#TensorFlow Extended (TFX)

[surajheroblog]

TensorFlow Mastery: Build Cutting-Edge AI Models

In the realm of artificial intelligence and machine learning, TensorFlow stands out as one of the most powerful and widely-used frameworks. Developed by Google, TensorFlow provides a comprehensive ecosystem for building and deploying machine learning models. For those looking to master this technology, a well-structured TensorFlow course for deep learning can be a game-changer. In this blog post, we will explore the benefits of mastering TensorFlow, the key components of a TensorFlow course for deep learning, and how it can help you build cutting-edge AI models. Whether you are a beginner or an experienced practitioner, this guide will provide valuable insights into the world of TensorFlow.

1. Understanding TensorFlow

1.1 What is TensorFlow?

TensorFlow is an open-source machine learning framework that allows developers to build and deploy machine learning models with ease. It provides a flexible and comprehensive ecosystem that includes tools, libraries, and community resources. TensorFlow supports a wide range of tasks, from simple linear regression to complex deep learning models. This versatility makes it an essential tool for anyone looking to delve into the world of AI.

1.2 Why Choose TensorFlow?

There are several reasons why TensorFlow is a popular choice among data scientists and AI practitioners. Firstly, it offers a high level of flexibility, allowing users to build custom models tailored to their specific needs. Secondly, TensorFlow’s extensive documentation and community support make it accessible to both beginners and experts. Lastly, TensorFlow’s integration with other Google products, such as TensorFlow Extended (TFX) and TensorFlow Lite, provides a seamless workflow for deploying models in production environments.

2. Key Components of a TensorFlow Course for Deep Learning

2.1 Introduction to Deep Learning

A comprehensive TensorFlow course for deep learning typically begins with an introduction to deep learning concepts. This includes understanding neural networks, activation functions, and the basics of forward and backward propagation. By grasping these foundational concepts, learners can build a solid base for more advanced topics.

2.2 Building Neural Networks with TensorFlow

The next step in a TensorFlow course for deep learning is learning how to build neural networks using TensorFlow. This involves understanding TensorFlow’s core components, such as tensors, operations, and computational graphs. Learners will also explore how to create and train neural networks using TensorFlow’s high-level APIs, such as Keras.

2.3 Advanced Deep Learning Techniques

As learners progress through the TensorFlow course for deep learning, they will encounter more advanced techniques. This includes topics such as convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for sequence data, and generative adversarial networks (GANs) for generating new data. These advanced techniques enable learners to tackle complex AI challenges and build cutting-edge models.

2.4 Model Optimization and Deployment

A crucial aspect of any TensorFlow course for deep learning is learning how to optimize and deploy models. This includes techniques for hyperparameter tuning, regularization, and model evaluation. Additionally, learners will explore how to deploy models using TensorFlow Serving, TensorFlow Lite, and TensorFlow.js. These deployment tools ensure that models can be efficiently integrated into real-world applications.

3. Practical Applications of TensorFlow

3.1 Computer Vision

One of the most popular applications of TensorFlow is in the field of computer vision. By leveraging TensorFlow’s powerful libraries, developers can build models for image classification, object detection, and image segmentation. A TensorFlow course for deep learning will typically include hands-on projects that allow learners to apply these techniques to real-world datasets.

3.2 Natural Language Processing

Another key application of TensorFlow is in natural language processing (NLP). TensorFlow provides tools for building models that can understand and generate human language. This includes tasks such as sentiment analysis, language translation, and text generation. By mastering TensorFlow, learners can develop sophisticated NLP models that can be used in various applications, from chatbots to language translation services.

3.3 Reinforcement Learning

Reinforcement learning is a branch of machine learning that focuses on training agents to make decisions by interacting with their environment. TensorFlow provides a robust framework for building and training reinforcement learning models. A TensorFlow course for deep learning will often cover the basics of reinforcement learning and provide practical examples of how to implement these models using TensorFlow.

4. Benefits of Mastering TensorFlow

4.1 Career Advancement

Mastering TensorFlow can significantly enhance your career prospects. As one of the most widely-used machine learning frameworks, TensorFlow skills are in high demand across various industries. By completing a TensorFlow course for deep learning, you can demonstrate your expertise and open up new career opportunities in AI and machine learning.

