1. Visit the Android Studio Website: Go to the official Android Studio website at https://developer.android.com/studio.

2. Download Android Studio: On the website, you’ll see a big green “Download Android Studio” button. Click on it.

3. Accept Terms and Conditions: You might be prompted to review and accept the terms and conditions of use. Read through them and if you agree, proceed.

4. Download the Installer: Click on the download button for the package you selected. The download should start automatically.

5. Once the download is complete, open the downloaded file.

6. Follow the installation instructions provided by the setup wizard.

7. Once the installation is complete, you can launch Android Studio by double-clicking on the desktop shortcut or searching for it in the Start menu.

That’s it! You’ve successfully downloaded and installed Android Studio on your Windows 10 system. Now you can start building amazing Android applications.

The post How to download android studio in windows 10 appeared first on Artificial Intelligence.

Software development Life cycle (SDLC) is a process of producing high-quality software at the lowest cost and in possibly less time. Generally, SDLC has well-tested and ready-to-use phases which provide an organization to help in creating high-quality software. ISO/IEC 12207 is an international standard of software life cycle process. This standard defines all the tasks which need to develop and maintain software. SDLC targets to produce high-quality software by meeting the expectations of clients within the time limit and in budget. It is made up of a plan which describes how to develop, maintain, alter, and improve the software.

Why we need SDLC

Basically, SDLC is a method with the process, which helps in creating high-quality software. By this you can understand the whole criteria of producing effective software, that’s why SDLC is important. Without SDLC you can’t create a standard software because it gives a standard way to produce an effective and efficient software that will run in the market with client expectations which will help him in managing his part of work. With time we update the software as per customer feedbacks to get a better result which is also a part of SDLC.

Benefits of the Software Development Lifecycle

• Forms the base for project planning.
• Helps to estimate cost and time.
• It gives the clarity of the project and the development process.
• Enhance the speed and accuracy of development progress.
• Minimizes the risks and maintenance during the project.
• Its given standard improves client relations.
• SDLC implement checks to ensure that the software is well tested before being installed in greater source code
• Developers can’t move to the next step until the prior one is completed by SDLC.

What are the SDLC phases

Software Development Life Cycle Models

Waterfall Model:-

A little too old and harsh model is the Waterfall model. It is one of the old-fashioned SDLC models that is not much preferred in the modern software development ecosystem.

The reason it is not favored much is that it runs on a very inflexible structure conditioning that the entire set of requirements should be laid down from the very beginning of a project. This limits the freedom and flexibility of the actual design and development of software.

After completing the development, the product goes through the test for meeting its initial requirements. If it is not good enough, it is to be restructured, which is a lot of work.

Usually, software development companies resist dealing with Waterfall though it still seems to be an effective model for the handful of projects.

RAD Model:-

The rapid Application Development (RAD) process is an adoption of the waterfall model. It aims to developing software in a short period. The RAD model is based on prototyping and iterative development with no specific planning involved. The process of writing the software itself involves the required planning for developing the product. The RAD model is based on the concept that a better system can be developed in less time by using focus groups to collect system requirements

• Business Modeling
• Data Modeling
• Process Modeling
• Application Generation
• Testing and Turnover

 Spiral Model:-

The spiral model is a risk-based process model. This SDLC model helps the group to adopt elements of one or more process models like waterfall, incremental, etc. The spiral technique is a combination of fast prototyping and concurrency in design and development activities. The following will explain the typical uses of a Spiral Model –

• When there is a budget compellable and risk evaluation is important.
• For intermediate to high-risk projects.
• Long-term project commitment because of probable changes to economic priorities as the requirements change with time.
• Customer is not sure of their requirements which is ordinarily the case.
• Requirements are complicated and need evaluation to get clarity.
• Some changes are expected in the product during the development cycle.

V-Model :-

In this model execution of processes happens in a sequential method in a ‘V-shape’. It is also known as ‘Verification and Validation model’. The V-Model is an Expansion of the waterfall model and is based on the association of a testing phase for each related development stage. That means for every single phase in the development cycle, there is a directly associated testing stage. This is a disciplined model and the next phase starts only after completion of the previous phase.

