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Learn how to enhance the quality of your machine learning code using Scikit-learn Pipeline and ColumnTransformer.View the full article

The podcasts that would improve your ML knowledge.View the full article

Kubernetes streamlines cloud operations by automating key tasks, specifically deploying, scaling, and managing containerized applications. With Kubernetes, you have the ability to group hosts running containers into clusters, simplifying cluster management across public, private, and hybrid cloud environments... View the full article

In the advancement of the recent technology space, there have been some excitements in data science, deep learning, artificial intelligence, and big data. These advancements have led to the development of a dynamic ecosystem for data analysis. However, data analysis became more complicated as the data increased and there was the need to bring in new algorithms relating to machine learning to help data scientists have a better analyzing experience. MLOps was birthed from the development of these algorithms since it was required to deploy resources, version codebases, integrate data, and even test procedures. Since then, it has been widely used across several industries. View the full article

SAN FRANCISCO, Nov. 16, 2020 (GLOBE NEWSWIRE) — Tecton, the enterprise feature store company, today announced that it will become a core contributor to Feast and allocate engineering and financial resources to the project to build advanced capabilities. Feast is the leading open source feature store for machine learning (ML) that bridges data and models and […] The post Tecton Becomes Feast Core Contributor to Build the Most Advanced Open Source Feature Store for Machine Learning appeared first on DevOps.com. View the full article

Organizations today are striving to build agility and resilience to the fast-changing environment we live in. AI and machine learning innovation can help tackle these emerging challenges and enable cost efficiencies. However, organizations still encounter barriers to adopting and deploying machine learning at scale. Recently at Microsoft Ignite, Azure Machine Learning made a number of announcements that help organizations harness machine learning more easily, securely, and at scale. This includes capabilities like designer and automated machine learning UI, now generally available, that simplify machine learning for beginners and professionals alike. Advanced role-based access control (RBAC) and private IP link, in preview, make it possible to build machine learning solutions more securely. In addition, we are merging the Azure Machine Learning Enterprise and Basic Editions to deliver greater value at no extra cost. “With Azure Machine Learning, we’re increasing speed-to-value while reducing cost-to-value.” – Sarah Dods, Head of Advanced Analytics, AGL. Read the story. Machine learning simplified Azure Machine Learning designer provides a drag-and-drop canvas to build no-code models with ease. Built-in modules help preprocess data and build and train models using machine learning and deep learning algorithms, including computer vision, text analytics, recommendation, anomaly detection, and more. You can also customize models using Python or R code and deploy them as batch or real-time endpoints with a few clicks. “By using Azure Machine Learning designer we were able to quickly release a valuable tool built on machine learning insights, that predicted occupancy in trains, promoting social distancing in the fight against COVID-19” – Steffen Pedersen, Head of AI and advanced analytics, DSB (Danske Statsbaner, Danish State Railways) ​Azure Machine Learning designer used for image classification. You can use Azure Machine Learning automated machine learning to rapidly build highly accurate models by automating iterative tasks. The no-code UI helps build and deploy models refined by a wide array of algorithms and hyperparameters. It supports a variety of tasks like classification, regression, and time-series forecasting and statistical models like ARIMA, Prophet, and deep learning models like TCN. You can understand and control the model building process, discover errors and inconsistencies in data using guardrails, and use model explanations for transparency into the model. ​ Model explanations in Automated machine learning UI help understand what features impact the model. Data labeling in Azure Machine learning gives data teams a central place to create, manage, and monitor labeling projects. It supports image classification, multi-label and multi-class, and object identification with bounded boxes. The machine learning assisted labeling capability helps trigger automatic machine learning models to accelerate labeling tasks. Mission-critical MLOps, security, and scale “Azure Machine Learning’s MLOps is at the core of our product. Because of the reproducible machine learning pipelines, reusable environments, versioned models and more, we’re detecting things that we missed before. Which, in terms of risk management, is critical.” – Ignasi Paredes-Oliva, Senior Data Scientist, Nestlé Global Security Operations Center. Read the story. Azure Machine Learning now fully supports managing the end-to-end machine learning lifecycle using open MLflow standards. You can submit training