4.2 Personal Growth

Beyond career advancement, mastering TensorFlow offers personal growth and intellectual satisfaction. The ability to build and deploy cutting-edge AI models allows you to tackle complex problems and contribute to innovative solutions. Whether you are working on personal projects or collaborating with a team, TensorFlow provides the tools and resources needed to bring your ideas to life.

4.3 Community and Support

One of the key benefits of learning TensorFlow is the vibrant community and support network. TensorFlow’s extensive documentation, tutorials, and community forums provide valuable resources for learners at all levels. By engaging with the TensorFlow community, you can gain insights, share knowledge, and collaborate with other AI enthusiasts.

Conclusion

In conclusion, mastering TensorFlow through a well-structured TensorFlow course for deep learning can open up a world of possibilities in the field of artificial intelligence. From understanding the basics of neural networks to building and deploying advanced models, a comprehensive course provides the knowledge and skills needed to excel in AI. This deep dive into TensorFlow not only enhances your career prospects but also offers personal growth and intellectual satisfaction.

[devibask]

Revolutionizing Industries: The Latest in AI Technology

Artificial Intelligence (AI) is like teaching computers to do smart things that humans usually do. It helps them understand language, spot patterns, learn from experience, and make decisions. The big goal of Artificial Intelligence is to make machines as smart as humans so they can solve tough problems, predict, adapt to new situations, and get better over time.

Artificial Intelligence has become a big part of our lives. We see it in things like Siri, Alexa, and recommendation systems on Netflix. It's also behind self-driving cars, medical tools, and smart home devices, changing how we live and work. AI takes part in all the industry.

AI is combined with different parts like machine learning, natural language processing, and robotics. Machine learning is especially important because it helps systems learn from data without us having to tell them exactly what to do.

Benefits of AI Tools for Individuals and Businesses

For businesses, AI is super helpful. It can automate tasks, analyze lots of data quickly, and give insights into what customers like. As a marketer, it's important to understand how AI can help your business stand out.

One big way AI helps marketing is by personalizing the customer experience. By looking at customer data, AI can create personalized content, like product suggestions, for each person. This can make customers more engaged and loyal, which means more money for your business. It saves a lot of time for the customer. While using this they feel comfortable.

AI can also make marketing campaigns better by figuring out the best time and way to send messages to customers. By studying customer data, AI can help you reach the right people at the right time, making it more likely they'll buy what you're selling.

Understanding the basics of AI is important today because it's changing so fast. But what's making AI amazing are the special tools and systems that help people who work with AI do their jobs better. Here are some of these AI tools:

PyTorch: It's like a toolkit from Facebook that makes building and using AI easier.

TensorFlow: Made by Google, it's great for building AI that works on all kinds of devices.

Scikit-learn: This is a handy tool for working with AI in Python.

TensorFlow Extended (TFX): It's like a big helper that makes sure AI programs run smoothly.

Hugging Face Transformers: This tool is super helpful for working with words and text in AI.

Ray: Ray makes it easier to work with big AI projects by getting lots of computers to work together.

ONNX (Open Neural Network Exchange): ONNX helps different AI tools talk to each other, making it easier to use AI in different places.

These tools are like superpowers for people working with AI, helping them make AI smarter and more useful in our lives. And as AI keeps growing, these tools will keep getting better, helping us solve even bigger problems in the future.

[mrkhan75090]

Exploring the Top 10 AI Development Software in 2024

What is AI Development Software?

AI development software refers to a category of tools, frameworks, platforms, and programming environments specifically designed to facilitate the creation, training, testing, and deployment of artificial intelligence (AI) models and applications. This software is used by developers, data scientists, researchers, and engineers to build AI systems that can perform tasks traditionally requiring human intelligence, such as understanding natural language, recognizing patterns in data, making predictions, and automating decision-making processes.

