Machine Learning Engineer is one of the most lucrative and dynamic career paths in Data Science and AI

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To work effectively as a MLOps Engineer, you must be a technically sound programmer with a solid foundation in mathematics, statistics, cloud computing and software engineering.

ML Engineer

There are several Certification programs from big tech companies and educators that validate your competency in Machine Learning.

>>> AWS, Microsoft Azure, Google Cloud, Udacity and DataCamp.

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>>> UDACITY & AWS

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AWS Machine Learning Engineer

The goal of this Nanodegree program is to equips learners with machine learning skills required to build, train and deploy ML models in production using Amazon SageMaker.

 Educational Objectives  

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Create ML Models using AWS Tools

Deploy trained ML models

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Computer Vision

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Neural Networks

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Deep Learning

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Machine Learning with AWS SageMaker

>>> Udacity and Microsoft

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Machine Learning Engineer for Microsoft Azure

This Nanodegree program helps learners strengthen their skills by building and deploying Machine Learning solutions using open source tools and popular frameworks.

 Educational Objectives  

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Configure ML pipelines in Azure

Learn Automated Machine Learning

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Azure MLOps and its features

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Shipping ML models into production

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Learn to use Azure ML SDK

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CONTINUE

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>>> Google Cloud Training

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ML Engineer Certification - Google Cloud

A Google Certified - Professional ML Engineer designs, builds, optimizes, operates, and maintains ML systems to solve complex business challenges using Google Cloud ML Engine.

 Educational Objectives  

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Frame Machine Learning problems

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Data preparation and processing

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Develop ML models

Automating & orchestrating ML pipeline

Feature Engineering

>>> Udacity

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Machine Learning DevOps Engineer

This Nanodegree program equips practitioners to employ the best DevOps practices for building, training, and deployment of ML models.

 Educational Objectives  

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Deploy ML models using AWS SageMaker, Azure ML, etc.

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Learn continuous delivery

Learn model validation within a CI/CD

Automate pipelines in ML

Automate data workflows

Automated model scoring and monitoring

Prevent model-degradation

>>> DataCamp

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Machine Learning Scientist in Python

This comprehensive career-track is designed and developed by DataCamp to help learners master the essential skills to land a machine learning job role.

 Educational Objectives  

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ML Fundamentals

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Supervised & Unsupervised Learning

Advanced Machine Learning

Feature Engineering

Deep Learning, NLP, TensorFlow, Keras, & Pytorch

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Winning a Kaggle Competition

>>> DataCamp

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Machine Learning Scientist in R

This program aims to equip R Programmers to become ML/AI Scientist by learning the Scientific libraries and frameworks in Machine Learning.

 Educational Objectives  

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ML Fundamentals

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Supervised & Unsupervised Learning

Bayesian Data Analysis

Bayesian Regression Modeling

Natural Language Processing

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Support Vector ML

Apache Spark and R

>>> Coursera and ASU 

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Machine Learning MasterTrack Certificate

This comprehensive certification program will equip you with the working knowledge of Machine Learning and AI through a combination of both theory and practice.

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Statistical Machine Learning

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Deep Learning Architectures

Machine Learning Methods

Deep Learning in Visual Computing

Image Classification

 Educational Objectives  

>>> Closing Notes

These machine learning certifications will equip you to master specific tools, learn the technical concepts and guide you through building realistic, complete machine learning applications.

We have also compiled the high-quality Machine Learning resources suitable according to the specialization and experience-level.

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