Google Certified Professional Machine Learning Engineer

Master ML Algorithms, Data Modeling, TensorFlow & Google Cloud AI/ML Services. 137 Questions, Answers with Explanations

Master ML Algorithms, Data Modeling, TensorFlow & Google Cloud AI/ML Services. 137 Questions, Answers with Explanations

Overview

Framing ML problems, Architecting ML solutions, Designing data preparation and processing systems, Developing ML models, Automating and orchestrating ML pipelines, Monitoring, optimizing, and maintaining ML solutions

Anyone wishing to get Google Cloud Certified Professional Machine Learning Engineer

Some prior experience with Google Cloud and Machine Learning will help. Also if you are already certified with Google Professional Data Engineer that will help you greatly.

  • Translate business challenges into ML use cases

  • Choose the optimal solution (ML vs non-ML, custom vs pre-packaged)

  • Define how the model output should solve the business problem

  • Identify data sources (available vs ideal)

  • Define ML problems (problem type, outcome of predictions, input and output formats)

  • Define business success criteria (alignment of ML metrics, key results)

  • Identify risks to ML solutions (assess business impact, ML solution readiness, data readiness)

  • Design reliable, scalable, and available ML solutions

  • Choose appropriate ML services and components

  • Design data exploration/analysis, feature engineering, logging/management, automation, orchestration, monitoring, and serving strategies

  • Evaluate Google Cloud hardware options (CPU, GPU, TPU, edge devices)

  • Design architectures that comply with security concerns across sectors

  • Explore data (visualization, statistical fundamentals, data quality, data constraints)

  • Build data pipelines (organize and optimize datasets, handle missing data and outliers, prevent data leakage)

  • Create input features (ensure data pre-processing consistency, encode structured data, manage feature selection, handle class imbalance, use transformations)

  • Build models (choose framework, interpretability, transfer learning, data augmentation, semi-supervised learning, manage overfitting/underfitting)

  • Train models (ingest various file types, manage training environments, tune hyperparameters, track training metrics)

  • Test models (conduct unit tests, compare model performance, leverage Vertex AI for model explainability)

  • Scale model training and serving (distribute training, scale prediction service)

  • Design and implement training pipelines (identify components, manage orchestration framework, devise hybrid or multicloud strategies, use TFX components)

  • Implement serving pipelines (manage serving options, test for target performance, configure schedules)

  • Track and audit metadata (organize and track experiments, manage model/dataset versioning, understand model/dataset lineage)

  • Monitor and troubleshoot ML solutions (measure performance, log strategies, establish continuous evaluation metrics)

  • Tune performance for training and serving in production (optimize input pipeline, employ simplification techniques)

Deepak Dubey

Highly Skillful, Knowledgeable and Result-Driven IT Professional with over 20 years of Experience (Totally Hands-on).

AWS Cloud Architecture, Design, Development, DevOps, SysOps - EC2, ECS, EKS, Elastic Beanstalk, Lambda, S3, Glacier, Storage Gateway, RDS, DynamoDB, ElastiCache, Redshift, VPC, ELB, Route 53, CloudWatch, CloudFormation, CloudTrail, OpsWorks, Elastic MapReduce, Kinesis, Data Pipeline, SWF, SQS, SNS, SES

Big Data Engineering - Hadoop, HDFS, YARN, Hive, Spark, Kafka, MongoDB, HBase, Cassandra, Storm, ZooKeeper, Redis, Elastisearch, Logstash, Kibana, Grafana

Data Science, Machine Learning, AI, Python, TensorFlow, Natural Language Processing

Java/Java EE/Web/Web Services, Spring, Spring MVC, Microservices, RESTful API, Spring Boot, Hibernate Development

Full Stack Development experience using MERN (MongoDB, Express, ReactJS, Node) Stack. Proficient with Angular, HTML5, CSS3, Bootstrap, Ant Design

DevOps - Kubernetes, Docker, Jenkins, Maven, Gradle, GIT, JIRA, Terraform, Puppet, Chef, Ansible, Prometheus

Google Cloud Platform - Cloud SQL, Datastore, Bigtable, Cloud Spanner, Cloud Dataflow, Dataproc, BigQuery, AI Platform, Datalab, Dataprep, Data Studio, Cloud Composer

I hold 50+ Internationally recognized Professional Certifications listed as below:-

Amazon Web Services  (9X Certified)

AWS Certified Solutions Architect - Professional

AWS Certified DevOps Engineer - Professional

AWS Certified Machine Learning - Specialty

AWS Certified Security - Specialty

AWS Certified Advanced Networking - Specialty

AWS Certified Data Analytics - Specialty

AWS Certified Database - Specialty

Google

Google Cloud Certified Professional Machine Learning Engineer

TensorFlow Developer Certificate

Google Cloud Certified Professional Data Engineer

Microsoft Azure

Microsoft Certified: Azure Solutions Architect Expert

Microsoft Certified: Azure AI Engineer Associate

Big Data

Elasticsearch, Logstash, Kibana Certified Engineer

Cloudera Certified Associate (CCA) Spark and Hadoop Developer

Confluent Certified Developer For Apache Kafka

Confluent Certified Operator for Apache Kafka

DevOps

Certified Kubernetes Application Developer

Certified Kubernetes Administrator

Docker Certified Associate

Certified Jenkins Engineer 2018

Python

Certified Python Associate

Java

OCP Java SE 6 & 8 Programmer

SOA and Web Services

Oracle SOA Suite 12c Certified Implementation Specialist

Oracle Certified Expert, Java EE 6 Web Services Developer

Security

CISSP & CISM

Enterprise Architecture and Management

TOGAF 9 Certified, PRINCE2 and ITIL


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