Intermediate MLOps

This course reviews the mechanisms behind anthropogenic climate change and its impact on global temperatures and weather patterns. It includes two case studies: one using time series analysis for wind power forecasting and another using computer vision for biodiversity monitoring, demonstrating how AI techniques can help mitigate and adapt to climate change.

The first course in this specialization is an excellent entry point for practical computer vision. You will learn to build and deploy models in the browser using TensorFlow.js, including creating a real-time object detector that runs on a webcam feed.

This course covers key Industry 4.0 technologies including AI, machine learning, and big data analytics, and their applications in creating smart factories and improving production processes.

This course covers the end-to-end process of building and maintaining production ML systems. It includes modules on data needs and modeling strategies, which touch upon the importance of choosing the right data storage and handling evolving data, a key consideration when deciding between row, columnar, and vector-based storage.

This course focuses on the best practices and tools for deploying, evaluating, monitoring, and operating production machine learning systems on Google Cloud. It provides hands-on practice with Vertex AI Feature Store, including streaming ingestion at the SDK layer. The curriculum is designed to teach learners how to containerize ML workflows for reproducibility and scalability, and how to efficiently manage ML features.

This course explores new metrics and best practices to monitor your LLM systems and ensure safety and quality. You will learn to identify hallucinations, detect jailbreaks using sentiment analysis, identify data leakage, and build your own monitoring system.

An introductory course on the integration of AI in manufacturing to enhance production efficiency, reduce downtime, and improve product quality, covering predictive maintenance and quality control.

This course explores the transformative impact of AI on the fashion industry. It covers AI-driven fashion design, trend forecasting, manufacturing, and retail. Students will learn how AI is used to create innovative designs, predict future trends using big data, and improve efficiency in clothing production.

Learn how to detect and respond to anomalies using Metricbeat and the Elastic Stack for enhanced enterprise monitoring.

A course on Pluralsight that covers the end-to-end process of time series forecasting in Python, from data exploration to model deployment.

ML Ops with Weights & Biases

Machine Learning Engineering for Production (ML Ops)

This bootcamp on Udemy focuses on the operational aspects of deploying large language models. It covers CI/CD, Docker, Kubernetes, and monitoring for production LLM deployment, which are essential skills for managing and optimizing the cost and performance of LL Ms at scale.

Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. TensorFlow, an open source software library, is extensively used for numerical computation using data flow graphs.The flexible architecture allows you to deploy computation to one or more CP Us or GP Us in a desktop, server, or mobile device with a single API. So if you’re looking forward to acquiring knowledge on machine learning and deep learning with this powerful TensorFlow library, then go for this Learning Path. Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. The highlights of this Learning Path are: Setting up TensorFlow for actual industrial use, including high-performance setup aspects like multi-GPU support Embedded with solid projects and examples to teach you how to implement TensorFlow in production Empower you to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage Let's take a look at your learning journey. You will start by exploring unique features of the library such as data flow graphs, training, visualization of performance with Tensor Board – all within an example-rich context using problems from multiple industries. The focus is towards introducing new concepts through problems which are coded and solved over the course of each video. You will then learn how to implement TensorFlow in production. Each project in this Learning Path provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Finally, you will be acquainted with the different paradigms of performing deep learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more,

Bienvenido a este curso 100% práctico y aplicado en el que podrás aprender de forma intuitiva, guiada y paso-a-paso el despliegue de modelos Deep Learning para ambientes de Desarrollo y principalmente para Producción en escenarios de alto desempeño usando la librería TensorFlow 2.0 y la creación de servicios REST.Estructura temática:¿Por qué desplegar modelos Deep Learning?Despliegue en Desarrollo vs Producción Servidores de despliegue en la Nube: Google Cloud Platform (GCP) y CentOS Despliegue de modelos en la nube como Servicio Web REST desde cero (AP Is)Gestor de contenedores Docker para despliegues en Producción (Docker Swarm, TensorFlow Serving) Implementación de llamadas al Servicio Web desde cero Consideraciones técnicas para el despliegue de modelos Deep Learning Dev Ops y Machine Learning / ML Ops | IA Ops | XX Ops Interoperabilidad de modelos: ONNX.Despliegue Customizado vs Plataformas100% práctico:El curso prioriza el desarrollo de algoritmos en sesiones de laboratorio y actividades de programación 100% hands-on con los que podrás reproducir cada una de las líneas de código con explicaciones muy bien detalladas, sin descuidar los fundamentos teóricos de cada uno de los conceptos descritos.Herramientas:Todas las herramientas necesarias para el curso se podrán configurar directamente en la nube de Google; por tanto, no será necesario invertir tiempo en instalaciones de herramienta de forma local.El curso se desarrolla con las herramientas más populares y de alta madurez del ecosistema de Python 3.0 como:TensorFlow 2.0Tensor Flow Serving Flask