Incremental Model :-

The incremental model is not a distinct model. It is radically a series of waterfall cycles. The requirements are divided into groups at the initial stage of the project. For each group, the SDLC model is adhered to develop software. The SDLC process repeats with each release adding more functionality till all requirements are met.

use of  Incremental Model:-

• When the requirements are much superior.
• A project has a lengthy development program.
• When Software team are not well skilful or trained.
• When the customer demands an immediate release of the product.
• You can develop precedence requirements first.

Agile Model :-  

The agile model is a model which promotes continuous interaction of development and testing during the SDLC process of any project. The agile model is a combination of iterative and incremental process models with aims on process and customer satisfaction by continuous delivery of working software products. Agile Methods have divided the product into small incremental builds. These builds are issued in iterations. Each iteration lasts from typically one to three weeks. Every iteration involves cross-functional teams working together on various areas like

• Planning
• Requirements Analysis
• Design
• Coding
• Unit Testing and
• Acceptance Testing.

At the end of the iteration, a functional product is displayed to the customer.

Iterative Model :-

In the iterative model, the iterative process starts with the implementation of a small set of software requirements, makes  enhancements in the evolving versions till the complete system is implemented and ready to deploy on the market. In this model development of the life cycle doesn’t start with full requirements, instead, it begins with the implementation of just a part of the software, which will be reviewed to identify further requirements later. This process is repeated till the new version of the software is produced at the end.

Big bang model :- 

The big bang model comprises focusing all types of possible resources in software development and coding with little bit or no planning. This model works best for small projects with the smaller size development team who works together. It is useful in academic software projects as well. It is also an ideal model where requirements are either unknown or a final release date is not provided.

Advantages of the Big Bang Model

• This is very easy to use model
• Little bit or no planning required
• Easy to handle
• Very few resources are needed
• provides flexibility to developers

Disadvantages of the Big Bang Model

• Very High risk & uncertainty.
• Not a good model for difficult and object-oriented projects.
• Poor model for long-term and ongoing projects.
• Can become very expensive if requirements are not properly understood.

Prototype Model :-

The prototype model starts with the gathering of required information to start the development process. In this the developer meets the client, understand the purpose of software and identify the actual requirement. Then a quick design is created, focused on each aspect of the software which will be visible to the user. Then it goes ahead with the development of prototype, customer checks and try to identify if any modification needs to be done. In this step, looping occurs and better versions of prototype are created. It continuously happens being in touch with client to show him if any further requirements needs to be done.  This process remains continue till the user is satisfied. Once the user is satisfied, the prototype is converted into the actual system to deploy in market.

DevOps:-  Let’s understand. In the agile Model, both Development and testing activities were concurrent, unlike the waterfall model. It was lost on practices that didn’t come up to speed with agile practices. Due to lack of collaborations between developers ad the operations team, slow down the development process and releases. Then software companies started realizing the need for better collaboration between teams and faster delivery of software. It gave birth to the DevOps approach. DevOps enabled fast software delivery with minimum problems to fix and faster resolution of problems. The term DevOps is deprived of two words development and operations. DevOps is a practice that allows a single team to manage the whole application development life cycle, i.e. development, testing, deployment, etc. The aim of DevOps is to shorten the development life cycle. DevOps is a software development approach that helps in producing high-quality software with reliability and in less time. DevOps is a software development method that aims at communication, integration, and collaboration between IT professionals to enable continuous deployment of products.

Which SDLC Model is Best

As far I have understood, DevOps is the best model in today’s software ecosystem which provides a better development life cycle with high effectiveness and efficiency in work progress.  But it doesn’t mean rest models are not useful, they are also useful and still getting used by some organizations who feel that model is best in their work. DevOps is a practice of bringing development and operation teams together whereas, Agile refers to the continuous iterative approach, which aims at collaboration, customer feedback, small, and continuous releases. DevOps’ purpose is to manage end-to-end engineering processes. It helps in increasing an organization’s speed to deliver applications and services.  The agile purpose is to manage difficult projects. The agile development process divides the product into smaller pieces and integrates them for final testing. It can be implemented in many ways, including Scrum, XP, etc.

