Machine Learning Training in Wellington

This instructor-led, live training in Wellington (delivered either online or on-site) is designed for beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

By the end of this training, participants will be able to:

• Understand the concept and benefits of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

This instructor-led, live training in Wellington (online or on-site) is aimed at participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customised chatbots for various applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of chatbot development.
• Navigate the Google Cloud Platform and access AutoML.
• Prepare data for training chatbot models.
• Train and evaluate custom chatbot models using AutoML.
• Deploy and integrate chatbots into various platforms and channels.
• Monitor and optimise chatbot performance over time.

This instructor-led, live training in Wellington (online or on-site) is designed for intermediate-level AI developers, machine learning engineers, and system architects who wish to optimise AI models for edge deployment.

By the end of this training, participants will be able to:

• Understand the challenges and requirements involved in deploying AI models on edge devices.
• Apply model compression techniques to reduce the size and complexity of AI models.
• Use quantisation methods to improve model efficiency on edge hardware.
• Implement pruning and other optimisation techniques to enhance model performance.
• Deploy optimised AI models across a range of edge devices.

This instructor-led, live training in Wellington (online or on-site) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

By the end of this training, participants will be able to:

• Understand the principles of Edge AI and its benefits.
• Set up and configure the edge computing environment.
• Develop, train, and optimise AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

This instructor-led, live training in Wellington (online or on-site) is designed for advanced-level AI engineers and data scientists with intermediate to advanced experience who aim to enhance DeepSeek model performance, minimise latency, and deploy AI solutions efficiently using modern MLOps practices.

By the end of this training, participants will be able to:

• Optimise DeepSeek models for efficiency, accuracy, and scalability.
• Implement best practices for MLOps and model versioning.
• Deploy DeepSeek models on cloud and on-premises infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

MLOps on Kubernetes is a framework for automating the training, validation, packaging, and deployment of machine learning models using containerised pipelines and GitOps workflows.

This instructor-led, live training (online or on-site) is aimed at intermediate-level practitioners who wish to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be equipped to:

• Design end-to-end CI/CD pipelines for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-led presentations and technical deep dives.
• Hands-on exercises that build real-world CI/CD workflows.
• Live-lab practice deploying ML workloads to Kubernetes.

Course Customisation Options

• Organisations may request tailored content aligned with their internal MLOps tools and infrastructure.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Course Format

• Guided presentations and collaborative discussions.
• Hands-on labs using real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customisation Options

• Customised versions of this training can be arranged to align with your team’s technology stack and project requirements.

TinyML is the practice of deploying optimised machine learning models on resource-constrained edge devices.

This instructor-led, live training (delivered online or on-site) is designed for advanced-level technical professionals who aim to design, optimise, and deploy complete TinyML pipelines.

By the end of this training, participants will be able to:

• Collect, prepare, and manage datasets for TinyML applications.
• Train and optimise models for low-power microcontrollers.
• Convert models into lightweight formats suitable for edge devices.
• Deploy, test, and monitor TinyML applications in real-world hardware environments.

Course Format

• Instructor-led lectures and technical discussions.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Course Customisation Options

• To customise the training with specific toolchains, hardware boards, or internal workflows, please contact us to arrange.

This instructor-led, live training in Wellington (online or on-site) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimise AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Use tools and techniques for model conversion and optimisation.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Wellington (online or on-site) is designed for advanced-level professionals seeking to master the technologies underpinning autonomous systems.

By the end of this training, participants will be able to:

• Design and implement AI models for autonomous decision-making.
• Develop control algorithms for autonomous navigation and obstacle avoidance.
• Ensure safety and reliability in AI-powered autonomous systems.
• Integrate autonomous systems with existing robotics and AI frameworks.

This instructor-led, live training in Wellington (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end of this training, participants will be able to:

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualise and interpret the results of image classification models.

TinyML is an approach to deploying machine learning models on low-power, resource-constrained devices operating at the network edge.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

By the end of this course, participants will be able to:

• Identify security risks unique to on-device TinyML inference.
• Implement privacy-preserving mechanisms for edge AI deployments.
• Harden TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in constrained environments.

Course Format

• Engaging lectures supported by expert-led discussions.
• Practical exercises emphasising real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tooling.

Course Customisation Options

• Organisations may request a tailored version of this training to align with their specific security and compliance needs.

This instructor-led, live training in Wellington (available online or on-site) is designed for advanced-level professionals who wish to deepen their understanding of machine learning models, refine their skills in hyperparameter tuning, and learn how to deploy models effectively using Google Colab.

By the end of this training, participants will be able to:

• Implement advanced machine learning models using popular frameworks such as Scikit-learn and TensorFlow.
• Optimise model performance through hyperparameter tuning.
• Deploy machine learning models in real-world applications using Google Colab.
• Collaborate on and manage large-scale machine learning projects within Google Colab.