jobs using MLflow experiments and MLflow Projects. When ready to scale to the cloud, easily switch the configuration to run models on Azure Machine Learning. The models are registered and tracked using MLOps and the central registry, making it easier to deploy to Azure Container Instance or Azure Kubernetes Service. ​ Azure Machine Learning logs MLflow properties for MLflow model traceability. Private IP is a common requirement in regulated industries such as government, finance, and healthcare. Workspace Private Link is a network isolation capability that enables access to Azure Machine Learning over a private IP in your virtual network (VNet). Administrators can ensure that traffic between your VNet and Azure Machine Learning travels through the Microsoft network so that the machine learning workspace and assets are no longer be exposed to the internet.​ With Azure Machine Learning advanced RBAC, IT admins can create roles that map to user types in Azure Machine Learning for better control of users working in a workspace using permissions and reducing risk. For example, data labelers can be scoped to only allow data labeling actions, or MLOps roles can only submit published pipelines. Access Azure Machine Learning today We are merging Azure Machine Learning Enterprise and Basic Editions to bring you all the rich capabilities for end-to-end machine learning, at no added cost, in a single offering. You only pay for Azure resources consumed, with no additional charge. This builds on Azure Machine Learning's cost management capabilities, and we are committed to ensuring that Azure remains your platform of choice for machine learning. We hope you will join us and start your journey with Azure Machine Learning today! Try Azure Machine Learning for free. Learn more about Azure Machine Learning and follow the quick start guides and tutorials. See how Forrester named Microsoft and Azure Machine Learning a leader in their Notebook-based Predictive Analytics and Machine Learning wave report. View the full article

What is Machine Learning? Machine Learning (ML) is an application of artificial intelligence (AI) that allows systems the ability to automatically learn from experience and to predict outcomes without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves. ML has become one of the most important investments from community funding in recent years. Indeed, ML is the technology most likely to provide machines a way to eventually surpass the intelligence levels of humans. With the news technologies introduced these past few years, ML is becoming more and more important, and, in due time, it will be absolutely necessary for the survival of companies in all sectors. How will it impact businesses? Machine Learning involves creating computer algorithms that learn from existing data. Hence, ML tools allow companies to identify valuable opportunities and potential risks more quickly. The applications of ML drive business results in a way that can positively affect a company. With the rapid growth of new techniques, ML is always evolving and creating endless possibilities. For industries that depend on vast amounts of data, which need to be analyzed efficiently, Machine Learning is the best solution to build models, strategize, and plan accurately. For instance, many industries, and especially start-ups, use ML and apply it to specific industry verticals, such as detecting bank fraud or preventing a cyber-attack, with predictive data models or software platforms that analyze behavioral data. Machine Learning needs to be integrated fully within businesses to increase performance and retention. To accomplish this, there is a need to prioritize IT applications over IT architecture as well as have more engagement with AI. Using more ML tools also promote a healthier work environment. ML has now been used across various industries such as healthcare, manufacturing, and financial services, and there is no sign of it slowing down. A majority of companies are actively using ML to work on data; hence, the most successful models today are those which enable certain tasks to be taken over by AI. Thus, ML can learn from and predict consumer behavior, and deliver reliable data sets, which will drive results. Businesses are now making better use of employees by having AI work on certain tasks and allowing humans to use their skills to improve productivity. For this, they need to adopt a clear data governance framework where information is handed to these new technologies and making Machine Learning the best way of survival for businesses in the future. The post Why is Machine Learning essential to businesses? appeared first on DevOps Online. View the full article

