Integrated Development Environments (IDEs) are essential tools that provide comprehensive facilities to programmers for software development. They combine various development tools into a single application, enhancing productivity and streamlining workflows. Below is a detailed overview of the top 21 IDEs, each with its major features to assist you in selecting the most suitable environment for your development needs.

1. Visual Studio Code

Visual Studio Code (VS Code) is a free, open-source IDE developed by Microsoft. It’s renowned for its lightweight nature and extensive extension ecosystem, making it adaptable to various programming languages and workflows.

Major Features:

• Extensibility: A vast library of extensions allows customization to support different languages and tools.
• Integrated Terminal: Execute commands directly within the editor without switching contexts.
• IntelliSense: Provides intelligent code completions based on variable types, function definitions, and imported modules.
• Version Control Integration: Built-in support for Git and other version control systems facilitates seamless code management.
• Live Share: Enables real-time collaborative coding sessions with peers.

2. IntelliJ IDEA

Developed by JetBrains, IntelliJ IDEA is a powerful IDE primarily focused on Java development but also supports a wide range of other languages. It’s known for its intelligent code assistance and ergonomic design.

Major Features:

• Smart Code Completion: Offers context-aware suggestions to speed up coding.
• Advanced Refactoring: Provides reliable tools to restructure code efficiently.
• Built-in Developer Tools: Includes version control integration, terminal, and database tools.
• Framework Support: Extensive support for frameworks like Spring, Hibernate, and Java EE.
• Plugin Ecosystem: Access to a vast library of plugins to extend functionality.

3. Eclipse

Eclipse is a free, open-source IDE with a modular architecture, making it highly extensible. It’s widely used for Java development but supports other languages through plugins.

Major Features:

• Plugin-Based Architecture: Allows customization and extension to support various languages and tools.
• Robust Debugging Tools: Provides a comprehensive debugging environment with breakpoints and step-through execution.
• Refactoring Support: Offers tools to restructure code efficiently.
• Integration with Build Systems: Seamless integration with build tools like Maven and Gradle.
• Community Support: A large community contributes to a rich ecosystem of plugins and extensions.

4. PyCharm

PyCharm, also developed by JetBrains, is an IDE specifically designed for Python development. It offers a range of tools to enhance productivity and code quality.

Major Features:

• Intelligent Code Editor: Features code completion, inspections, and quick fixes tailored for Python.
• Web Development Support: Supports frameworks like Django and Flask for web development.
• Scientific Tools Integration: Integrates with Jupyter Notebooks and scientific libraries.
• Testing Assistance: Provides a test runner and debugger for efficient testing.
• Database Tools: Built-in tools to manage databases and SQL support.

5. NetBeans

NetBeans is an open-source IDE that supports multiple languages and is known for its user-friendly interface and powerful project management features.

Major Features:

• Project Management: Simplifies project setup with templates and a user-friendly interface.
• Code Generation: Automates repetitive coding tasks to boost productivity.
• Cross-Platform Support: Runs on various operating systems, including Windows, macOS, and Linux.
• Dynamic Language Support: Supports languages like Java, PHP, and HTML5.
• Version Control Integration: Integrates with Git, Mercurial, and Subversion.

6. Xcode

Xcode is Apple’s official IDE for macOS and iOS application development, providing a comprehensive suite of tools for developers.

Major Features:

• Interface Builder: Allows for drag-and-drop UI design for macOS and iOS applications.
• Swift and Objective-C Support: Comprehensive support for Apple’s primary programming languages.
• Simulator: Test applications on various virtual devices.
• Instruments: Performance analysis and debugging tools.
• App Store Integration: Streamlines the process of submitting apps to the App Store.

7. Android Studio

Android Studio is the official IDE for Android application development, offering tools tailored specifically for building Android apps.

Major Features:

• Layout Editor: Design complex layouts with a visual editor.
• APK Analyzer: Inspect the contents of your APKs to reduce app size.
• Advanced Code Completion: Provides smart code suggestions for Java and Kotlin.
• Real-Time Profilers: Monitor CPU, memory, and network activity in real-time.
• Firebase Integration: Seamless integration with Firebase services for enhanced app capabilities.

8. CLion

CLion, another JetBrains product, is a cross-platform IDE for C and C++ development, known for its smart coding assistance and deep code analysis.

Major Features:

• Smart C and C++ Support: Advanced code analysis and refactoring tools for C and C++ languages.
• Cross-Platform Development: Supports Windows, macOS, and Linux platforms.