1. TensorFlow:

TensorFlow remains a cornerstone in the AI development landscape. Developed by Google, TensorFlow offers a comprehensive framework for building machine learning models across a variety of platforms. Its flexibility, scalability, and extensive community support make it a preferred choice for developers worldwide. TensorFlow’s latest versions integrate advanced features such as TensorFlow Extended (TFX) for end-to-end ML pipeline development and TensorFlow Lite for deploying models on mobile and IoT devices.

2. PyTorch:

PyTorch has emerged as a formidable competitor to TensorFlow, renowned for its dynamic computational graph and intuitive interface. Developed by Facebook’s AI Research lab (FAIR), PyTorch provides a seamless experience for prototyping, experimentation, and deployment of deep learning models. Its imperative programming paradigm enables developers to define and modify models on-the-fly, fostering rapid iteration and innovation. PyTorch’s strong integration with Python ecosystem and support for GPU acceleration make it a preferred choice for researchers and practitioners alike.

3. Microsoft Azure Machine Learning:

Microsoft Azure Machine Learning stands out as a comprehensive platform for end-to-end AI development

, offering a suite of tools and services for data preparation, model training, and deployment. With seamless integration with Azure cloud infrastructure, Azure Machine Learning empowers developers to scale AI projects efficiently while leveraging Microsoft’s robust security and compliance features. Its automated machine learning capabilities simplify model selection and hyperparameter tuning, enabling developers to focus on problem-solving rather than infrastructure management. Additionally, Azure Machine Learning’s support for a wide range of programming languages and frameworks enhances its appeal to diverse developer communities.

4. IBM Watson Studio:

IBM Watson Studio is a feature-rich AI development platform that caters to both novice and experienced developers. With its drag-and-drop interface and visual modeling tools, Watson Studio streamlines the process of building and deploying AI applications. Leveraging IBM’s extensive suite of AI services, developers can access pre-trained models, natural language processing (NLP) capabilities, and computer vision algorithms to accelerate development cycles. Watson Studio’s collaboration features and version control support foster teamwork and facilitate knowledge sharing among development teams.

5. Amazon SageMaker:

Amazon SageMaker remains a frontrunner in cloud-based AI development platforms, offering a scalable and cost-effective solution for building, training, and deploying machine learning models. Integrated with Amazon Web Services (AWS), SageMaker provides a unified environment for data scientists and developers to experiment with diverse algorithms and frameworks. Its managed services for data labeling, model training, and inference orchestration streamline the end-to-end machine learning workflow. With support for reinforcement learning and distributed training, SageMaker caters to complex AI applications across industries.

6. H2O.ai:

H2O.ai stands out as an open-source AI platform that empowers organizations to democratize AI across their operations. With its suite of machine learning algorithms and automated feature engineering capabilities, H2O.ai simplifies the process of building predictive models from structured data. The platform’s AutoML functionality automates model selection and hyperparameter optimization, enabling users to derive insights from data rapidly. H2O.ai’s scalable architecture and support for integration with popular programming languages make it a versatile choice for AI development teams.

7. :

Google Cloud AI Platform offers a suite of tools and services designed to accelerate the development and deployment of AI solutions on Google Cloud infrastructure. From data preprocessing and model training to deployment and monitoring, the platform provides end-to-end support for machine learning workflows. Google Cloud AI Platform’s robust security features, including encryption and access controls, ensure data privacy and compliance with regulatory requirements. With built-in support for TensorFlow and other popular ML frameworks, developers can leverage Google Cloud’s scalable infrastructure to tackle complex AI challenges efficiently.

8. NVIDIA Clara:

NVIDIA Clara is a specialized AI platform designed for healthcare and life sciences applications, catering to the unique requirements of medical imaging, genomics, and drug discovery. Powered by NVIDIA’s high-performance GPUs and deep learning frameworks, Clara offers state-of-the-art algorithms for medical image analysis, segmentation, and classification. Its federated learning capabilities enable collaboration and knowledge sharing while preserving data privacy and security. NVIDIA Clara’s extensible architecture allows developers to integrate custom algorithms and workflows tailored to specific healthcare use cases, accelerating innovation in the medical field.