Looking for some of the best machine learning tools on AWS? Here your search ends! Let’s get familiar with some basic details and dive deep into the list of top AWS machine learning tools. Machine learning may be a new term for many, although having been popular across different sectors. Back in 1952, Arthur Samuel first coined the term “Machine Learning,” thus establishing the foundation for one of the radical technological interventions. In present times, machine learning technology is a vital tool for obtaining predictions and valuable insights regarding business operations. AWS has been one of the frontrunners in the field of machine learning alongside its other counterparts. However, the effectiveness of AWS machine learning tools is one of the foremost highlights that provide a competitive advantage to AWS. The following discussion aims to reflect on some of the notable machine learning tools of AWS. Readers can find out more about the efficiency of AWS as a reliable platform for machine learning from this discussion. Try Now: AWS Certified Machine Learning Specialty Free Test Importance of Machine Learning Tools of AWS Amazon Web Services is the leading public cloud service provider and has a wide array of cloud services and technologies on offer. Therefore, you could also find AWS machine learning tools suited to your various enterprise requirements. AWS provides a wider and deeper variety of machine learning and AI services for different businesses. The machine learning tools on AWS primarily aimed at helping customers in addressing critical challenges that restrict developers from leveraging the optimal power of machine learning. Users could select pre-trained AI services to address applications of forecasting, computer vision, recommendations, and language processing. On the other hand, AWS also provides tools for faster creation, training, and deployment of machine learning models with higher scalability. Users also have the advantage of building custom models while ensuring compatibility with major open-source frameworks. The most promising strength of AWS machine learning tools is that they are based on a highly comprehensive cloud platform. AWS is ideally optimized for machine learning with the facility of high-performance compute and a lack of compromises in security and analytics. All of these aspects clearly establish the necessity for finding out the machine learning tools offered by AWS. New in Machine Learning? Read our previous blog to understand the basics of Amazon Machine Learning. List of Top AWS Machine Learning Tools Now when you have understood the importance of AWS machine learning tools, it’s time to check out the top AWS machine learning tools. These machine learning solutions help in building and deployment of the machine learning models. Let’s move to the list: Amazon SageMaker Amazon SageMaker is always the obvious addition among machine learning solutions in the AWS marketplace. It is a fully-managed platform that helps data scientists and developers ensure the easier and faster building, training, and deployment of machine learning models at a different scale. Amazon SageMaker clips off all the barriers which generally slow down developers aspiring to use machine learning. Machine learning generally presents difficulty in learning due to the complex processes for building and training the models. In addition, the deployment of machine learning models into production is also slow and complicated. Furthermore, the expertise required for all these processes alongside other resource requirements presents many barriers to machine learning for developers. Amazon SageMaker removes the complexity and helps developers understand and utilize the full potential of all steps in machine learning. The modular design of Amazon SageMaker makes it one of the most flexible machine learning tools on AWS. You can use the different modules together or independently for building, training, and deploying machine learning models. Amazon SageMaker Ground Truth Datasets are the lifeblood of machine learning, and Amazon SageMaker Ground Truth offers the platform for the development of training datasets for machine learning with higher accuracy and speed. SageMaker Ground Truth is one of the top AWS machine learning tools because it provides easy access to public and private human labelers. In addition, it also facilitates labelers with interfaces and in-built workflows for general labeling tasks. Most important of all, SageMaker Ground Truth can reduce labeling costs by almost 70% through automatic labeling. The effective use of machine learning for automatic data labeling offers better cost savings and productivity. The SageMaker Ground Truth model gradually becomes efficient over time through learning continuously from labels by human labelers. As a result, it can improve its capability for labeling more data automatically and contributing to faster training of datasets. Also Read: Deep Learning on AWS Amazon Lex The next promising addition among Amazon machine learning tools is Amazon Lex. It is a service for developing conversational interfaces in any application through the use of voice and text. Lex offers the functionalities of advanced deep learning in the form of automatic speech recognition (ASR) for the conversion of speech to text. In addition, it also provides natural language understanding features for recognizing the intent in a text. As a result, it can enable the development of applications with highly interactive user experiences and almost real conversational interactions. Amazon Lex simplifies access to speech recognition and natural language understanding alongside presenting the power of Alexa to all developers. It is one of the leading technologies for the development of entirely new categories of products created only through conversational interfaces. AWS Inferentia One of the striking AWS machine learning tools is AWS Inferentia. It is a machine learning inference chip that