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Logs are an essential part of modern IT systems. They provide detailed insights into the operations, performance, and security of applications and infrastructure. Effective log management helps organizations monitor their systems, troubleshoot issues, and enhance security. In this guide, we present a detailed list of the most popular and effective log management tools available today.

1. Splunk

Splunk is one of the most popular log management tools, offering robust features for log analysis and monitoring.

• Features: Real-time search, analysis, and visualization of machine data. Advanced machine learning capabilities.
• Use Cases: IT operations, security, and DevOps.
• Strengths: Scalable, supports large data volumes, and offers extensive third-party integrations.

2. Elastic Stack (ELK Stack)

The Elastic Stack consists of Elasticsearch, Logstash, and Kibana, forming a powerful open-source log management and analysis suite.

• Features: Centralized logging, real-time analytics, and rich visualization.
• Use Cases: Application performance monitoring, security analytics, and operational monitoring.
• Strengths: Cost-effective, customizable, and open-source.

3. Graylog

Graylog is an open-source log management platform known for its user-friendly interface and efficient performance.

• Features: Centralized log storage, quick search capabilities, and alerting.
• Use Cases: Application debugging, security event management, and compliance reporting.
• Strengths: Simple setup, intuitive UI, and affordable pricing.

4. LogRhythm

LogRhythm provides enterprise-grade log management and security information and event management (SIEM).

• Features: Threat detection, log collection, and machine analytics.
• Use Cases: Security operations, compliance, and IT operations.
• Strengths: Strong security focus, advanced threat intelligence, and user-friendly dashboards.

5. Datadog

Datadog is a comprehensive monitoring and analytics platform that also offers log management capabilities.

• Features: Log ingestion, monitoring, and correlation with metrics and traces.
• Use Cases: DevOps, cloud monitoring, and application performance management.
• Strengths: Easy integration with cloud services and seamless correlation of logs with other metrics.

6. Sumo Logic

Sumo Logic is a cloud-native log management and analytics platform built for scalability.

• Features: Real-time log analytics, security analytics, and predictive insights.
• Use Cases: Cloud monitoring, DevSecOps, and compliance.
• Strengths: Scalable, fast, and designed for modern cloud architectures.

7. Papertrail

Papertrail is a simple and lightweight log management solution suitable for small to medium-sized businesses.

• Features: Real-time log aggregation, live tailing, and alerts.
• Use Cases: Debugging, error tracking, and performance monitoring.
• Strengths: Easy to use, affordable, and quick to set up.

8. SolarWinds Log Analyzer

SolarWinds Log Analyzer is a powerful log management tool that integrates with other SolarWinds products.

• Features: Real-time log streaming, search, and event correlation.
• Use Cases: Network troubleshooting, application monitoring, and compliance.
• Strengths: Seamless integration with SolarWinds ecosystem and easy setup.

9. Fluentd

Fluentd is an open-source data collector that helps with log aggregation and processing.

• Features: Log collection, transformation, and forwarding.
• Use Cases: Unified logging and monitoring for cloud-native applications.
• Strengths: Lightweight, flexible, and open-source.

10. LogDNA

LogDNA is a log management tool designed for modern DevOps teams and cloud environments.

• Features: Centralized log storage, real-time search, and custom parsing.
• Use Cases: Kubernetes log management, security monitoring, and troubleshooting.
• Strengths: Easy Kubernetes integration and intuitive interface.

11. ManageEngine EventLog Analyzer

ManageEngine EventLog Analyzer is a comprehensive tool focusing on log management and compliance.

• Features: Log analysis, audit trails, and compliance reporting.
• Use Cases: Security audits, compliance management, and IT operations.
• Strengths: Affordable and feature-rich for SMBs and enterprises.

12. Humio

Humio is a modern log management solution emphasizing real-time insights and scalability.

• Features: Real-time log data streaming, advanced search, and analytics.
• Use Cases: Security operations, DevOps, and incident response.
• Strengths: High-performance search and unlimited scalability.

13. Syslog-ng

Syslog-ng is an open-source log management tool focusing on log collection and forwarding.

• Features: Log routing, filtering, and centralization.
• Use Cases: Network monitoring, application logging, and system auditing.
• Strengths: Lightweight and flexible configuration options.

14. Splunk Light

A lighter version of Splunk, tailored for small IT environments and startups.

• Features: Basic log collection, monitoring, and search capabilities.
• Use Cases: Small-scale IT operations and troubleshooting.
• Strengths: Cost-effective alternative to full Splunk.

15. Nagios Log Server

Nagios Log Server provides centralized log management and monitoring.

