Grasp ML Algorithms, Knowledge Modeling, TensorFlow & Google Cloud AI/ML Providers. 137 Questions, Solutions with Explanations
What you’ll study
Framing ML issues
Architecting ML options
Designing knowledge preparation and processing programs
Creating ML fashions
Automating and orchestrating ML pipelines
Monitoring, optimizing, and sustaining ML options
Description
- Translate enterprise challenges into ML use instances
- Select the optimum resolution (ML vs non-ML, customized vs pre-packaged)
- Outline how the mannequin output ought to clear up the enterprise drawback
- Establish knowledge sources (out there vs splendid)
- Outline ML issues (drawback sort, end result of predictions, enter and output codecs)
- Outline enterprise success standards (alignment of ML metrics, key outcomes)
- Establish dangers to ML options (assess enterprise affect, ML resolution readiness, knowledge readiness)
- Design dependable, scalable, and out there ML options
- Select acceptable ML companies and elements
- Design knowledge exploration/evaluation, characteristic engineering, logging/administration, automation, orchestration, monitoring, and serving methods
- Consider Google Cloud {hardware} choices (CPU, GPU, TPU, edge units)
- Design architectures that adjust to safety considerations throughout sectors
- Discover knowledge (visualization, statistical fundamentals, knowledge high quality, knowledge constraints)
- Construct knowledge pipelines (arrange and optimize datasets, deal with lacking knowledge and outliers, forestall knowledge leakage)
- Create enter options (guarantee knowledge pre-processing consistency, encode structured knowledge, handle characteristic choice, deal with class imbalance, use transformations)
- Construct fashions (select framework, interpretability, switch studying, knowledge augmentation, semi-supervised studying, handle overfitting/underfitting)
- Prepare fashions (ingest numerous file sorts, handle coaching environments, tune hyperparameters, monitor coaching metrics)
- Check fashions (conduct unit exams, examine mannequin efficiency, leverage Vertex AI for mannequin explainability)
- Scale mannequin coaching and serving (distribute coaching, scale prediction service)
- Design and implement coaching pipelines (establish elements, handle orchestration framework, devise hybrid or multicloud methods, use TFX elements)
- Implement serving pipelines (handle serving choices, take a look at for goal efficiency, configure schedules)
- Observe and audit metadata (arrange and monitor experiments, handle mannequin/dataset versioning, perceive mannequin/dataset lineage)
- Monitor and troubleshoot ML options (measure efficiency, log methods, set up steady analysis metrics)
- Tune efficiency for coaching and serving in manufacturing (optimize enter pipeline, make use of simplification strategies)
English
language
Content material
Introduction
Introduction
The right way to Enhance Knowledge High quality
Exploratory Knowledge Evaluation (EDA)
How EDA is Utilized in Machine Studying
Knowledge evaluation and visualization
Supervised Studying
Linear Regression
Logistic Regression
Machine Studying Vs. Deep Studying
Automated Machine Studying
Evaluating AutoML Fashions
ML Mannequin Utilizing BigQuery ML
BigQuery ML Mannequin Sorts
Introduction to Neural Networks and Deep Studying
Gradient Descent
Loss Capabilities
Activation Capabilities
Ensemble Strategies
Tensorflow, Tensorflow on Google Cloud
Introduction to Tensorflow
Tensorflow – Scalar, Vector, Matrix, 4D Tensors
Tensorflow APIs
Tensorflow’s tf.knowledge.Dataset APIs
TensorFlow Knowledge Dealing with
Embeddings
TensorFlow 2 and the Keras Purposeful API
TensorFlow Prolonged (TFX) Overview
Structure for MLOps utilizing TensorFlow Prolonged, Vertex AI Pipelines, and Cloud
Vertex AI
Create Customized Coaching Jobs
Export mannequin artifacts for prediction
Vertex AI Characteristic Retailer
Vertex AI Mannequin Monitoring
Vertex Explainable AI
Vertes AI Vizier
BigQuery ML
Characteristic Engineering in BigQuery
Apply Questions & Solutions
Half 1 – 10 Questions
Half 2 – 10 Questions
Half 3 – 10 Questions
Half 4 – 10 Questions
Half 5 – 10 Questions
Half 6 – 10 Questions
Half 7 – 10 Questions
Half 8 – 10 Questions
Half 9 – 10 Questions
Half 10 – 10 Questions
Half 11 – 10 Questions
Half 12 – 10 Questions
Half 13 – 10 Questions
Half 14 – 7 Questions
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