Grasp ML Algorithms, Information 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
Growing ML fashions
Automating and orchestrating ML pipelines
Monitoring, optimizing, and sustaining ML options
Description
- Translate enterprise challenges into ML use instances
- Select the optimum answer (ML vs non-ML, customized vs pre-packaged)
- Outline how the mannequin output ought to remedy the enterprise downside
- Determine knowledge sources (accessible vs ultimate)
- Outline ML issues (downside sort, consequence of predictions, enter and output codecs)
- Outline enterprise success standards (alignment of ML metrics, key outcomes)
- Determine dangers to ML options (assess enterprise influence, ML answer readiness, knowledge readiness)
- Design dependable, scalable, and accessible ML options
- Select acceptable ML providers and elements
- Design knowledge exploration/evaluation, function 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 issues throughout sectors
- Discover knowledge (visualization, statistical fundamentals, knowledge high quality, knowledge constraints)
- Construct knowledge pipelines (set up and optimize datasets, deal with lacking knowledge and outliers, forestall knowledge leakage)
- Create enter options (guarantee knowledge pre-processing consistency, encode structured knowledge, handle function 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 varied file varieties, handle coaching environments, tune hyperparameters, observe coaching metrics)
- Take a look at fashions (conduct unit assessments, evaluate 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, check for goal efficiency, configure schedules)
- Observe and audit metadata (set up and observe 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 methods)
English
language
Content material
Introduction
Introduction
Find out how to Enhance Information High quality
Exploratory Information Evaluation (EDA)
How EDA is Utilized in Machine Studying
Information 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 Information Dealing with
Embeddings
TensorFlow 2 and the Keras Useful 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
Follow 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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