• Features: Log storage, search, and alerting.
• Use Cases: Network monitoring, event logging, and security.
• Strengths: Integrates well with other Nagios tools.

Choosing the Right Log Management Tool

When selecting a log management tool, consider the following:

1. Scale and Volume: Choose a solution that can handle your organization’s data volume.
2. Ease of Use: Prioritize tools with intuitive interfaces and easy deployment.
3. Integrations: Ensure compatibility with your existing systems and applications.
4. Budget: Select a tool that aligns with your budget without compromising essential features.
5. Security and Compliance: Evaluate the tool’s ability to meet regulatory requirements and enhance security.

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No wonder, data science has emerged out to be the most sought-after profession. Obtaining insights from data, as data science is rightly defined, has proven to be no less than a blessing in almost every sector that one can think of. Making the best of data is what data scientists are expected to do. Data visualization is thus a critical aspect of data science and when done well can yield the desired results. That being said, a question that is seeking answers is – how to achieve efficient data visualization so that the organization is in a position to make better decisions? Well, data visualization tools to the rescue it is!

In order to make the whole data visualization process smooth and to achieve valuable results, having the right data visualization tools that are worth relying on – is the need of the hour. Here is a list of top data visualization tools for 2021 that you wouldn’t want to miss out on.

Tableau

Tableau is one of the most widely used data visualization tools What sets it apart from the rest is its ability to manage the data using the combination of data visualization and data analytics tools. From a simple chart to creative and interactive visualizations, you can do it all using Tableau. One of the many remarkable features of this tool is that the data scientists do not have to write custom code in this tool. Additionally, the tasks are completed fast and with ease because of the drag and drop feature supported by this tool. All in all, Tableau is interactive software that is compatible with a lot of data sources.

Sisense

If looking for a data visualization tool that is used to create dashboards and visualise large amounts of data, then Sisense is the one for you! From health, manufacturing to social media marketing, Sisense has proved to be beneficial. The best part about Sisense is that the dashboard can be created in the way the user wants to according to their needs.

PowerBI

This is yet another interactive data visualization tool that helps in converting data from various data sources into interactive dashboards and reports. In addition to providing real-time updates on the dashboard, it also provides a secure and reliable connection to your data sources in the cloud or on-premise. Enterprise data analytics as well as self-service is something that you get on a single pl PowerBI, being available for both mobile and desktop versions has, without a doubt, benefitted many. Why PowerBI gets all the attention is because even non-data scientists can easily create machine learning models.

E charts

E charts is one of the most sought after enterprise-level chart data visualization tool. E charts are compatible with a majority of browsers, runs smoothly on various platforms and are referred to as a pure JavaScript chart No matter what size the device is, charts would be available. This data visualization tool being absolutely free to use provides a framework for the rapid construction of web-based visualizations and boasts of multidimensional data analysis.

DataWrapper

DataWrapper is an excellent data visualization tool for creating charts, maps and tables. With this, you can create almost any type of chart, customizable maps and also responsive tables. Additionally, printing and sharing the charts is not at all an issue to be bothered about. From students to exerts, everyone can make use of DataWrapp This data visualization tool gives away the message that charts and graphs stand the potential to look great even without coding or any design skills. The free version of this tool has many features that are definitely worth giving a try.

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Learn the best machine learning tools to efficiently build ML models  

With ever-growing data generation and its usage, the demand for machine learning models is multiplying. As ML systems encompass algorithms and rich ML libraries, it helps analyze data and make decisions. There is no wonder that machine learning is gaining more visibility as ML applications are dominating almost every aspect of the modern-day world. With rapidly increasing exploration and adoption of this technology in businesses, it is setting the ground for ample employment opportunities. However, landing a career in this disruptive field, you must be well-equipped and familiar with some of the best machine learning tools to create efficient and functional ML algorithms.

Here are the 10 best machine learning tools to look for in 2021.

1. TensorFlow

TensorFlow is a free machine learning tool that can be used on platforms such as Linux, Mac OS, and Windows. It offers a JavaScript library that helps in machine learning. It helps users to build and train their models. TensorFlow is an excellent ML tool for deep learning systems and neural nets. Users can also run their existing models using TensorFlow.js, a model converter.

2. KNIME

KNIME has been utilized in pharmaceutical research and other areas like CRM customer data analysis, business intelligence, text mining and financial data analysis. It is a free, open-source data analytics, reporting and integration platform. This machine learning tool can integrate the code of programming languages like R, Python, Java, JavaScript, C, C++, etc. KNIME is useful for beginners as this platform is built for powerful analytics on a GUI-based workflow. This means, if users don’t have the knowledge of how to code, they will be able to work using KNIME and derive insights.

