MLOps

update : December 2021

Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera:

Download link: https://drive.google.com/file/d/1BIpdN0dQ_mNHuZdmcRhJx2l8yXRpZ86T/view?usp=sharing

COURSE 1 Introduction to Machine Learning in Production

In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.

Week 1: Overview of the ML Lifecycle and Deployment

Week 2: Selecting and Training a Model

Week 3: Data Definition and Baseline


COURSE 2 Machine Learning Data Lifecycle in Production

In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.

Week 1: Collecting, Labeling, and Validating data

Week 2: Feature Engineering, Transformation, and Selection

Week 3: Data Journey and Data Storage

Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types


COURSE 3 Machine Learning Modeling Pipelines in Production

In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.

Week 1: Neural Architecture Search

Week 2: Model Resource Management Techniques

Week 3: High-Performance Modeling

Week 4: Model Analysis

Week 5: Interpretability


COURSE 4 Deploying Machine Learning Models in Production

In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system.

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.

Week 1: Model Serving: Introduction

Week 2: Model Serving: Patterns and Infrastructure

Week 3: Model Management and Delivery

Week 4: Model Monitoring and Logging

Download full resolution images:

Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: https://drive.google.com/file/d/1BIpdN0dQ_mNHuZdmcRhJx2l8yXRpZ86T/view?usp=sharing

C1+C2: https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing

Monitoring and observability,

End to End solution for computer vision applications in industry

my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera + very good practice & lab

COURSE 4 Deploying Machine Learning Models in Production: Week 4: Model Monitoring and Logging

Download and see more: https://www.tiziran.com/topics/courses/mlops :

you can download my complete summary of Machine Learning Engineering for Production (MLOps) Specialisation: https://www.tiziran.com/topics/courses/mlops .

#MLOps #ComputerVision #Tiziran

C4-W4.png

tools for experiment tracking, Logging metrics using TensorBoard, vertex tensorboard, progressive delivery

End to End solution for computer vision applications in industry

my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera + very good practice & lab

COURSE 4 Deploying Machine Learning Models in Production: Week 3: Model Management and Delivery

Download and see more: https://www.tiziran.com/topics/courses/mlops

#MLOps #ComputerVision #Tiziran

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model serving, balance cost, latency and throughput, improving prediction latency and reducing resource costs, tensorflow serving, torchserve, KF serving, triton inference server, scaling infrastructure, pre processing operations needed before inference, ETL: extract , transform, load; kafka, pub sub, cloud dataflow, beam, spark streaming;

End to End solution for computer vision applications in industry

my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera + very good practice & lab

COURSE 4 Deploying Machine Learning Models in Production: Week 1: Model Serving: Introduction + Week 2: Model Serving: Patterns and Infrastructure +

Download and see more: https://www.tiziran.com/topics/courses/mlops

#MLOps #ComputerVision #Tiziran

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explainable AI, model interpretation methods, TensorFlow Lattice, understanding model predictions, PDP: partial dependence plots, permutation feature importance, SHAP: SHapley Additive exPlanation, testing concept activation vectors, testing concept activation vectors, LIME: local interpretable model agnostic explanations, Google cloud AI explanations for AI platform, XRAI: eXplanation with Ranked Area Integrals,

End to End solution for computer vision applications in industry

my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera + very good practice & lab

COURSE 3 Machine Learning Modeling Pipelines in Production: Week 5: Interpretability

Download and see more: https://www.tiziran.com/topics/courses/mlops

#MLOps #ComputerVision #Tiziran

C3-W5.png

End to End solution for computer vision applications in industry

my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera + very good practice & lab

COURSE 3 Machine Learning Modeling Pipelines in Production: Week 4: Model Analysis

tensorflow model analysis, TFMA, TFX, model debugging, TFMAL: TensorFlow Model Analysis, model remediation techniques, continuous evaluation and monitoring,