aims at delivering higher performance at lower costs. AWS Inferentia offers support for Apache MXNet, PyTorch, and TensorFlow deep learning frameworks and models using the ONNX format. AWS Inferentia facilitates higher throughput, low latency inference performance at unbelievably low costs. Every chip can assure hundreds of TOPS (Tera Operations Per Second) of inference throughput for allowing complex models to ensure faster predictions. Users can also use AWS Inferentia chips in combination to achieve additional TOPS of throughput. In addition, it will be supported on Amazon Elastic Inference, Amazon SageMaker, and Amazon EC2. Amazon Textract Amazon Textract is undoubtedly one of the productive Amazon machine learning tools. It is a service that extracts text and data automatically from scanned documents. Amazon Textract offers more than the capabilities of optical character recognition (OCR) and helps in the identification of content in the fields through forms and information stored in tables. Textract addresses the challenges of slow and expensive manual data entry processes for the extraction of data from documents. It also enables faster automation of document workflows, thereby ensuring that you can process many documents within hours. After capturing the information, you can take necessary action on it. Users can also create automated approval workflows and smart search indexes with Textract. Furthermore, it also offers better compliance with rules of document archival. Preparing to become a certified professional in AWS Machine Learning? Follow this preparation guide for AWS Certified Machine Learning – Speciality exam and get ahead. Amazon Comprehend Amazon Comprehend is the foremost entry among AWS machine learning tools that comes to mind when you think of Natural Language Processing (NLP). It is an NLP service based on machine learning for finding insights and relationships between various attributes in text. Amazon Comprehend utilizes machine learning for discovering new insights and relationships in the available unstructured data. It can identify the language in the text and extract key phrases, events, places, brands, and people in a text. Amazon Comprehend utilizes tokenization and parts of speech for analysis of text and automatic organization of a set of text files according to the topic. The AutoML features in Amazon Comprehend can also help in creating a custom set of text classification models or entities built specifically according to an enterprise’s needs. Amazon Rekognition Amazon Rekognition is among the many common AWS machine learning tools that you can find at present. It is a service that helps in adding image analysis capabilities to different applications. Rekognition can help in the detection of objects, faces, and scenes in particular images. It can also help in searching and comparing faces. The Amazon Rekognition API provides the ease of adding advanced deep-learning-based visual search and image classification capabilities to applications. Amazon Rekognition leverages deep neural network models for the detection and labeling of multiple objects and scenes in images. As a result, you can find Amazon Rekognition as a vital tool for integrating powerful visual search and discovery functionalities into an application. Also Read: Impact of Machine Learning on Cloud Computing Amazon Elastic Inference Amazon Elastic Inference is also one of the formidable entries among AWS machine learning tools. It helps in attachment of low-cost GPU-based acceleration with Amazon SageMaker and EC2 instances for reduction of the costs in running deep learning inference by almost 75%. Amazon Elastic Inference addresses the problems of resource inefficiency in GPU compute by attacking the right amount of GPU-based inference acceleration to EC2 or SageMaker instance types without modifications in the code. Users can select the instance type suited perfectly for the overall CPU and memory requirements of an application. You can also configure the amount of inference acceleration for efficient use of resources and reduction in costs of running inference. Amazon Translate Amazon Translate is one of the productive AWS machine learning tools with the maximum potential of machine learning for users. It is a neural machine translation device for faster, affordable, and highly accurate language translation. Amazon Translate helps in localization of content such as applications and websites for international users. Its primary functionalities are evident in the easier translation of large volumes of text with the assurance of efficiency. Preparing for a Machine Learning interview? Here are the top Machine Learning interview questions that will get you ready for the interview. Bottom Line On a concluding note, it is inevitable to note that covering all AWS machine learning tools in a limited discussion is quite hard. There are many other notable machine learning tools such as Amazon Forecast, Amazon DeepRacer, Amazon Personalize, Amazon DeepLens, Amazon Transcribe, TensorFlow on AWS, and others. All of the tools have specific functionalities that simplify the work of developers and data scientists. Machine learning tools offer sophisticated frameworks for data analysis alongside reliable tools for developers to add application functionalities. Interestingly, Amazon continues to add new machine learning tools and services frequently alongside introducing new features in existing solutions. Learn more about the machine learning tools of AWS and validate your expertise with the AWS Machine Learning Specialty certification exam. Enroll in our AWS Machine Learning Specialty training courses and try out the practice tests to give your preparation a new edge. The post List of Top AWS Machine Learning Tools appeared first on Whizlabs Blog. View the full article