3. Scikit-Learn

Scikit-Learn is a free machine learning library for Python. It helps in data mining and data analysis, and provides models and algorithms for classification, regression, clustering, dimensional reduction, model selection, and pre-processing. Built on NumPy, SciPy, and Matplotlib, Scikit-Learn involves an array of efficient tools for machine learning and statistical modeling.

4. PyTorch

PyTorch is based on the Torch library used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab. This open-source machine learning library helps in building neural networks through Autograd Module. It provides a variety of optimization algorithms for building neural nets. PyTorch can be used on cloud platforms and can provide distributed training, with numerous tools and libraries.

5. BigML

It is a comprehensive machine learning platform that offers a host of ML algorithms to solve complex, real-world problems through a unified and integrated framework. BigML consists of a wide array of useful machine learning features, such as classification, regression, cluster analysis, time-series forecasting, anomaly detection, topic modeling, etc. As it comes with an extensive range of features well-integrated within a convenient Web UI, it allows users to load their dataset, build and share their machine learning models, train and assess their models, and make new predictions.

6. Weka

Weka is a data mining, open-source machine learning software. It can be accessed through a graphical user interface (GUI), standard terminal applications, or a Java API. Weka encompasses a collection of visualization tools and algorithms for data analysis and predictive modeling and is widely used for teaching, research, and industrial applications. It supports various standard data mining tasks, particularly, data pre-processing, clustering, classification, regression, visualization and feature selection.

7. Colab

Colab is a Google Research product for machine learning tasks. It allows developers to write and execute Python code through their browser. Colab notebooks let users combine executable code and rich text in a single document, along with images, HTML, LaTeX and more. When a user creates their Colab notebooks, they are stored in their Google Drive account that can be easily shared with their peers.

8. Amazon Machine Learning

Amazon offers a cohort of machine learning tools. Amazon Machine Learning (AML) is a cloud-based, robust machine learning software application that can be used by all levels of web and mobile app developers. AML provides wizards & visualization tools as well as supports three types of models: multi-class classification, binary classification, and regression.

9. IBM Watson Studio

IBM Watson Studio allows users to build, run and manage machine learning models. It offers all the tools required to solve business problems through a collaborative data experience. It brings together vital open-source tools, including RStudio, Spark and Python, in an integrated environment, along with additional tools such as a managed Spark service and data shaping facilities, in a secure and governed environment.

10. Apache Mahout

As an open-source, distributed linear algebra framework, Apache Mahout helps mathematicians, statisticians and data scientists to execute their algorithms. It is a project of the Apache Software Foundation to make free implementations of distributed or otherwise scalable ML algorithms focused primarily on linear algebra. It contains Java libraries for common maths operations.

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Building upon years of momentum, architects are well in the swing of transitioning from the monolith to microservices. Here are three of the top trends they’ll face in 2021.

Throughout 2020, application architects sought new design methods and tools to help ease the transition from the monolith to a distributed architecture design — and they’re not done yet. Specifically, they are looking to solve issues surrounding consistency in distributed service management, incremental migration to microservices and service granularity.

Let’s take a closer look at some of these distributed architecture trends and examine how these shifts will affect the development landscape in 2021, including the microservices design patterns and tools that are top-of-mind for architects.

The modular monolith reigns

Despite the flexibility and scaling benefits associated with microservices, this type of architecture imposes significant hurdles upfront — specifically regarding observability, inter-service communication and complex deployment cycles. As such, architects must find a way to pursue microservices design patterns while still maintaining the dependability of their existing monolith. In 2021, architects will continue to pursue the modular monolith and establish bounded context by segmenting code, limiting dependencies and isolating data stores. This allows them to introduce the desired cohesion and loose coupling found in microservices, but avoid the headaches of managing multiple runtimes and asynchronous communication.

Loose coupling becomes essential

While a microservices design patterns demand a granular separation of services and responsibilities, development teams continue to apply traditional MVC patterns that demand tight component coupling. To successfully introduce distributed architecture design, more architects should make an effort to implement hexagonal architecture patterns in 2021. This pattern allows developers to create coarse-grained services that can gradually parse individual functions into individual services, but uses proxy components to spawn these services without affecting the underlying process logic.

Distributed services, centralized management

Teams that manage distributed systems must spend considerable time repetitively performing the same management tasks for hundreds of services, such as deployments, monitoring, logging and versioning. In an effort to eliminate waste, it’s likely that teams will increasingly adopt service templates and scaffolding tools in 2021. The hope is that these tools will promote coherence across collections of microservices by providing preconfigured service designs and enforcing standardized management practices.