9. DataRobot:

DataRobot is an automated machine learning platform that empowers organizations to build, deploy, and manage predictive models at scale. By automating the end-to-end machine learning pipeline, DataRobot enables users to derive insights from data quickly and efficiently. Its intuitive interface and guided workflows make machine learning accessible to users with varying levels of expertise, from business analysts to data scientists. DataRobot’s extensive library of algorithms and model interpretability features enhance transparency and trust in AI-driven decision-making processes.

10. Salesforce Einstein:

Salesforce Einstein is an AI-powered platform that integrates seamlessly with Salesforce’s suite of customer relationship management (CRM) solutions. By leveraging machine learning and natural language processing, Einstein enables organizations to personalize customer interactions, automate routine tasks, and uncover valuable insights from data. Its predictive analytics capabilities empower sales and marketing teams to make data-driven decisions and optimize customer engagement strategies. Salesforce Einstein’s scalable architecture and pre-built AI models streamline the integration of AI into business workflows, driving efficiency and innovation across industries.

AI Development Software benefit

AI Development Software in 2024

AI development software offers a multitude of benefits to developers, researchers, businesses, and society as a whole. Some of the key advantages of using AI development software include:

Accelerated Development Process: AI development software streamlines the process of building and deploying AI models and applications. By providing pre-built algorithms, libraries, and frameworks, developers can significantly reduce the time and effort required to implement complex AI solutions.

Accessibility and Democratization: AI development software makes AI technology more accessible to a wider audience, including individuals with varying levels of technical expertise. With user-friendly interfaces, intuitive APIs, and automated workflows, these tools empower users to experiment with AI algorithms and build innovative applications without extensive programming knowledge.

Enhanced Productivity: AI development software automates repetitive tasks and simplifies complex processes, allowing developers to focus on higher-level problem-solving and innovation. By providing built-in features for data preprocessing, model training, hyperparameter tuning, and model evaluation, these tools enable developers to iterate quickly and efficiently.

Scalability and Performance: Many AI development software solutions are designed to leverage distributed computing resources and specialized hardware accelerators, such as GPUs and TPUs, to scale AI applications effectively. By harnessing the power of parallel processing and optimized algorithms, developers can train and deploy AI models at scale to handle large volumes of data and complex computations.

Versatility and Flexibility: AI development software supports a wide range of use cases and applications across diverse domains, including natural language processing, computer vision, speech recognition, recommendation systems, and autonomous systems. With support for various programming languages, frameworks, and libraries, developers can choose the tools that best suit their project requirements and preferences.

Innovation and Discovery: AI development software fuels innovation and drives advancements in AI research and technology. By providing access to cutting-edge algorithms, state-of-the-art models, and large-scale datasets, these tools enable researchers and developers to explore new avenues of inquiry, discover novel solutions to complex problems, and push the boundaries of AI capabilities.

Business Value and Competitive Advantage: For businesses and organizations, AI development software offers opportunities to gain insights from data, automate business processes, improve decision-making, enhance customer experiences, and drive operational efficiency. By integrating AI capabilities into their products and services, companies can stay competitive in the digital marketplace and unlock new revenue streams.

Ethical and Responsible AI Development: AI development software promotes ethical and responsible practices in AI development by providing tools and frameworks for fairness, transparency, interpretability, and accountability. By incorporating principles of ethical AI into the design and deployment of AI systems, developers can mitigate biases, safeguard privacy, and ensure that AI technologies serve the best interests of individuals and society.

In summary, AI development software plays a crucial role in democratizing AI technology, accelerating innovation, and driving positive social and economic impact. By empowering developers and organizations to harness the power of artificial intelligence, these tools pave the way for a future where AI contributes to solving some of the world’s most pressing challenges and enriching the lives of people everywhere.

Conclusion: AI Development Software in 2024

In the rapidly evolving landscape of AI development, choosing the right tools and platforms is essential for unlocking the full potential of artificial intelligence. The top 10 AI development software highlighted in this article represent a diverse array of solutions tailored to meet the needs of developers, data scientists, and organizations across industries. Whether building machine learning models, deploying AI applications, or harnessing the power of data, these platforms empower users to innovate, collaborate, and drive meaningful impact in the world of artificial intelligence. As AI continues to advance, the role of AI development software will remain pivotal in shaping the future of technology and driving transformative change across society.