This instructor-led, live training in Wellington (online or onsite) is designed for intermediate-level professionals who wish to apply AI techniques to optimise yield management in semiconductor manufacturing.

By the end of this training, participants will be able to:

• Analyse production data to identify factors affecting yield rates.
• Implement AI algorithms to enhance yield management processes.
• Optimise production parameters to reduce defects and improve yields.
• Integrate AI-driven yield management into existing production workflows.

This instructor-led, live training in Wellington (online or on-site) is aimed at intermediate-level business and AI professionals who wish to apply machine learning in business, forecasting, and AI-driven systems using real-world case studies and Python-based tools.

By the end of this training, participants will be able to:

• Understand how machine learning fits within AI and business strategy.
• Apply supervised and unsupervised learning techniques to structured business problems.
• Preprocess and transform data for modelling.
• Use neural networks for classification and prediction tasks.
• Perform sales forecasting using statistical and machine learning-based methods.
• Implement clustering and association rule mining for customer segmentation and pattern discovery.

This instructor-led, live training in Wellington (online or on-site) is designed for intermediate-level professionals who wish to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to improve production efficiency and reduce unexpected equipment failures.

By the end of this training, participants will be able to:

• Implement AI models to predict equipment failures in semiconductor manufacturing.
• Analyse maintenance data to identify patterns and trends that signal potential issues.
• Integrate AI-driven predictive maintenance into existing manufacturing workflows.
• Reduce downtime and maintenance costs through proactive equipment management.

This instructor-led, live training in Wellington (available online or on-site) is designed for advanced-level professionals who wish to apply cutting-edge AI techniques to semiconductor design automation, thereby improving efficiency, accuracy, and innovation in chip design and verification.

By the end of this training, participants will be able to:

• Apply advanced AI techniques to optimise semiconductor design processes.
• Integrate machine learning models into EDA tools for enhanced design verification.
• Develop AI-driven solutions for complex design challenges in chip fabrication.
• Leverage neural networks to improve the accuracy and speed of design automation.

This instructor-led, live training in Wellington (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilise advanced features of TensorFlow for deep learning.

This instructor-led, live training in Wellington (online or on-site) is designed for intermediate-level professionals seeking to understand and apply AI techniques to optimise semiconductor fabrication processes.

By the end of this training, participants will be able to:

• Understand AI methodologies for process optimisation in chip fabrication.
• Implement AI models to improve yield and reduce defects.
• Analyse process data to identify key parameters for optimisation.
• Apply machine learning techniques to fine-tune semiconductor manufacturing processes.

This instructor-led, live training in Wellington (online or on-site) is designed for intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

By the end of this training, participants will be able to:

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimise machine learning workflows in production.

This instructor-led, live training in Wellington (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently using the Google Colab environment.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab for machine learning projects.
• Understand and apply various machine learning algorithms.
• Use libraries like Scikit-learn to analyse and predict data.
• Implement supervised and unsupervised learning models.
• Optimise and evaluate machine learning models effectively.

TinyML is the practice of deploying machine learning models on highly resource-constrained hardware.

This instructor-led, live training (online or on-site) is aimed at advanced-level practitioners who wish to optimise TinyML models for low-latency, memory-efficient deployment on embedded devices.

Upon completing this training, participants will be able to:

• Apply quantisation, pruning, and compression techniques to reduce model size without sacrificing accuracy.
• Benchmark TinyML models for latency, memory consumption, and energy efficiency.
• Implement optimised inference pipelines on microcontrollers and edge devices.
• Evaluate trade-offs between performance, accuracy, and hardware constraints.

Format of the Course

• Instructor-led presentations supported by technical demonstrations.
• Practical optimisation exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines in a controlled lab environment.

Course Customisation Options

• For tailored training aligned with specific hardware platforms or internal workflows, please contact us to customise the program.

This instructor-led, live training in Wellington (online or on-site) is designed for advanced-level professionals seeking to explore state-of-the-art XAI techniques for deep learning models, with a particular focus on building interpretable AI systems.

By the end of this training, participants will be able to:

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.

This instructor-led, live training in Wellington (online or on-site) is designed for beginner-level professionals who want to understand and apply AI technologies within the semiconductor manufacturing industry.

By the end of this training, participants will be able to:

• Understand the core principles of AI and how they apply to semiconductor manufacturing.
• Identify areas within semiconductor manufacturing where AI can be effectively deployed.
• Use AI tools and techniques to improve production efficiency and quality control.
• Implement basic AI models to optimise manufacturing processes.

Docker is a containerisation platform used to build reproducible, portable, and scalable environments for ML systems.

This instructor-led, live training (online or on-site) is designed for intermediate to advanced-level technical professionals who wish to containerise and operationalise complete ML pipelines using Docker.