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Before implementing a new model, a data scientist must be sure they understand their data set. One method data scientists use to better understand their data and understand what model would work best for it is exploratory data analysis.

Though there are many tools to employ in this process, the Python programming language offers some of the most used tools in exploratory data analysis. Since Python is one of the most popular programming languages used in data analysis and data mining, most data scientists already find it accessible. Using Python exploratory data analysis tools allows you to easily summarize and visualize your data to choose the best algorithms and modeling to derive the most useful information from your data.

What is exploratory data analysis? 

Exploratory data analysis focuses on what insights you can derive from the data. The process starts with defining the problem you need the data to solve. One of the first steps is to figure out what the business objective you need to address is. What is the specific end goal for your analysis?

Once you know the business objective, it’s possible to prepare your data and develop different models to achieve that goal. It also allows you to summarize the available data, find correlations and create visualizations to present your findings to stakeholders.

Exploratory data analysis is an important part of defending your model to business stakeholders and proving that it can produce the desired results.

Commonly used tools

There are plenty of tools that can facilitate the exploratory data analysis process, from tools built into programming languages such as Python and R to tools supported by vendors such as SAS and KNIME.

Python exploratory data analysis is common among data scientists as Python is one of the most-used programming languages in the field. Available programming libraries such as pandas and Matplotlib can easily summarize and create visualizations for your data. Additionally, there are libraries in the language that can facilitate hypothesis testing and identify correlations between factors.

Learning to use Python for exploratory data analysis

By understanding your data set with exploratory data analysis, it is possible to determine the effectiveness of your model and better determine the correct machine learning model to reach your defined business goals. Learning Python exploratory data analysis can help you easily pull the most meaningful insights from your data using a tool you’re already familiar with.

In their new book, Hands-On Exploratory Data Analysis with Python, published by Packt Publishing, authors Suresh Kumar Mukhiya and Usman Ahmed talked about useful techniques and Python tools for exploratory data analysis. The book includes links to sample code with step-by-step instruction on how to perform each process.

Here’s a preview of the book. Click here to read Chapter 1: Exploratory Data Analysis Fundamentals.

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Data Science can look like an intimidating field to some, especially if you have just started your journey. What tool to learn? What techniques to focus on? Do you learn to code? How much statistics is required? There are several such questions that you will find answers during your journey. Here are some tips that will help you start a career in the field of Data Science:  

1. Select the right role 

In the data science industry, there are several varied roles. You can be a data engineer, a data scientist, a machine learning expert, a data visualization expert, etc. These are just a few roles that you can go into. Depending on your work experience and background, getting into a certain role can be easier than others. For example, for a software developer, shifting into data engineering is not that difficult. However, if you are not clear regarding the path you should be taking, you will be confused. If you are unclear about the differences between the roles and want to figure out what to do, follow the below-mentioned steps: 

• Talk to the people already working in the field of data science to determine what roles to entail. 
• You can also take mentorship from people. Take a small amount of time and ask the questions. 
• Figure out the area that you are interested in and select the role that is best suited for your field of study. 

An important tip that you should keep in mind while deciding your role is that you should not jump to a role. You must understand the requirements of the field and then prepare for it. 

2. Take up a training program and finish it 

Once you have decided the role for you, the next thing that you have to do is make efforts to understand the role. This includes more than just going through the requirements. There are several courses available for data scientists. Finding the resources to learn is not difficult. However, learning them is a different story. 

If you decide to take up the training program, make sure that you go through it actively. You have to follow the coursework, discussions, and assignments that happen throughout the course. For example, if you want to be a data scientist, you have to take up a training program for data science certification . For the machine learning engineering role, you should enroll in a machine learning training program. You have to follow the course materials diligently. This also means that the assignments in the course are as important as going through the lectures. When you complete a course from one end to another, you will get a clearer picture of the field. 
 

3. Choosing your tools and language 

As mentioned above, it is important for you to complete the course from end-to-end of the topic that you are pursuing. However, a common question that students face is on selecting the tools and language. The straight answer is that you have to select mainstream tools and languages and start your journey. Remember that tools are just a method of implementation. What is more important is understanding the concept.  

However, if you are still not sure about what to use, you can start with the simplest language or the one you are familiar with. However, if you are not well-versed with coding, you can use GUI tools. As you cover the concepts, you can start coding as well.  