In the previous tutorial, we have discussed some basic concepts of NumPy in Python Numpy Tutorial For Beginners With Examples. In this tutorial, we are going to discuss some problems and the solution with NumPy practical examples and code. As you might know, NumPy is one of the important Python modules used in the field of data science and machine learning. As a beginner, it is very important to know about a few NumPy practical examples. Numpy Practical Examples Let’s have a look at 7 NumPy sample solutions covering some key NumPy concepts. Each example has code with a relevant NumPy library and its output. How to search the maximum and minimum element in the given array using NumPy? Searching is a technique that helps finds the place of a given element or value in the list. In Numpy, one can perform various searching operations using the various functions that are provided in the library like argmax, argmin, etc. numpy.argmax( )This function returns indices of the maximum element of the array in a particular axis. Example: import numpy as np # Creating 5x4 array array = np.arange(20).reshape(5, 4) print(array) print() # If no axis mentioned, then it works on the entire array print(np.argmax(array)) # If axis=1, then it works on each row print(np.argmax(array, axis=1)) # If axis=0, then it works on each column print(np.argmax(array, axis=0)) Output: [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15] [16 17 18 19]] 19 [3 3 3 3 3] [4 4 4 4] Similarly one can use numpy.argmin( ) to return indices of the minimum element of the array in a particular axis. How to sort the elements in the given array using Numpy? Sorting refers to arrange data in a particular format. Sorting algorithm specifies the way to arrange data in a particular order. In Numpy, one can perform various sorting operations using the various functions that are provided in the library like sort, argsort, etc. numpy.sort( )This function returns a sorted copy of an array. Example: import numpy as np array = np.array([ [3, 7, 1], [10, 3, 2], [5, 6, 7] ]) print(array) print() # Sort the whole array print(np.sort(array, axis=None)) # Sort along each row print(np.sort(array, axis=1)) # Sort along each column print(np.sort(array, axis=0)) Output: [[ 3 7 1] [10 3 2] [ 5 6 7]] [ 1 2 3 3 5 6 7 7 10] [[ 1 3 7] [ 2 3 10] [ 5 6 7]] [[ 3 3 1] [ 5 6 2] [10 7 7]] numpy.argsort( )This function returns the indices that would sort an array. Example: import numpy as np array = np.array([28, 13, 45, 12, 4, 8, 0]) print(array) print(np.argsort(array)) Output: [28 13 45 12 4 8 0] [6 4 5 3 1 0 2] How to find the mean of every NumPy array in the given list? The problem statement is given a list of NumPy array, the task is to find mean of every NumPy array. Using np.mean( )import numpy as np list = [ np.array([3, 2, 8, 9]), np.array([4, 12, 34, 25, 78]), np.array([23, 12, 67]) ] result = [] for i in range(len(list)): result.append(np.mean(list[i])) print(result) Output: [5.5, 30.6, 34.0] How to add rows and columns in NumPy array? The problem statement is given NumPy array, the task is to add rows/columns basis on requirements to numpy array. Adding Row using numpy.vstack( ) import numpy as np array = np.array([ [3, 2, 8], [4, 12, 34], [23, 12, 67] ]) newRow = np.array([2, 1, 8]) newArray = np.vstack((array, newRow)) print(newArray) Output: [[ 3 2 8] [ 4 12 34] [23 12 67] [ 2 1 8]] Adding Column using numpy.column_stack( ) import numpy as np array = np.array([ [3, 2, 8], [4, 12, 34], [23, 12, 67] ]) newColumn = np.array([2, 1, 8]) newArray = np.column_stack((array, newColumn)) print(newArray) Output: [[ 3 2 8 2] [ 4 12 34 1] [23 12 67 8]] How to reverse a NumPy array? The problem statement is given NumPy array, the task is to reverse the NumPy array. Using numpy.flipud( )import numpy as np array = np.array([3, 6, 7, 2, 5, 1, 8]) reversedArray = np.flipud(array) print(reversedArray) Output: [8 1 5 2 7 6 3] How to multiply two matrices in a single line using NumPy? The problem statement is given two matrices and one has to multiply those two matrices in a single line using NumPy. Using numpy.dot( )import numpy as np matrix1 = [ [3, 4, 2], [5, 1, 8], [3, 1, 9] ] matrix2 = [ [3, 7, 5], [2, 9, 8], [1, 5, 8] ] result = np.dot(matrix1, matrix2) print(result) Output: [[19 67 63] [25 84 97] [20 75 95]] How to print the checkerboard pattern of nxn using NumPy? The problem statement is given n, print the checkerboard pattern for a nxn matrix considering that 0 for black and 1 for white. Solution: import numpy as np n = 8 # Create a nxn matrix filled with 0 matrix = np.zeros((n, n), dtype=int) # fill 1 with alternate rows and column matrix[::2, 1::2] = 1 matrix[1::2, ::2] = 1 # Print the checkerboard pattern for i in range(n): for j in range(n): print(matrix[i][j], end=" ") print() Output: 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 View the full article