[testrigtechnologies]

Top 5 AI/ML Testing Tools for Streamlining Development and Deployment

As artificial intelligence (AI) and machine learning (ML) applications continue to proliferate across industries, ensuring the quality and reliability of these systems becomes paramount. Testing AI/ML models presents unique challenges due to their complexity and non-deterministic nature. To address these challenges, a range of specialized testing tools have emerged. In this article, we'll explore five top AI/ML testing tools that streamline the development and deployment process.

TensorFlow Extended (TFX): TensorFlow Extended (TFX) is an end-to-end platform for deploying production-ready ML pipelines. It offers a comprehensive suite of tools for data validation, preprocessing, model training, evaluation, and serving. TFX integrates seamlessly with TensorFlow, Google's popular open-source ML framework, making it an ideal choice for organizations leveraging TensorFlow for their AI projects. TFX's standardized components ensure consistency and reliability throughout the ML lifecycle, from experimentation to deployment.

PyTorch Lightning: PyTorch Lightning is a lightweight PyTorch wrapper that simplifies the training and deployment of complex neural networks. It provides a high-level interface for organizing code, handling distributed training, and integrating with popular experiment tracking platforms like TensorBoard and Weights & Biases. PyTorch Lightning automates many aspects of the training loop, allowing researchers and developers to focus on model design and experimentation while ensuring reproducibility and scalability.

MLflow: MLflow is an open-source platform for managing the end-to-end ML lifecycle. Developed by Databricks, MLflow provides tools for tracking experiments, packaging code into reproducible runs, and deploying models to production. Its flexible architecture supports integration with popular ML frameworks like TensorFlow, PyTorch, and scikit-learn, as well as cloud platforms such as AWS, Azure, and Google Cloud. MLflow's unified interface simplifies collaboration between data scientists, engineers, and DevOps teams, enabling faster iteration and deployment of ML models.

Seldon Core: Seldon Core is an open-source platform for deploying and scaling ML models in Kubernetes environments. It offers a range of features for model serving, monitoring, and scaling, including support for A/B testing, canary deployments, and multi-armed bandit strategies. Seldon Core integrates with popular ML frameworks like TensorFlow, PyTorch, and XGBoost, as well as cloud-based platforms such as AWS S3 and Google Cloud Storage. Its built-in metrics and logging capabilities enable real-time monitoring and performance optimization of deployed models.

ModelOp Center: ModelOp Center is an enterprise-grade platform for managing and monitoring AI/ML models in production. It provides a centralized hub for deploying, versioning, and governing models across heterogeneous environments, including on-premises data centers and cloud infrastructure. ModelOp Center's advanced features include model lineage tracking, regulatory compliance reporting, and automated drift detection, helping organizations ensure the reliability, security, and scalability of their AI/ML deployments.

Conclusion

Testing AI/ML models is essential for ensuring their reliability, scalability, and performance in production environments. The tools mentioned in this article provide comprehensive solutions for streamlining the development and deployment of AI/ML applications, from data preprocessing and model training to monitoring and optimization.

By leveraging these tools, organizations can accelerate their AI initiatives while minimizing risks and maximizing the value of their machine learning investments.

Need to ensure the reliability and performance of your AI/ML models? Explore Testrig Technologies AI/ML Testing Services for comprehensive validation and optimization, ensuring robustness and scalability in production environments.

[datascienceassignmenthelp]

key python packages for data science

7 most important Python libraries for data science.

1) TensorFlow

TensorFlow is a comprehensive free, open source platform for machine learning that includes a wide range of tools, libraries, and resources. It was first released by the Google Brain team on November 9, 2015. TensorFlow makes it easy to design and train machine learning models using high-level APIs like Keras. It also offers different levels of abstraction, allowing you to choose the best approach for your model. TensorFlow allows you to deploy machine learning models across multiple environments, including the cloud, browsers, and your device. If you want the full experience, choose TensorFlow Extended (TFX); TensorFlow Lite if you are using TensorFlow on a mobile device; and TensorFlow.js if you are going to train and deploy models in JavaScript contexts.