4. Join the peer group 

Now that you are aware of the role that you want and are prepared for it, the next important thing to do is join a peer group. This step is important as it helps in keeping you motivated. When you take on a new field, the whole process can be daunting if you do it alone. But when you have friends or colleagues beside you, the task can get a little easier. You can either join a group or connect with people online. Even if you don’t want to join a group, you can have a meaningful discussion over the internet. There are several online forums that can provide you with this form of environment. 
 

5. Focus on practical applications as well as the theory 

While you are undergoing training, it is important that you pay attention to the practical applications of all the concepts. This will help you in understanding the concept better and have a deeper sense of how you can apply these concepts in reality. Here are a few tips that you can follow: 

• Make sure that you complete all the assignments and exercises for understanding the applications better. 

• Work on data sets to apply your learning. Even if you don’t have any idea of the math behind a technique, you can understand what it does, its assumptions, and interpretation of the results. At a later stage, you can develop a better understanding.  
• Take a look at the solutions created by the people who are working in the field.  

6. Follow the right resources 

If you want to learn Data Science, you must engulf every source of knowledge that you can find. Some of the most influential data scientists have blogs where you can find several useful sources of information. All of these data scientists are active and update the followers regarding their findings. They also post frequently about Data Science’s recent advancements.  
 

You have to make a habit of reading about data science every day and stay updated with the recent findings. However, you also have to make sure that you don’t follow incorrect practices. 

7. Improve your communication skills 

Communication skills are an important part of the data scientist role. Even if you are technically profound, it is possible that you get rejected because of your poor communication skills. What you can do is take the help of a friend who has good communication skills and ask them for honest feedback. You need this skill for sharing your idea or proving your point. 

8. Network  

When you have just started studying, it is better to focus on learning and not do too many things at the initial stage. Only after you have a hang of the field should you attend industry conferences and events. You can also participate in hackathons and meetups.  

Data Science is in huge demand right now. And this is the reason why employers have been investing significant time, effort, and money. If you take the right steps, it can lead to significant growth.  

The post 8 tips before Starting a Career in Data Science appeared first on Artificial Intelligence.

Organizations failing to adopt AI risk falling behind in their markets. Even without the budget for it, advancing the AI know-how in your organization must include democratizing AI so it can become the province of everyday operations, and not just data scientists.

“‘Democratization’ can be a dirty word,” wrote blogger Chrissy Kidd in 2019. “Some folks hear it and conjure up long conference calls or work meetings abuzz with words like synchronicity and alignment. On the much more positive side, however, democratization is a good thing, where a resource that is useful can become more readily available to the masses.” 

Kidd said many companies are unsure how to begin. “Lots of other companies are behind. They may not know how to start using AI, and they may not have the resources to create their own AI.” 

Not exactly knowing what AI is, what it can and can’t do, and how to expand its use to different areas of the organization is precisely where most companies are. In the midst of the COVID-19 crisis, companies are also concerned about operating budgets and how much they can invest. In this environment, a discretionary expense such as internal staff training is one of the first items in the budget to get scaled back.

One way to keep AI momentum moving forward while also conserving budgets is by taking advantage of free AI educational resources that can help employees at all levels of the organization learn.

Here are two great, free ways to do this.

AI courses

Yes, you can take free AI courses to begin your education or sharpen your AI skills.

• Google offers a free online Machine Learning Crash Course. It’s 15 hours of work consisting of 25 lessons and 40 exercises. There’s also a free four-hour introductory course to machine learning. 
• Free courses are available for getting started with IBM Watson as well, and also for education on Watson’s cognitive learning functions. 
• Microsoft offers a free AI bootcamp for business executives.

AI tools

The open-source community is rich with free AI tools that enable employees to get involved with AI. Many of these tools provide on-ramps into AI application development methods that can be plugged directly into the company’s IT and data science work with AI.

Here is small sampling of some of the free AI tools that are are available as open source:

• IBM Watson Studio Desktop is available to academic and educational institutions as free, open-source AI software on the cloud. The tool covers AI and subsets of the discipline such as machine learning. Watson is an established platform that students and staff can experiment with as they develop their AI skills. Watson also offers code-free analysis tools, making it easier for non-IT and data science staff to experiment.
• Apache Mahout is a free AI tool for data mining in Hadoop, Facebook, Foursquare, Twitter, LinkedIn, and Yahoo. It is especially useful in online sales and marketing, given its ability to use purchase recommender engines, along with data filtering that can target users and customers.
• OpenNN provides rapid AI data processing speeds and free neural network libraries. Its sweet spot is dealing with machine learning, advanced algorithms, and the design of neural networks. It has delivered impactive results in the utilities and infrastructure sectors. OpenNN focuses on AI, machine learning, and predictive analytics. It is an open-source tool best used by IT and data science professionals.