Upon completion of this training, participants will be able to:

• Containerise ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines using Docker and supporting tools.
• Implement versioning, reproducibility, and CI/CD for ML components.
• Deploy, monitor, and scale ML services in containerised environments.

Course Format

• Interactive lectures supported by practical demonstrations.
• Hands-on exercises focused on building real ML pipeline components.
• Live-lab implementation for end-to-end containerised workflows.

Course Customisation Options

• For customised training aligned with specific ML infrastructure needs, please contact us to discuss options.

This instructor-led, live training in Wellington (available online or on-site) is designed for data scientists and developers who wish to leverage ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.

By the end of this training, participants will be able to:

• Install ML.NET and integrate it into the application development environment.
• Understand the machine learning principles underpinning ML.NET tools and algorithms.
• Build and train machine learning models to make intelligent predictions using provided data.
• Evaluate the performance of a machine learning model using ML.NET metrics.
• Optimise the accuracy of existing machine learning models based on the ML.NET framework.
• Apply ML.NET machine learning concepts to other data science applications.

This instructor-led, live training in Wellington (online or on-site) is designed for intermediate-level data professionals who wish to apply machine learning techniques to data-driven business challenges, including sales forecasting and predictive modelling using neural networks.

By the end of this training, participants will be able to:

• Understand the core concepts and types of machine learning.
• Apply key algorithms for classification, regression, clustering, and association analysis.
• Perform exploratory data analysis and data preparation using Python.
• Use neural networks for non-linear modelling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimise model performance using visual and statistical techniques.

This instructor-led, live training in Wellington (delivered online or on-site) is designed for intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to deepen their expertise in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimisation strategies for high-quality image synthesis.
• Optimise performance and scalability for large datasets and complex models.
• Tune hyperparameters to improve model performance and generalisation.
• Integrate Stable Diffusion with other deep learning frameworks and tools.

This instructor-led, live training in Wellington (online or onsite) is aimed at intermediate-level to advanced-level cybersecurity professionals who wish to elevate their skills in AI-driven threat detection and incident response.

By the end of this training, participants will be able to:

• Implement advanced AI algorithms for real-time threat detection.
• Customise AI models for specific cybersecurity challenges.
• Develop automation workflows for threat response.
• Secure AI-driven security tools against adversarial attacks.

This instructor-led, live training in Wellington (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and its advantages for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimise, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• Optimise AI models for power efficiency and memory constraints.

This instructor-led, live training in Wellington (online or onsite) is aimed at beginner-level cybersecurity professionals who wish to learn how to leverage AI for improved threat detection and response capabilities.

By the end of this training, participants will be able to:

• Understand AI applications in cybersecurity.
• Implement AI algorithms for threat detection.
• Automate incident response with AI tools.
• Integrate AI into existing cybersecurity infrastructure.

This instructor-led, live training in Wellington (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

This instructor-led, live training in Wellington (online or on-site) is designed for intermediate-level data analysts who want to learn how to use RapidMiner to estimate and forecast values, and to leverage analytical tools for time series forecasting.

By the end of this training, participants will be able to:

• Apply the CRISP-DM methodology, select appropriate machine learning algorithms, and improve model construction and performance.
• Use RapidMiner to estimate and forecast values, and utilise analytical tools for time series forecasting.

This instructor-led, live training (online or on-site) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimise the performance and stability of Stable Diffusion models.

This instructor-led, live training in Wellington (available online or on-site) is designed for beginner-level engineers and data scientists who wish to understand the fundamentals of TinyML, explore its practical applications, and deploy AI models on microcontrollers.

By the end of this training, participants will be able to:

• Grasp the core principles of TinyML and understand its significance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimise and fine-tune machine learning models for low-power consumption.
• Apply TinyML to real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

The aim of this course is to equip participants with practical proficiency in applying Machine Learning methods. Using the Python programming language and its diverse libraries, and drawing on numerous real-world examples, the course covers the core components of Machine Learning, guiding learners through data modelling decisions, interpretation of algorithm outputs, and validation of results.

Our goal is to empower you with the confidence to understand and apply the most essential tools from the Machine Learning toolbox, while helping you avoid common pitfalls encountered in Data Science applications.

Applied AI from Scratch in Python equips programmers and data analysts with foundational techniques for building machine learning solutions from the ground up using Python. Covers core principles of supervised learning classification and regression, unsupervised learning clustering and anomaly detection, and advanced neural network architectures. Examines proven methods for working with scikit-learn, Apache Spark MLlib, and Jupyter notebooks for hands-on AI development. Helps professionals implement practical ML models, evaluate algorithm limitations, and complete applied projects for real-world problem solving.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions through interaction with their environments. It underpins many modern AI advancements, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables an artificial agent to learn strategies, optimise policies, and make autonomous decisions based on trial and error using reward-based learning.

