies and industrial groups
Every program built with a dedicated engineer
Learning objectives
- Master Google AutoML to develop professional ML models without code
- Prepare and train optimized datasets for vision and NLP in enterprise settings
- Deploy scalable and certifiable predictive solutions with AutoML
- Optimize no-code model performance for critical business applications
- Implement automated ML pipelines connected to Google Cloud
- Evaluate and monitor AutoML models in real production
Our social commitment
A school kit donated to a child for every training booked
To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.
- Backpack, notebooks and essential supplies
- Distributed through our partner charities
- Included, at no extra cost to you
The Learni story
Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.
Don't let this gap widen
Why this program matters
Without Google AutoML skills, your teams waste 50% of their time on manual ML models, inflating development costs to 3x.
In 2026, 68% of data projects fail due to lack of no-code automation, resulting in 200k€ losses in missed business opportunities according to Gartner.
Unequipped companies see customer churn rise by 25% without accurate predictions.
Every quarter without AutoML skills exposes your competitiveness to an insurmountable gap against market leaders who deploy AI in days, not months.
Program
Module 1Google AutoML Fundamentals: Environment Setup and Datasets (GCP Console, Vertex AI, Data Preparation)
Dive into the Google AutoML universe via the Vertex AI console, set up your secure professional environment on Google Cloud Platform, import and clean real enterprise datasets using integrated tools like Data Labeling Service, perform automated train/validation/test splits, conduct initial exploratory analysis to identify hidden patterns, produce an initial data quality report ready for training, and validate your first setups with practical exercises on concrete image classification cases.
Module 2Google AutoML Vision: Train Classification and Object Detection Models (AutoML Vision, Auto Hyperparameters, Evaluation Metrics)
Create and train your first vision models without writing a single line of code using AutoML Vision to classify product images or detect industrial anomalies, adjust automatic hyperparameters to boost accuracy up to 95%, test on enterprise datasets like e-commerce catalogs, analyze natively generated confusion matrices and ROC curves, optimize with integrated data augmentations, deploy an interactive prototype via the Edge interface, and document your metrics for immediate professional audit.
Module 3Google AutoML Tables and NLP: Tabular Prediction and Text Analysis (AutoML Tables, Natural Language, Auto Feature Engineering)
Advance to predictive analysis on structured data with AutoML Tables for sales forecasting or customer churn, integrate NLP for sentiment analysis on user reviews or emails, prepare automated features via Google's intelligent engine, launch parallel trainings on large volumes, evaluate performance with business-adapted RMSE and F1-score, iterate quickly on hybrid text/table models, and generate SHAP explanations to justify predictions in executive meetings.
Module 4Google AutoML Optimization: Advanced Tuning and Custom Models (Hyperparameter Search, Ensemble Methods, Cost/Performance Trade-off)
Deepen AutoML model optimization with advanced hyperparameter searches and ensemble methods to achieve accuracies over 90% on industrial benchmarks, integrate custom transformations without code via the Vertex AI UI, analyze GCP costs for production scaling, test robustness on noisy or biased enterprise datasets, reduce false positives via integrated post-processing, build a reproducible pipeline with deliverables like TensorBoard dashboards, and prepare for Qualiopi certification of your skills.
Module 5Google AutoML Deployment: Production, Monitoring and Scaling (Vertex AI Endpoints, No-Code MLOps, Performance Alerts)
Deploy AutoML models to scalable endpoints on Vertex AI for real-time API integration in CRM or IoT applications, configure automatic monitoring of drifts and performance with Cloud Monitoring, implement no-code A/B tests to validate business impact, secure with IAM and VPC for enterprise environments, generate quantified ROI reports on live predictions, train your team on maintenance via practical tutorials, and conclude with a certifying capstone project deployed in simulated production.