There are free AI resources and tools out there that can help you and your organization enrich your AI knowledge. At the same time, you can be mindful of the financial bottom line of your company as you continue to move forward with your AI.

The post How to get free AI training and tools appeared first on Artificial Intelligence.

Fueled by the need to expand machine learning, cloud service providers are offering machine leaning as a service, together with extensive storage and processing options, to save time, money and resources. These tools now offer services spanning across model training, data processing, model evaluation and prediction making.

Companies with a cloud-based infrastructure are starting to take advantage of cloud computing for machine learning services for provisioning AI training and inferencing tasks in the cloud. Popular cloud computing tools are deployed through APIs and integrated into existing microservices infrastructure.

How cloud powers AI

Cloud platforms are starting to create better building blocks for setting up and automating many aspects of the machine learning development and deployment pipeline, according to Shekhar Vemuri, CTO of Clairvoyant, an AI consultancy firm.

“Cloud providers are abstracting the infrastructure, giving data scientists a head start on most of the components, the building blocks and providing application developers the ability to consume pretrained models,” Vemuri said.

These tools are being offered on demand, so enterprises only pay for what they use, which makes the overall ecosystem more compelling. As the industry sees the growth of two broad categories of AI-related services, machine learning as a service (MLaaS) and automated machine learning tools, providers are starting to roll out their own versions of automated machine learning tooling, such as Google Cloud AutoML, Amazon SageMaker Autopilot and Watson Studio AutoAI. In addition, automated machine learning capabilities can also be provisioned on top of popular open source packages, like auto-sklearn and MLBox, as well as cloud services provided by companies like DataRobot and H20.ai.

There have also been changes in ease of use, and better integration upstream with data wrangling and downstream with model serving capabilities. The aim of cloud platforms and tools are to get better at supporting the entire AI pipeline.

“Even until fairly recently, [automated machine learning] and MLaaS tools were more stand-alone tools that teams would use where data had to be moved in and out, whenever they had to build models and run experiments,” Vemuri said.

Deploying machine learning

Deploying AI and cloud computing for machine learning can enable more enterprise adoption without expert data scientists. Jeff Fried, director of product management at InterSystems, sees many enterprises using machine learning to find a sweet spot in self-tuning applications. Instead of having to analyze and tune the parameters of memory use, operations can use machine learning to find the right parameters and adjust them dynamically.

“Not only can you eliminate the analysis work, you can deploy without necessarily engineering for peak load, knowing that the system will adjust as needed,” Fried said.

Another application is in anomaly detection. Operational systems create a huge amount of data, and it’s hard to figure out what to pay attention to. Developers can use machine learning to identify unusual events — for example, a transaction or user load that is much higher than expected or a CPU utilization that could correlate with future issues.

“Trying to do that with traditional models and thresholds is a ton of work, especially because things vary a lot normally with time of day, weekends, holidays or seasons. [Machine learning] makes it much easier for DevOps,” Fried said.

Fried believes that many enterprises will have to navigate various cultural challenges to make this democratization work in practice. Developers and test teams have traditionally worked with an expectation that the behavior of applications in production is reproducible in a test environment. But machine learning applications can change behavior in production for reasons that may be opaque to the developer and may be difficult, if not impossible, to reproduce in a test environment. As a result, development teams adopting AI in the cloud will need to learn how to deal with scenarios that can only be adjusted and not necessarily debugged, Fried said.

Better workflow is required to succeed in the cloud

For companies to get the most out of cloud computing for machine learning services, they will need to improve their process of provisioning and updating machine learning models, said Alicia Frame, lead project manager of data science at Neo4j, a graph database tools provider.

Similar to what many enterprises have already done for DevOps, development teams need to figure out the right level of services. Although cloud AI services make it easier to augment the amount of capacity for a given workload, if you provision too much, you end up paying for more than you need, and if you provision too little, the application might crash when it gets popular.

Developers also need to start thinking about the process of updating models — teams need to decide how often a model should be retrained based on time or in response to degradation in its performance.

Both processes of provisioning and updating require forecasting models to estimate growth trajectories for usage and data and frequent monitoring and alerts to make sure you upgrade your infrastructure or retrain your model before it’s too late.

“It’s meta — you need machine learning to keep your machine learning models current and performant,” Frame said.

Focus on the solution

Although broad cloud-based AI services can simplify many aspects of AI development, it’s important to ensure that developers are using them to solve the right problem. Scott Stephenson, CEO and co-founder of speech recognition tools provider Deepgram, believes that many of these services focus on the technology rather than the appropriate goal.