Download and see more: https://www.tiziran.com/topics/courses/mlops

#MLOps #ComputerVision #Tiziran

C3-W4.png

End to End solution for computer vision applications in industry

my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera + very good practice & lab

COURSE 3 Machine Learning Modeling Pipelines in Production: Week 3: High-Performance Modeling

distributed training, Gpipe: Open-source tensorflow library (using lingvo), teacher and student networks, idea: create a simple ;student; model that learns from a complex ;teacher; model. make efficientNets robust to noise with distillation

Download and see more: https://www.tiziran.com/topics/courses/mlops

#MLOps #ComputerVision #Tiziran

C3-W3.png

my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera + very good practice & lab

COURSE 3 Machine Learning Modeling Pipelines in Production: Week 2: Model Resource Management Techniques

PCA, PLS, LDA, latent semantic indexing/analysis (LSI and LSA) (SVD) [removes redundant features from the dataset], independent component analysis (ICA), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA),

Quantization [make models run faster and use less power with low-precision] and pruning, ML kit, Core ML, TensorFlow Lite (TFX), post training quantization, quantization aware training (QAT),

Download and see more: https://www.tiziran.com/topics/courses/mlops

#MLOps #ComputerVision #Tiziran

C3-W2.png

my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera + very good practice & lab

COURSE 3 Machine Learning Modeling Pipelines in Production: Week 1: Neural Architecture Search

NAS, Keras autotuner, AutoML,

Download and see more: https://www.tiziran.com/topics/courses/mlops

#MLOps #ComputerVision #Tiziran

C3-W1.png

my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera + very good practice & lab

COURSE 1 Introduction to Machine Learning in Production + COURSE 2 Machine Learning Data Lifecycle in Production

https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing

Download and see more: https://www.tiziran.com/topics/courses/mlops

#MLOps #ComputerVision #Tiziran

C1+C2.png

my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera + very good practice & lab

COURSE 2 Machine Learning Data Lifecycle in Production: Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types

semi-supervised data augmentation: UDA, semi-supervised learning with GANs; policy-based data augmentation: AutoAugment; time series, advanced labeling; active learning; human activity recognition (HAR);

Download and see more: https://www.tiziran.com/topics/courses/mlops

#MLOps #ComputerVision #Tiziran

my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera + very good practice & lab

COURSE 2 Machine Learning Data Lifecycle in Production: Week 3: Data Journey and Data Storage

!pip install ml-metadata,

Download and see more: https://www.tiziran.com/topics/courses/mlops

#MLOps #ComputerVision #Tiziran

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Week 1: Collecting, Labeling, and Validating data

ML modeling vs production ML; data collection, labeling, validating ; TFDV;

C2-W1.pdf

w2

COURSE 1 Introduction to Machine Learning in Production:


Course 2: Week1, Week 2: Selecting and Training a Model

error analysis example for speech recognition example; iterative process of error analysis; prioritising what to work on; skewed datasets; performance auditions; F1 score based on precision and recall; data augmentation;

Week 3: Data Definition and Baseline

data definition; label ambiguity; type of data; HLP; meta-data; data pipeline; balanced train/dev/test splits in small dataset; Dilligence: assess the feasibility and value of potential solution;

Course 2: Week 3:

Course 2: Week 4:

Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI

COURSE 3 Machine Learning Modeling Pipelines in Production - Week 1: Neural Architecture Search (NAS) - AutoML, Hyperparameter tuning, Cloud AutoML

#autoML #NAS #hyperparameter

Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI

COURSE 3 Machine Learning Modeling Pipelines in Production

Week 3: High-Performance Modeling

PCA, ICA, SVD, QAT: quantization aware training,


my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera

COURSE 2 Machine Learning Data Lifecycle in Production: Week 2: Feature Engineering, Transformation, and Selection

feature scaling, normalization and standaridization; TensorFlow extended; TensorFlow Transform; tf.transform analyzers; TensorFlow Ops;

Download and see more: https://www.tiziran.com/topics/courses/mlops

#MLOps #ComputerVision #Tiziran