Evaluation method
- Certifying multiple-choice quiz to validate skills acquired at the end of the training
- Continuous assessment via practical exercises and daily quizzes
- Presentation of the AutoML capstone project to the expert trainer
Learning method
- Courses led by an active certified Google Cloud trainer
- Hands-on exercises on real business cases and open-source datasets
- Progressive capstone project for a fully deployed ML model
- Detailed course notes and unlimited access to GCP resources
Methods, materials and delivery
The Training Google AutoML - No-Code ML for Professionals in 2026 program is delivered onsite or remote (blended-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.
The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.
Equipment required
For the smooth delivery of the Training Google AutoML - No-Code ML for Professionals in 2026 program, the following equipment is required:
- Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
- Training environments installed on workstations or accessible online
- Course materials, hands-on exercises and complementary resources
- Post-training access to materials and educational resources
For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.
* contact us for remote delivery feasibility** ratio varies depending on the program
Skills assessment methods
Assessment of skills acquired during the Training Google AutoML - No-Code ML for Professionals in 2026 program is performed through:
- During training: case studies, hands-on labs and professional scenarios
- End of training: self-assessment questionnaire and skills evaluation by the trainer
- After training: completion certificate detailing acquired skills
Program accessibility
Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.
Enrollment terms and lead times
Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.
Our method
Training quality, guaranteed at every step
Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.
You meet the trainer beforehand
30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.
- Live briefing on goals and team context.
- Veto right — we swap the trainer for free if needed.
Content tailored to your context
No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.
- Hands-on cases drawn from your stack and projects.
- Program co-written then validated by your team.
Continuous quality follow-up
Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.
- NPS, knowledge quizzes and skills self-assessment.
- Satisfaction guarantee: fully satisfied or free rework.
A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.
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FAQ
Frequently asked questions
Which tools are used in Google AutoML training?+
Hands-on work runs on GCP Console, vertex AI, data Preparation, autoML Vision, auto Hyperparameters and evaluation Metrics. Those tools come from the Google AutoML program itself, delivered on site over 5 days, so participants practise on the stack they will use at work.
What can participants do after Google AutoML training?+
They can master Google AutoML to develop professional ML models without code, prepare and train optimized datasets for vision and NLP in enterprise settings and deploy scalable and certifiable predictive solutions with AutoML. Those are the stated objectives of the Google AutoML program at Intermediate level, checked by the end-of-session assessment.
What does the Google AutoML program cover, day by day?+
Day 1: Google AutoML Fundamentals: Environment Setup and Datasets (GCP Console, Vertex AI, Data Preparation). Day 2: Google AutoML Vision: Train Classification and Object Detection Models (AutoML Vision, Auto Hyperparameters, Evaluation Metrics). Day 3: Google AutoML Tables and NLP: Tabular Prediction and Text Analysis (AutoML Tables, Natural Language, Auto Feature Engineering). Day 4: Google AutoML Optimization: Advanced Tuning and Custom Models (Hyperparameter Search, Ensemble Methods, Cost/Performance Trade-off). Day 5: Google AutoML Deployment: Production, Monitoring and Scaling (Vertex AI Endpoints, No-Code MLOps, Performance Alerts). The Google AutoML program runs over 5 days, delivered on site.
How is Google AutoML training assessed?+
Assessment relies on certifying multiple-choice quiz to validate skills acquired at the end of the training, continuous assessment via practical exercises and daily quizzes and presentation of the AutoML capstone project to the expert trainer. Learni issues a completion certificate together with the individual assessment report at the end of the Google AutoML program, which an L&D team can file as evidence.
How much does Google AutoML training cost per participant?+
$5,765 (USD) per participant. A detailed quote is sent within one business day, and team pricing applies from the second participant.
How long does Google AutoML training take?+
5 days, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.
Who pays for Google AutoML training?+
Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
What do participants need to know before Google AutoML training?+
Knowledge of data manipulation, basics of Google Cloud Platform and descriptive statistics.
Is a certificate issued after Google AutoML training?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.
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