The post Software Development Lifecycle (SDLC) Beginners Guide appeared first on Artificial Intelligence.

Chief Technology Officer at Unit4, overseeing development of intelligent software for service organizations.

There’s a lot of hype around machine learning, but what does it really mean in the context of enterprise software? How does it work, where is it adding business value today, and what should we expect from it in the future?

Let’s start with some definitions. Artificial intelligence (AI) is an umbrella term that includes machine learning (ML), deep learning and cognitive learning. The part most relevant to enterprise software is ML, which in this context is the ability to create automation through AI algorithms.

A lot of what ML does is really just statistical analysis: crunching numbers, measuring parameters, identifying patterns and projecting future outcomes based on past results. You don’t actually need fancy ML algorithms to do this; you can do it with standard logical programming.

The degree to which the ML itself improves business outcomes is currently marginal. The accuracy of a financial forecast, for example, is sensitive to far greater factors than whether the algorithm can refine itself slightly over time. If you haven’t got harmonized, accurate and complete data to start with, simply applying ML to it isn’t in itself going to result in better business decisions.

Realizing the Growth Potential of AIArtificial Intelligence Is Learning To Categorize And Talk About Art

A Solution Looking For A Problem?

In terms of Gartner’s hype cycle, ML is currently at the peak of inflated expectations. You cannot simply throw ML at a bucket of big data and expect it to magically come up with a perfect business plan.

As so often in business, you shouldn’t start with the technology itself. Before you think about where to apply ML, you need to step back and ask: What is it we’re trying to achieve?

Look for points in your business processes where some sort of judgment or prediction is required and where any small improvement in accuracy would have a disproportionate benefit to the business. These are the potential use cases for ML. Otherwise, ML is at risk of becoming a solution looking for a problem.

For example, if you apply ML instead of conventional statistics — and you have good underlying data — you should be able to continuously enhance the accuracy of the predictions to improve, say, operational efficiency and customer experience.

Where Is ML Adding Value Today?

ML is currently being used to good effect in enterprise software to automate routine business processes.

Receipt recognition: In this use case, an ML algorithm examines a scan of a receipt and deduces what type of receipt it is, then automatically matches it against an expense record in the ledger.

Smart invoice processing: Here, the ML algorithm examines a scanned paper invoice or electronic invoice and identifies the key elements: invoice number, customer number, amount, payment terms and line items, then matches them against the relevant purchase order or delivery note.

Time sheet completion: Typically, there are around five dimensions to completing a time sheet — project, task, level of resource, type of work and time spent — all of which, until now, had to be input manually. An ML algorithm can auto-populate them based on previous patterns. This can free up a lot of time for people and make work easier for them.

The Human Intelligence In AI

A great deal of human intelligence is required to get AI to work. To get predictable, reliable results, you have to decide the use case and make sure the data itself is of a high enough quality before setting the algorithm a task. Then you have to train it.

In its simplest form, training an algorithm involves a person checking the results and providing feedback on their accuracy. This is called supervised learning.

The human mind is by far the best pattern-matching machine in the universe. The average 2-year-old can probably correctly identify a cat after it’s seen two or three, while an ML algorithm might need to see 2,000 before it can be sure. But, once trained, ML excels at dealing with huge volumes of data and processing it very quickly, while never getting bored performing repetitive, tedious tasks day in, day out.

What Can We Expect Next?

Based on my experience, typically less than 20% of business processes are automated in enterprise software. I believe that in as little as two to three years we could see up to 80% of routine business processes automated by ML.

The current frontier is about how we interact with software, and there’s an ongoing paradigm shift around user experience. As in the book, The Best Interface is No Interface, which calls for an end to “screen-based solutions,” software should be able to recognize human speech through natural language recognition; ML is now making this a reality.

