In this course, you will learn how to develop ML (Machine Learning) models to resolve business issues with the help of Google Cloud technologies which are widely used to design ML models. You will also learn the process of framing ML problems while designing ML models. You will also be able to design and develop solutions for ML Architect, as well. You will learn about how to process and prepare data to configure, enhance and troubleshoot ML solutions. Through the subject matter of this course, you will learn about the techniques of processing data, including the deployment of Simple Linear, Multiple Linear, and Polynomial Regression. You will be able to configure the process of clustering data. Here, you will also earn adequate knowledge and skills to start working on NLP (Natural Language Processing) and Deep Learning. You will also learn about the process of model boosting and model selection.
PROFESSIONAL MACHINE LEARNING ENGINEER TRAINING
Instructor
admin
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Prerequisites
- There is no particular prerequisite mentioned to get enrolled in this course.
- It is ideal if you have prior experience of working with ML, but not required.
- It is expected that you are familiar with the ML Data model and ML Architect.
- It would be better if you have the fundamental knowledge about Data Clustering and NLP (Natural Language Processing)
- It is also expected that you are familiar with supervised learning and unsupervised learning.
What will you gain after this course
- You can develop ML models to train the ML architecture with a particular set of data.
- You will learn the details about Data Clustering
- You will be able to work on ML solutions
- You will earn adequate knowledge and skills on Deep Learning and NLP
- You will also be able to develop ML solutions using different types of the data regression model
Jobs you can get
with a Professional Machine Learning Engineer
- Machine learning Data Associate
- Machine Learning – Computer Vision
- Machine Learning – Full Stack Developer
- Machine Learning Engineer
- Manager of Applied Machine Learning
Select your preferred training delivery mode
Framing ML Problems
<ul>
<li> Translating business challenges into ML use cases.</li>
<li> Defining ML problems</li>
<li> Defining business success criteria</li>
<li> Identifying risks to feasibility of ML solutions.</li>
</ul>
<li> Translating business challenges into ML use cases.</li>
<li> Defining ML problems</li>
<li> Defining business success criteria</li>
<li> Identifying risks to feasibility of ML solutions.</li>
</ul>
Architecting ML Solutions
<ul>
<li> Designing reliable, scalable, and highly available ML solutions.</li>
<li> Choosing appropriate Google Cloud hardware components.</li>
<li> Designing architecture that complies with security concerns across sectors/industries.</li>
</ul>
<li> Designing reliable, scalable, and highly available ML solutions.</li>
<li> Choosing appropriate Google Cloud hardware components.</li>
<li> Designing architecture that complies with security concerns across sectors/industries.</li>
</ul>
Designing Data Preparation and Processing Systems
<ul>
<li> Exploring data (EDA)</li>
<li> Building data pipelines</li>
<li> Creating input features (feature engineering)</li>
</ul>
<li> Exploring data (EDA)</li>
<li> Building data pipelines</li>
<li> Creating input features (feature engineering)</li>
</ul>
Developing ML Models
<ul>
<li> Building models</li>
<li> Training models</li>
<li> Testing models</li>
<li> Scaling model training and serving</li>
</ul>
<li> Building models</li>
<li> Training models</li>
<li> Testing models</li>
<li> Scaling model training and serving</li>
</ul>
Automating and Orchestrating ML Pipelines
<ul>
<li> Designing and implementing training pipelines.</li>
<li> Implementing serving pipelines.</li>
<li> Tracking and auditing metadata.</li>
</ul>
<li> Designing and implementing training pipelines.</li>
<li> Implementing serving pipelines.</li>
<li> Tracking and auditing metadata.</li>
</ul>
Monitoring, Optimizing, and Maintaining ML Solutions
<ul>
<li> Monitoring and troubleshooting ML solutions.</li>
<li> Performance and business quality of ML model predictions</li>
<li> Logging strategies</li>
<li> Establishing continuous evaluation metrics (e.g., evaluation of drift or bias)</li>
<li> Understanding Google Cloud permissions model</li>
<li> Identification of appropriate retraining policy</li>
<li> Common training and serving errors (TensorFlow)</li>
<li> ML model failure and resulting biases</li>
<li> Tuning performance of ML solutions for training and serving in production.</li>
<li> Optimization and simplification of input pipeline for training</li>
<li> Simplification techniques</li>
</ul>
<li> Monitoring and troubleshooting ML solutions.</li>
<li> Performance and business quality of ML model predictions</li>
<li> Logging strategies</li>
<li> Establishing continuous evaluation metrics (e.g., evaluation of drift or bias)</li>
<li> Understanding Google Cloud permissions model</li>
<li> Identification of appropriate retraining policy</li>
<li> Common training and serving errors (TensorFlow)</li>
<li> ML model failure and resulting biases</li>
<li> Tuning performance of ML solutions for training and serving in production.</li>
<li> Optimization and simplification of input pipeline for training</li>
<li> Simplification techniques</li>
</ul>
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Course details
Duration
Full time, Part-time, Evening and Weekends, Virtual online
Video
London, Flexible online
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