2) NumPy

NumPy stands for Numerical Python. It is a Python library for numerical calculations and scientific calculations. NumPy provides numerous high performance features that Python enthusiasts and programmers can use to work with arrays. NumPy arrays provide a vectorization of mathematical operations. These vectorized operations provide a performance boost over Python's loop constructs.

3) SciPy

SciPy-codenamed Scientific Python, is a variety of mathematical functions and algorithms built on Python's NumPy extension. SciPy provides many high-level commands and classes for manipulating and displaying data. SciPy is useful for exploratory data analysis and data processing systems.

4) Pandas

We can all do data analysis with pencil and paper on small data sets. We need specialized tools and techniques to analyze and derive meaningful insights from large data sets. Pandas Python is one such data analysis library with tools for high-level data structures and easy data manipulation. The ability to index, retrieve, split, join, restructure, and perform various other analyzes on multidimensional and single-dimensional data is essential to provide a simple yet effective way to analyze data.

5)  PyCaret

PyCaret is a fully accessible machine learning package for model deployment and data processing. It allows you to save time as it is a low code library. It's an easy-to-use machine learning library that will help you run end-to-end machine learning tests, whether you're trying to impute missing values, analyze discrete data, design functions, tune hyperparameters, or build coupled models.

[mystryofmachinelearning]

Google launches Cloud AI Platform pipelines in beta to simplify machine learning development

Google today announced the beta launch of Cloud AI Platform pipelines, a service designed to deploy robust, repeatable AI pipelines along with monitoring, auditing, version tracking, and reproducibility in the cloud. Google’s pitching it as a way to deliver an “easy to install” secure execution environment for machine learning workflows, which could reduce the amount of time enterprises spend bringing products to production.

“When you’re just prototyping a machine learning model in a notebook, it can seem fairly straightforward. But when you need to start paying attention to the other pieces required to make a [machine learning] workflow sustainable and scalable, things become more complex,” wrote Google product manager Anusha Ramesh and staff developer advocate Amy Unruh in a blog post. “A machine learning workflow can involve many steps with dependencies on each other, from data preparation and analysis, to training, to evaluation, to deployment, and more. It’s hard to compose and track these processes in an ad-hoc manner — for example, in a set of notebooks or scripts — and things like auditing and reproducibility become increasingly problematic.”

AI Platform Pipelines has two major parts: (1) the infrastructure for deploying and running structured AI workflows that are integrated with Google Cloud Platform services and (2) the pipeline tools for building, debugging, and sharing pipelines and components. The service runs on a Google Kubernetes cluster that’s automatically created as a part of the installation process, and it’s accessible via the Cloud AI Platform dashboard. With AI Platform Pipelines, developers specify a pipeline using the Kubeflow Pipelines software development kit (SDK), or by customizing the TensorFlow Extended (TFX) Pipeline template with the TFX SDK. This SDK compiles the pipeline and submits it to the Pipelines REST API server, which stores and schedules the pipeline for execution.

AI Pipelines uses the open source Argo workflow engine to run the pipeline and has additional microservices to record metadata, handle components IO, and schedule pipeline runs. Pipeline steps are executed as individual isolated pods in a cluster and each component can leverage Google Cloud services such as Dataflow, AI Platform Training and Prediction, BigQuery, and others. Meanwhile, the pipelines can contain steps that perform graphics card and tensor processing unit computation in the cluster, directly leveraging features like autoscaling and node auto-provisioning.

AI Platform Pipeline runs include automatic metadata tracking using ML Metadata, a library for recording and retrieving metadata associated with machine learning developer and data scientist workflows. Automatic metadata tracking logs the artifacts used in each pipeline step, pipeline parameters, and the linkage across the input/output artifacts, as well as the pipeline steps that created and consumed them.

In addition, AI Platform Pipelines supports pipeline versioning, which allows developers to upload multiple versions of the same pipeline and group them in the UI, as well as automatic artifact and lineage tracking. Native artifact tracking enables the tracking of things like models, data statistics, model evaluation metrics, and many more. And lineage tracking shows the history and versions of your models, data, and more.