The next big leap forward will be to eliminate humans from some business processes entirely. In a workflow, at the point where a person has to approve something, an ML algorithm will examine an approver’s behavior and learn what falls within the usual tolerances. By copying the person’s judgments, ML can carry out the work itself and simply inform the user when it’s done.

Focus On What Matters

The promise of ML in enterprise software is to make it pervasive but not visible. Will it put us out of our jobs? No, but it will allow us to offload the hum-drum, low-value tasks and focus on what really matters — adding value to the business.

The post The Truth About Machine Learning In Enterprise Software appeared first on Artificial Intelligence.

Analytics Insight provides a glimpse of top Data Science software available in 2021.

The data explosion in the tech-driven world has instigated reputed organizations to utilize useful Data Science software to transform multiple sets of complex data into meaningful business insights against competitors worldwide. The emergence of the data-driven culture has provided a plethora of job opportunities in the field of Data Science where the professionals implement data visualization tools for effective data management. Data management is a long process of cultivating real-time data into in-depth outcomes with predictive analytics. Let’s look at some of the top Data Science software available in 2021 to boost productivity efficiently.

Top Data Science software you must know in 2021

Tableau

Tableau is one of the popular Data Science software that guides users to have a better understanding of the complex raw data. The visual analytics platform helps in data visualization to solve complex problems efficiently. It also provides unlimited data exploration without any disturbance in the flow of data. This predictive analytics help in data management to enhance customer relationship for the long term. There are six types of products that Tableau offers to the users— Tableau Desktop, Tableau Server, Tableau Online, Tableau Prep, Tableau CRM, and Tableau Public.

BigML

BigML is an accessible and consumable platform that brings machine learning close to the users for exploring the hidden predictive analytics of complex data efficiently. Users can build sophisticated machine learning solutions by implementing the predictive patterns from complex sets of data.  BigML also helps to automate regression, time series forecasting also topic modeling tasks with end-to-end seamless transformation. It allows interactive data visualization and to export visual charts to smart devices. There are multiple BigML products for appropriate data management like BigML Bindings, BigMLer, BigML for Alexa, BigML Zapier App, BigML-GAS, BigML PredictServer, and BigMLX.

MATLAB

MATLAB is one of the well-known Data Science software that helps to analyze data, develop algorithms as well as create models for organizations. The code is always production-ready for users to integrate it with data sources and business systems.  This platform provides data visualization with interactive graphics and customizable functions. MATLAB helps data scientists inefficiently data management with vector graphics files such as PDF, EPS, and PNG as well as deep learning algorithms.

Apache Spark

Apache Spark is one of the most used Data Science software that provides a powerful predictive analytics engine for data management and stream processing. It has a record of achieving high performance with a DAG scheduler, a query optimizer as well as a physical execution engine. It allows data scientists to get access to complex data for machine learning, SQL, and so on. Apache Spark offers multiple machine learning APIs that are programmable in R, Python, Java, Scala, and many more.

Jupyter

Jupyter is known as a non-profit and 100% open-source project that supports Data Science across all programming languages such as Python, R, Scala, Julia, and other forty languages. Jupyter Notebooks can be shared with email, Dropbox, GitHub and the codes can generate interactive output like HTML, images, videos, LaTeX, and so on. Data scientists can utilize this powerful 3D data visualization tool known as K3D-Jupyter to perform effective data management to create predictive machine learning models.

TensorFlow

TensorFlow is a popular end-to-end open-source machine learning platform that provides a flexible ecosystem of data visualization tools, libraries, and other community resources to deploy machine learning applications. It provides a variety of applications like speech recognition, image and language generations from complex sets of data to the data scientists.

RapidMiner

RapidMiner is known as one of the best Data Science and machine learning end-to-end platforms that provides an integrated platform for appropriate data management to drive revenue. It consists of multiple products like RapidMiner Studio, RapidMiner Go, RapidMiner Notebooks, and RapidMiner AI Hub for data scientists to generate business insights. RapidMiner allows data scientists to automate predefined connections as well as to use built-in templates for repetitive workflows. It also removes the complexity of data preparation and machine learning for data scientists.