Have you been looking for a course that teaches you effective machine learning in Scikit-Learn and TensorFlow 2.0? Or have you always wanted an efficient and skilled working knowledge of how to solve problems that can't be explicitly programmed through the latest machine learning techniques?If you're familiar with pandas and Num Py, this course will give you up-to-date and detailed knowledge of all practical machine learning methods, which you can use to tackle most tasks that cannot easily be explicitly programmed; you'll also be able to use algorithms that learn and make predictions or decisions based on data.The theory will be underpinned with plenty of practical examples, and code example walk-throughs in Jupyter notebooks. The course aims to make you highly efficient at constructing algorithms and models that perform with the highest possible accuracy based on the success output or hypothesis you've defined for a given task.By the end of this course, you will be able to comfortably solve an array of industry-based machine learning problems by training, optimizing, and deploying models into production. Being able to do this effectively will allow you to create successful prediction and decisions for the task in hand (for example, creating an algorithm to read a labeled dataset of handwritten digits).About the Author Samuel Holt has several years' experience implementing, creating, and putting into production machine learning models for large blue-chip companies and small startups (as well as within his own companies) as a machine learning consultant.He has machine learning lab experience and holds an MEng in Machine Learning and Software Engineering from Oxford University, where he won four awards for academic excellence.Specifically, he has built systems that run in production using a combination of Scikit-Learn and TensorFlow involving automated customer support, implementing document OCR, detecting vehicles in the case of s

In this course you will learn how to deploy Machine Learning Deep Learning Models using various techniques.&nbsp; This course takes you beyond model development and explains how the model can be consumed by different applications with hands-on examples Course Structure:Creating a Classification Model using Scikit-Learn Saving the Model and the standard Scaler Exporting the Model to another environment - Local and Google Colab Creating a REST API using Python Flask and using it locally Creating a Machine Learning REST API on a Cloud virtual server Creating a Serverless Machine Learning REST API using Cloud Functions Building and Deploying TensorFlow and Keras models using TensorFlow Serving Building and Deploying&nbsp; PyTorch Models Converting a PyTorch model to TensorFlow format using ONNX Creating REST API for PyTorch and TensorFlow Models Deploying tf-idf and text classifier models for Twitter sentiment analysis Deploying models using TensorFlow.js and Java Script Tracking Model training experiments and deployment with MLF Low Running ML Flow on Colab and Databricks Appendix - Generative AI - Miscellaneous Topics.OpenAI and the history of GPT models Creating an OpenAI account and invoking a text-to-speech model from Python code Invoking OpenAI Chat Completion, Text Generation, Image Generation models from Python code Creating a Chatbot with OpenAI API and ChatGPT Model using Python on Google Colab ChatGPT, Large Language Models (LLM) and prompt engineering New Section : Agent-Mode Model Building and Deployment with Git Hub Copilot Vibe Coding: Model Development with Git Hub Copilot Using a Single Prompt<li

In this course I will cover, how to develop a Sentiment Analysis model to categorize a tweet as Positive or Negative using NLP techniques and Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model and finally deploying the same on Cloud platforms to let your customers interact with your model via an user interface.This course will walk you through the initial data exploration and understanding, data analysis, data pre-processing, data preparation, model building, evaluation and deployment techniques. We will explore NLP concepts and then use multiple ML algorithms to create our model and finally focus into one which performs the best on the given dataset.At the end we will learn to create an User Interface to interact with our created model and finally deploy the same on Cloud.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1&nbsp; :&nbsp; Installing Packages.Task 2&nbsp; :&nbsp; Importing Libraries.Task 3&nbsp; :&nbsp; Loading the data from source.Task 4&nbsp; :&nbsp; Understanding the data Task 5&nbsp; :&nbsp; Preparing the data for pre-processing Task 6&nbsp; :&nbsp; Pre-processing steps overview Task 7&nbsp; :&nbsp; Custom Pre-processing functions Task 8&nbsp; :&nbsp; About POS tagging and Lemmatization Task 9&nbsp; :&nbsp; POS tagging and lemmatization in action.Task 10 :&nbsp; Creating a word cloud of positive and negative tweets.Task 11 :&nbsp; Identifying the most frequent set of words in the dataset for positive and negative cases.Task 12 :&nbsp; Train Test Split Task 13 :&nbsp; About TF-IDF Vectorizer Task 14 :&nbsp; TF-IDF Vectorizer in action Task 15 :&nbsp; About Confusion Matrix Task 16 :&nbsp; About Classification Report Task 17 :&nbsp; About AUC-ROCT

Machine Learning Real value comes from actually deploying a machine learning solution into production and the necessary monitoring and optimization work that comes after it.Most of the problems nowadays as I have made a machine-learning model but what next.How it is available to the end-user, the answer is through API, but how it works?How you can understand where the Docker stands and how to monitor the build we created.This course has been designed to keep these areas under consideration. The combination of industry-standard build pipeline with some of the most common and important tools.This course has been designed into Following sections:1) Configure and a quick walkthrough of each of the tools and technologies we used in this course.2) Building our NLP Machine Learning model and tune the hyperparameters.3) Creating flask API and running the WebAPI in our Browser.4) Creating the Docker file, build our image and running our ML Model in Docker container.5) Configure Git Lab and push your code in Git Lab.6) Configure Jenkins and write Jenkins's file and run end-to-end Integration.This course is perfect for you to have a taste of industry-standard Data Science and deploying in the local server. Hope you enjoy the course as I enjoyed making it.