Google says that in the near future, AI Platform Pipelines will gain multi-user isolation, which will let each person accessing the Pipelines cluster control who can access their pipelines and other resources. Other forthcoming features include workload identity to support transparent access to Google Cloud Services; a UI-based setup of off-cluster storage of backend data, including metadata, server data, job history, and metrics; simpler cluster upgrades; and more templates for authoring workflows.

[holytheoristtastemaker]

by Steef-Jan Wiggers Follow In a recent blog post, Google announced the beta of Cloud AI Platform Pipelines, which provides users with a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility.  With Cloud AI Pipelines, Google can help organizations adopt the practice of Machine Learning Operations, also known as MLOps – a term for applying DevOps practices to help users automate, manage, and audit ML workflows. Typically, these practices involve data preparation and analysis, training, evaluation, deployment, and more.  Google product manager Anusha Ramesh and staff developer advocate Amy Unruh wrote in the blog post:  When you're just prototyping a machine learning (ML) model in a notebook, it can seem fairly straightforward. But when you need to start paying attention to the other pieces required to make an ML workflow sustainable and scalable, things become more complex. Moreover, when complexity grows, building a repeatable and auditable process becomes more laborious. Cloud AI Platform Pipelines - which runs on a Google Kubernetes Engine (GKE) Cluster and is accessible via the Cloud AI Platform dashboard – has two major parts:  The infrastructure for deploying and running structured AI workflows integrated with GCP services such as BigQuery, Dataflow, AI Platform Training and Serving, Cloud Functions, and The pipeline tools for building, debugging and sharing pipelines and components. With the Cloud AI Platform Pipelines users can specify a pipeline using either the Kubeflow Pipelines (KFP) software development kit (SDK) or by customizing the TensorFlow Extended (TFX) Pipeline template with the TFX SDK. The latter currently consists of libraries, components, and some binaries and it is up to the developer to pick the right level of abstraction for the task at hand. Furthermore, TFX SDK includes a library ML Metadata (MLMD) for recording and retrieving metadata associated with the workflows; this library can also run independently.  Google recommends using KPF SDK for fully custom pipelines or pipelines that use prebuilt KFP components, and TFX SDK and its templates for E2E ML Pipelines based on TensorFlow. Note that over time, Google stated in the blog post that these two SDK experiences would merge. The SDK, in the end, will compile the pipeline and submit it to the Pipelines REST API; the AI Pipelines REST API server stores and schedules the pipeline for execution. An open-source container-native workflow engine for orchestrating parallel jobs on Kubernetes called Argo runs the pipelines, which includes additional microservices to record metadata, handle components IO, and schedule pipeline runs. The Argo workflow engine executes each pipeline on individual isolated pods in a GKE cluster – allowing each pipeline component to leverage Google Cloud services such as Dataflow, AI Platform Training and Prediction, BigQuery, and others. Furthermore, pipelines can contain steps that perform sizeable GPU and TPU computation in the cluster, directly leveraging features like autoscaling and node auto-provisioning.   Source: https://cloud.google.com/blog/products/ai-machine-learning/introducing-cloud-ai-platform-pipelines AI Platform Pipeline runs include automatic metadata tracking using the MLMD - and logs the artifacts used in each pipeline step, pipeline parameters, and the linkage across the input/output artifacts, as well as the pipeline steps that created and consumed them. With Cloud AI Platform Pipelines, according to the blog post customers will get: Push-button installation via the Google Cloud Console Enterprise features for running ML workloads, including pipeline versioning, automatic metadata tracking of artifacts and executions, Cloud Logging, visualization tools, and more  Seamless integration with Google Cloud managed services like BigQuery, Dataflow, AI Platform Training and Serving, Cloud Functions, and many others  Many prebuilt pipeline components (pipeline steps) for ML workflows, with easy construction of your own custom components The support for Kubeflow will allow a straightforward migration to other cloud platforms, as a respondent on a Hacker News thread on Google AI Cloud Pipeline stated: Cloud AI Platform Pipelines appear to use Kubeflow Pipelines on the backend, which is open-source and runs on Kubernetes. The Kubeflow team has invested a lot of time on making it simple to deploy across a variety of public clouds, such as AWS, and Azure. If Google were to kill it, you could easily run it on any other hosted Kubernetes service. The release of AI Cloud Pipelines shows Google's further expansion of Machine Learning as a Service (MLaaS) portfolio - consisting of several other ML centric services such as Cloud AutoML, Kubeflow and AI Platform Prediction. The expansion is necessary to allow Google to further capitalize on the growing demand for ML-based cloud services in a market which analysts expect to reach USD 8.48 billion by 2025, and to compete with other large public cloud vendors such as Amazon offering similar services like SageMaker and Microsoft with Azure Machine Learning. Currently, Google plans to add more features for AI Cloud Pipelines. These features are: Easy cluster upgrades  More templates for authoring ML workflows More straightforward UI-based setup of off-cluster storage of backend data Workload identity, to support transparent access to GCP services, and  Multi-user isolation – allowing each person accessing the Pipelines cluster to control who can access their pipelines and other resources. Lastly, more information on Google's Cloud AI Pipeline is available in the getting started documentation.