LumenData

LumenData is a master in data management that transforms complex sets of large-scale data into viable opportunities. It helps organizations to modernize approaches to data management. It brings the technical know-how to enhance the data quality and implement solutions in clouds.

The post TOP DATA SCIENCE SOFTWARE YOU MUST KNOW IN 2021 appeared first on Artificial Intelligence.

AI has demonstrated its potential in the field of healthcare, education, space, crowd management, and even in reading and interpreting human emotions. AI researchers and experts are experimenting with the limits of artificial intelligence and its potential in penning down poetry.

A recent survey on literature in the category of machine learning and AI learning has revealed a steady progression in developing techniques to automate poetry generation.

AI Researchers are Farsighted about AI in Poetry Making

“Her eyes, twin pools of mystic light,
Forever in her radiance white—, 
She sought the bosom of the Night. 
Away it came, that mystic sight!”

— Anonymous human writer in collaboration with a poetry algorithm.

This poem was created by a machine in collaboration with a human poet. It is a Google software program known as Verse by Verse. Verse by Verse takes the first line of poetry as input and produces the subsequent lines following a definite rhyme.

Although AI algorithms have successfully generated poetic results, the quality of the poetry is fairly mediocre. AI’s attempt to generate poetry has met with resistance and indignation from authors, poets, and critics. They opine that it is impossible for AI to touch the most sublime emotions and moods that humans possess.

A Glimpse of Verse by Verse

The Google software, Verse by Verse is demonstrating abilities in producing a text by learning and interpreting a corpus of poetry by twenty-two different poets. On the website of the program, users are invited to choose their ‘muses’, three poets, and create the first line of the poetry. The software pens down subsequent lines.

Users of Verse by Verse are unsatisfied with the results. However, the software is welcomed by academicians and institutions as it ushers a new dimension to machine learning and artificial intelligence as a whole. Verse by Verse utilizes language models and programs that use machines to establish a statistical representation of how words fall beside each other to form a coherent sentence. The paradigmatic language model that the software uses is GPT-3. GPT-3 is a program launched by an OpenAI startup in San Francisco in 2020. GPT-3, the language model has demonstrated its poetic abilities by re-writing a phrase by Franz Kafka.

Verse by Verse is now used to read and understand Shakespeare’s couplets. Results on this have not been disclosed yet.

The post CAN AI-POWERED VERSE BY VERSE SOFTWARE BE THE NEXT WORDSWORTH? appeared first on Artificial Intelligence.

The system, developed by digital health startup Cognoa, uses information from questionnaires and videos to help pediatricians diagnose autism. It received marketing authorization from the FDA on Wednesday.

In a first, the Food and Drug Administration gave the green light to an algorithm designed to help clinicians diagnose autism in young children. Developed by Palo Alto-based startup Cognoa, the software uses questionnaires from parents, clinicians, and home videos to make a recommendation to assist pediatricians with diagnosis. 

The goal is to identify autism spectrum disorder (ASD) earlier. On average, most kids in the U.S. are diagnosed around age 4. 

“Many of these children are waiting for long periods of time before they get in (to a specialist),” Cognoa CMO Dr. Sharief Taraman, a pediatric neurologist, said in a Zoom interview. “This is a really big deal. We have not had a diagnostic of this kind getting market authorization.” 

Taraman said the software uses machine learning to identify “maximally predictive” features from the questionnaires and two short home videos 

Of course, asking people to provide videos of their kids is very personal. He said families have to give permission for videos to be reviewed by video analysts and the physicians involved in their care.

The FDA’s authorization was based on results from a prospective, double-blinded study that compared how well the software performed in helping diagnose autism compared to a panel of clinicians making a diagnosis based on DSM-5 criteria. Cognoa went through the FDA’s de novo pathway for low- or moderate-risk devices that don’t have a predicate. 

It was evaluated on 425 kids ages 18 months through five years, across 14 different sites. Taraman said the company also made a point to recruit a diverse group of patients for the trial, in terms of race, ethnicity, gender, education and socioeconomic status. Currently, girls and minorities are often diagnosed with ASD at a later age. 

