Process Optimisation Specialist

This role is responsible for developing ML modules and AI applications to optimize production processes, ensuring efficient and sustainable operations.

AutoML Expertise

Proficiency in developing AutoML modules that cover data preprocessing, model selection, and evaluation.

Machine Learning

Strong skills in applying machine learning techniques to optimize production processes.

Process Optimization Specialist / Operational Optimization Hyperparameter Optimization for Machine Learning

In this course, you will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models. If you are regularly training machine learning models as a hobby or for your organization and want to improve the performance of your models, if you are keen to jump up in the leader board of a data science competition, or you simply want to learn more about how to tune hyperparameters of machine learning models, this course will show you how.

Provider
Udemy
Target
  • Students who want to know more about hyperparameter optimization algorithms
  • Students who want to understand advanced techniques for hyperparameter optimization
  • Students who want to learn to use multiple open source libraries for hyperparameter tuning
  • Students interested in building better performing machine learning models
  • Students interested in participating in data science competitions
  • Students seeking to expand their breadth of knowledge on machine learning
Sector
  • Technology
  • Data Science
  • Artificial Intelligence
Area
  • Machine Learning
  • Hyperparameter Optimization
  • Data Analysis and Modeling
Method
Online
Certification
Yes
Duration
9.5 hours on-demand video

Learning Outcomes

  • Hyperparameter tuning and why it matters
  • Cross-validation and nested cross-validation
  • Hyperparameter tuning with Grid and Random search
  • Bayesian Optimization

Learning Content

  • Introduction
  • Hyperparameter Tuning - Overview
  • Performance metrics
  • Cross-Validation
  • Basic Search Algorithms
  • Bayesian Optimization
  • Other SMBO Algorithms
  • Scikit-Optimize


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Process Optimization Specialist / AI Application Development Foundations of AI and Machine Learning

This course provides a comprehensive introduction to fundamental components of artificial intelligence and machine learning (AI & ML) infrastructure. You will explore the critical elements of AI & ML environments, including data pipelines, model development frameworks, and deployment platforms. The course emphasizes the importance of robust and scalable design in AI & ML infrastructure.

Provider
Coursera
Target
  • Individuals with intermediate programming knowledge of Python
  • Professionals with basic understanding of AI and ML capabilities
  • Those familiar with generative AI (GenAI) and pretrained large language models (LLMs)
  • Learners with a foundational knowledge of statistics
Sector
  • Information Technology
  • Software Development
  • Data Science
  • Artificial Intelligence and Machine Learning
Area
  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Data Pipelines
  • Model Development Frameworks
  • Deployment Platforms
Method
Online
Certification
Yes
Duration
Approximately 35 hours to complete (Recommended pace: 3 weeks at 11 hours per week)

Learning Outcomes

  • Analyze and discuss critical components of AI & ML infrastructure and their interrelationships
  • Describe efficient data pipelines for AI & ML workflows
  • Evaluate model development frameworks for various AI & ML applications
  • Prepare AI & ML models for deployment in production environments

Learning Content

  • Introduction to AI/ML environments
  • Data management in AI/ML
  • Considering and selecting model frameworks
  • Deploying machine learning models
  • The evolving role of AI/ML engineers


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Process Optimization Specialist/Machine Learning Machine Learning Algorithms in the Real-World Specialization

This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business, or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.

Provider
Coursera
Target
  • Professionals interested in machine learning
  • Data analysts and data scientists
  • Managers and decision-makers in various industries
Sector
  • Technology
  • Finance
  • Medicine
  • Engineering
  • Business
Area
  • Machine learning application
  • Data analysis and automation
  • Project management in machine learning
  • Classification algorithms and model deployment
Method
Online
Certification
Yes
Duration
1 month/at 10 hours a week
Assessment
No
Cost
Free

Learning Outcomes

  • Clearly define an ML problem
  • Survey available data resources and identify potential ML applications
  • Prepare data for effective ML applications
  • Take a business need and turn it into a machine learning application
  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from Alberta Machine Intelligence Institute

Learning Content

  • Introduction to Applied Machine Learning (Course 1)
  • Machine Learning Algorithms: Supervised Learning Tip to Tail (Course 2)
  • Data for Machine Learning (Course 3)
  • Optimizing Machine Learning Performance (Course 4)


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Process Optimization Specialist Machine Learning in Production

In this Machine Learning in Production course, you will build intuition about designing a production ML system end-to-end: project scoping, data needs, modeling strategies, and deployment patterns and technologies. You will learn strategies for addressing common challenges in production like establishing a model baseline, addressing concept drift, and performing error analysis. You’ll follow a framework for developing, deploying, and continuously improving a productionized ML application. Machine learning engineering for production combines the foundational concepts of machine learning with the skills and best practices of modern software development necessary to successfully deploy and maintain ML systems in real-world environments.

Provider
www.coursera.org
Target
  • Data scientists and machine learning practitioners
  • Software engineers interested in machine learning
  • Machine learning engineers focusing on deployment
  • Technical project managers in AI and machine learning projects
  • Professionals transitioning to machine learning roles
Sector
  • Information technology and software development
  • Data science and analytics
  • Artificial intelligence and machine learning
  • Business intelligence and data engineering
Area
  • Machine learning lifecycle management
  • Model deployment and maintenance
  • Production-level machine learning systems
  • Data preparation and error analysis
  • Software engineering best practices in AI applications
Method
Online
Certification
Yes
Duration
Flexible schedule, approx. 11 hours
Assessment
No
Cost
Free

Learning Outcomes

  • Identify key components of the ML project lifecycle and pipeline
  • Select the best deployment and monitoring patterns for different scenarios
  • Optimize model performance and metrics for crucial dataset slices
  • Solve production challenges involving various data types

Learning Content

  • Overview of the ML Lifecycle and Deployment
    • Introduction to machine learning production systems
    • Deploying production systems while handling evolving data
  • Modeling Challenges and Strategies
    • Addressing error analysis and working with various data types
    • Dealing with class imbalance and skewed datasets
  • Data Definition and Baseline
    • Ensuring label consistency for classification problems
    • Establishing and improving performance baselines


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Machine Learning in Production

Mid Level Employee
Extended Know-How

Machine Learning Algorithms in the Real World Specialization

Mid Level Employee
Foundations

AI Application Development

Ability to develop AI applications for process optimization, ensuring efficient and sustainable production

Foundations of AI and Machine Learning

Mid Level Employee
Foundations

Operational Optimization

Experience in optimizing operational parameters based on real-time data from sensors and physics-based models.

Hyperparameter Optimization for Machine Learning

Mid Level Employee
Foundations