http://damianfallon.blogspot.com/2020/03/google-announces-cloud-ai-platform.html

[tensorflowtutorial]

Meet @ClemensMewald, a Product Manager for TensorFlow Extended (TFX). He talks with @lmoroney about how TFX helps developers deploy ML models in production, open source model analysis libraries and more. Watch here → https://t.co/ycqVgE5J2G https://t.co/G0veIwk1YO

[tensorflow4u]

Meet @ClemensMewald, a Product Manager for TensorFlow Extended (TFX). He talks with @lmoroney about how TFX helps developers deploy ML models in production, open source model analysis libraries and more. Watch here → https://t.co/ycqVgE5J2G https://t.co/G0veIwk1YO

[machinelearningmasteryindia]

Define and run Machine Learning pipelines on Step Functions using Python, Workflow Studio, or States Language

Define and run Machine Learning pipelines on Step Functions using Python, Workflow Studio, or States Language

You can use various tools to define and run machine learning (ML) pipelines or DAGs (Directed Acyclic Graphs). Some popular options include AWS Step Functions, Apache Airflow, KubeFlow Pipelines (KFP), TensorFlow Extended (TFX), Argo, Luigi, and Amazon SageMaker Pipelines. All these tools help you compose pipelines in various languages (JSON, YAML, Python, and more), followed by viewing and…

[saec-be]

Spotify a standardis sa plateforme de machine learning sur TFX et Kubeflow Pour tenir la cadence sur ses recommandations musicales, Spotify a bti une plateforme d'apprentissage machine standardise autour de Tensorflow Extended (TFX) et Kubeflow.

[pridesofblack]

Gmail's New Secret Feature Protects Your Computer Against Viruses

Google has essentially expanded security, with a component it added to Gmail a year ago that isn't even perceptible to clients. Gmail's new component makes innocuous Office documents that come notwithstanding the mail. For the security of PCs and information, it is prescribed not to open email connections from individuals who have not been known for a considerable length of time. Google figured out how to keep away from this issue with another profound learning calculation that it created. The new profound learning calculation created by Google for Gmail becomes effective when an extra record accompanies the email. The calculation handicaps the record before the malware in the Office archive joined to the email becomes an integral factor. With respect to's new calculation, Google likewise distributed a blog entry. "Our innovation is particularly useful in identifying assaults," says Google's blog entry. In cases, for example, assault, our new calculation expanded the identification rate by 150 percent. Our calculation utilizes an alternate TensorFlow profound learning model prepared with TFX (TensorFlow Extended) and a unique report examination for each document type. Report analyzers; answerable for parsing the record, recognizing normal assault designs, breaking content and investigating. " Gmail's calculation works with existing defensive measures. Right now, PC and email locations can be remained careful against digital assaults. Google clarified that noxious archives represented 58 percent of assaults focusing on Gmail clients, so this innovation against records is significant. The calculation is presently just taking a shot at Office records, not against other mail connections. Read the full article

[iamprogrammerz]

TensorFlow Extended (TFX): Machine Learning Pipelines ☞ https://morioh.com/p/62849c5921d9 #TensorFlow #MachineLearning #Morioh

[iamcodegeek]

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