This instructor-led, live training (available online or on-site) is designed for intermediate-level developers and data scientists who wish to learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in complex environments.

By the end of this training, participants will be able to:

• Understand the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement key RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Build and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to real-world applications such as games, robotics, and decision optimisation.
• Troubleshoot, visualise, and optimise training performance using modern tools.

Format of the Course

• Interactive lectures and guided discussions.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Course Customisation Options

• To request a customised version of this course (for example, using PyTorch instead of TensorFlow), please contact us to arrange.

A deep dive into the fundamentals of artificial intelligence illustrates how intelligent technology is transforming digital strategy, automation, and decision-making processes within enterprise operations. This course examines core concepts, including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside areas such as communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and integrate practical intelligent solutions to accelerate business agility.

This course covers AI (emphasising Machine Learning and Deep Learning) in the automotive industry. It helps determine which technologies can potentially be used in a variety of automotive scenarios—from simple automation and image recognition to autonomous decision-making.

This 8-day programme offers a comprehensive journey from solid Python engineering foundations to advanced AI system design. Participants will develop disciplined coding practices, master statistical and deep learning methods, and build production-ready generative AI and agent-based systems. The emphasis is on reliability, evaluation, safety, and real-world deployment rather than experimentation alone.

An Artificial Neural Network is a computational data model employed in the development of Artificial Intelligence (AI) systems capable of performing “intelligent” tasks. Neural Networks are widely used in Machine Learning (ML) applications, which represent one implementation of AI. Deep Learning is a specialised subset of ML.

Enhance your data science capabilities with this thorough Machine Learning training course. The curriculum covers essential algorithms such as Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques. You will acquire practical experience by combining theoretical foundations with real-world applications. This course is ideal for data analysts, software engineers, AI enthusiasts, and business professionals aiming to implement machine learning solutions. You will master classification performance metrics, cross-validation, the bias-variance trade-off, and deep learning fundamentals to construct robust predictive models.

This instructor-led, live training in Wellington (online or onsite) introduces participants to the field of pattern recognition and machine learning. It explores practical applications across statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

By the end of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Use key models such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to tackle complex problem-solving challenges.
• Improve prediction accuracy by combining different models.

This instructor-led, live training in Wellington (available online or on-site) is designed for data scientists who wish to leverage TensorFlow to analyse potential fraud data.

By the end of this training, participants will be able to:

• Develop a fraud detection model using Python and TensorFlow.
• Construct linear regressions and linear regression models to predict fraudulent activity.
• Build an end-to-end AI application for analysing fraud data.

Machine learning is a branch of Artificial Intelligence where computers learn without being explicitly programmed.

Deep learning is a subfield of machine learning that employs methods based on learning data representations and structures, such as neural networks.

Python is a high-level programming language renowned for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for the telecommunications sector using Python, guided through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning.
• Explore the applications and uses of deep learning in telecommunications.
• Use Python, Keras, and TensorFlow to build deep learning models for the telecommunications industry.
• Develop their own deep learning customer churn prediction model using Python.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised training session for this course, please contact us to arrange.

This hands-on, instructor-led programme serves as a logical follow-up to the Python for Data Analysis course.

It introduces participants to the foundational concepts of Machine Learning and demonstrates how to apply these directly to data analysis tasks, including prediction, classification, and segmentation.

The emphasis is on grasping the practical application of Machine Learning using familiar tools like Python, Pandas, and Jupyter Notebook, without the need for an advanced mathematical background.

This instructor-led, live training in Wellington (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organisation.

By the end of this training, participants will be able to:

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
• Prepare, validate and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalise the entire Machine Learning process so that it's reproducible and maintainable.

This instructor-led, live training in Wellington (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

By the end of this course, participants will be able to:

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.

This instructor-led, live training in Wellington (online or on-site) is designed for developers and data scientists who wish to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and more.

By the end of this training, participants will be able to:

• Install and configure TensorFlow 2.x.
• Understand the advantages of TensorFlow 2.x over earlier versions.
• Build deep learning models.
• Implement advanced image classifiers.
• Deploy deep learning models to the cloud, mobile devices, and IoT platforms.

This course begins by providing you with conceptual knowledge of neural networks, machine learning algorithms, and deep learning (including algorithms and applications).

Part 1 (40%) of this training focuses on fundamentals while helping you select the right technology, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part 2 (20%) introduces Theano—a Python library that simplifies writing deep learning models.

Part 3 (40%) is extensively based on TensorFlow, the API for Google's open-source software library for Deep Learning. All examples and hands-on activities will be conducted using TensorFlow.

Audience

This course is designed for engineers seeking to use TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will:

• have a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow's structure and deployment mechanisms
• be able to carry out installation, production environment, and architecture tasks and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production tasks such as training models, building graphs, and logging