“It’s possible to get a good result if the problem that companies are trying to solve fits in a narrow window; otherwise, they end up hooking up five services where each of them are unoptimized for their specific needs,” Stephenson said.

This results in teams spinning their wheels, using up engineers’ time and spending millions of dollars on tuition for how to do it right. As a result, he sees many companies move away from these cloud AI services or only use them for basic things. Similarly, cloud AI users need to ensure that the resulting model is easy to deploy into production, said Christian Selchau-Hansen, CEO and co-founder of Formation, which develops software for customizing marketing experiences.

“While it is now much easier to move a model from R&D into production, particularly if you already have the requisite data in the cloud, MLaaS tools are not a panacea,” he said.

Before deploying MLaaS tools, evaluate and solve the last-mile problem for connecting the raw data, the AI models and the production applications seamlessly. If an enterprise wants to create value from machine learning insights with their customers, it needs to invest in tools, as well processes and personnel, that can easily implement those insights into a variety of customer interactions across the customer journey.

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Altran has released a new tool that uses artificial intelligence (AI) to help software engineers spot bugs during the coding process instead of at the end.

Available on GitHub, Code Defect AI uses machine learning (ML) to analyze existing code, spot potential problems in new code, and suggest tests to diagnose and fix the errors.

Walid Negm, group chief innovation officer at Altran, said that this new tool will help developers release quality code quickly.

“The software release cycle needs algorithms that can help make strategic judgments, especially as code gets more complex,” he said in a press release.

Code Defect AI uses several ML techniques including random decision forests, support vector machines, multilayer perceptron (MLP) and logistic regression. The platform extracts, processes and labels historical data to train the algorithm and build a reliable decision model. Developers can use a confidence score from Code Defect AI that predicts whether the code is compliant or buggy.

Here is how Code Defect AI works:

1. For an open source GitHub project, historical data is collected using RESTFul interfaces and Git CLI. This data includes complete commit history and complete bugs history.
2. Preprocessing techniques like feature identification, label encoding, one hot encoding, data scaling and normalization are applied to the collected historical commit data.
3. Labelling is performed on the preprocessed data. The labelling process involves understanding of the pattern in which the fix commits (where a bug has been closed) are tagged for each of the closed issues. After the fix commits are collected, the commits which introduced the bugs are identified by backtracking on historical changes for each file in a fix commit.
4. If a data set contains a very small amount of bug data as compared with clean records, synthetic data is also generated to avoid bias toward the majority class.
5. Multiple modelling algorithms are trained on the data prepared.
6. Once there is a model that has acceptable value of precision and recall, the selected model is deployed for prediction on new commits.

Code Defect AI supports integration with third-party analysis tools and can help identify bugs in a given program code. Also, the Code Defect AI tool allows developers to assess which features in the code should take higher priority in terms of bug fixes.

“Microsoft and Altran have been working together to improve the software development cycle, and Code Defect AI, powered by Microsoft Azure, is an innovative tool that can help software developers through the use of machine learning,” said David Carmona, general manager of AI marketing at Microsoft, in a press release.

Code Defect AI can be hosted on premises as well as on cloud computing platforms such as Microsoft Azure. The solution can be integrated with other source-code management tools as needed.

AI employee joins the dev team

In a new report about artificial intelligence and software development, Deloitte predicts that more and more companies will use AI-assisted coding tools. From January 2018 to September 2019, software vendors launched dozens of AI-powered software development tools and startups working in this space, and raised $704 million over a similar timeframe.

The biggest benefit from these platforms is efficiency, according to Deloitte analysts David Schatsky and Sourabh Bumb, the authors of “AI is helping to make better software:”
“The benefits of AI-assisted coding are numerous. However, the principal benefit for companies is efficiency. Many of the new AI-powered tools work in a similar way to spell- and grammar-checkers, enabling coders to reduce the number of keystrokes they need to type by around 50%. They can also spot bugs while code is being written, while they can also automate as many as half of the tests needed to confirm the quality of software.”
This capability is even more important as companies continue to rely on open-source code.
The Deloitte report can speed up the coding process significantly by “reducing the number of keystrokes developers need to type by half, catching bugs even prior to code review or testing, and automatically generating half of the tests needed for quality assurance.”

According to the report, these tools are best suited for these elements of the software development process:

1. Project requirements
2. Coding, review and bug detection, and resolution
3. More thorough testing
4. Deployment
5. Project management

The post Machine learning tool trains on old code to spot bugs in new code appeared first on Artificial Intelligence.