According to the FDA, Cognoa’s test yielded a false positive result in 15 out of 303 kids in the trial without ASD. Meanwhile, it yielded a false negative in just one of the 122 kids with ASD. 

In cases where there wasn’t a clear diagnosis or a rule-out, the algorithm gave an indeterminate result. In total, it provided a diagnosis for about 32% of patients in the trial. 

Having the ability to give an indeterminate result was important, Taraman said, that way the algorithm wouldn’t yield too many false positives, or overlook kids who have other neurodevelopmental conditions that need to be addressed. 

“Technology’s always a tool. It should never be a replacement for a clinician,” he said. “The test is not meant to be a standalone.”

 Cognoa plans to begin marketing the software, called Canvas Dx, later this year. 

“Autism actually is a beautiful thing,” Taraman said. “Our goal is not to ‘turn off’ autism; our goal is to address challenges that come with autism.” 

The post FDA authorizes machine learning software to help diagnose autism appeared first on Artificial Intelligence.

Having worked as a software developer and with software developers for over a decade now, one of the things I have learned to appreciate is just how much developers dislike inefficiency. Anything we can do to automate our jobs and make them faster and easier will inevitably be done. Think back to how much work it was to build and host your own website a decade ago versus now. The manual builds and deploy steps have been gradually replaced with automated builds, testing, and deployments across multiple environments with fantastic scalability. 

As a technology moves along the hype cycle into maturity, frameworks, tooling, and methodologies rise and fall until we begin arriving at the things that truly make technology useful and efficient. Machine Learning (ML) has seen an explosion of development in the last few years and shows no signs of slowing. Just as in other software development areas, machine learning is beginning to find its stride in the development track, making it much more accessible than ever before, thanks to MLaaS.

What is Machine Learning as a Service (MLaaS)?

So, what is machine learning as a service? Simply put, MLaaS is when you use someone else’s tooling and infrastructure to enable machine learning development or deployment, usually at a price. MLaaS is a more specific version of Software as a Service (SaaS). In the olden days, if you wanted network storage, you bought or built a server, put it in a server rack, and attached it to your network. Now you can pay to use someone else’s server and let them handle redundancy, scalability, and maintenance, so you don’t have to.

Using these other servers is where much of the efficiency in as-a-service offerings come from; they help customers accelerate solutions. Economies of scale often make this solution faster to set up, easier to maintain, and generally more cost-effective over time. In the same way, if you wanted to do machine learning development a few years ago, you had to jump through several hoops. First, you needed to hire a machine learning expert. Second, put $2000+ into a high-end GPU-packed Linux box. Third, try to piece together several disparate frameworks and tooling, hoping you didn’t have any conflicting dependencies. Lastly, wrangle your data into some custom format until you could get a model training. While that may still be the right solution in some cases, for many, we have much better options now.

ML Services

Not surprisingly, the most prominent players in the cloud computing industry are also some of the most prominent MLaaS space players. Amazon Web Services, Google Cloud Platform, and Microsoft Azure are updating and releasing new and improved machine learning tooling at a lighting fast pace.

The types of services that these cloud providers offer include:

• Virtual machines for training models
• Data storage
• Data versioning
• Data labeling/ground-truthing tools
• Hosting options for models
• Pre-trained models for deployment such as:
  • Models for fraud detection
  • Models for detecting various objects in images
  • Models for doing sentiment analysis on text
  • Recommendation engines
  • Anomaly detection
• Development environments for data scientists and software developers

On top of these general offerings, we see a surge in particular offerings targeted at use cases in certain industries. One example is Amazon’s Lookout for Equipment. This offering is targeted at the industrial sector, which has generally struggled with adopting machine learning. This industry’s struggle is partly due to the lack of experts available to get companies started and the high cost of entry into the ML space. Specific services like these reduce the need for in-house expertise, lower the barrier to entry, and start at a low cost. AWS has gone so far with this that they offer devices, such as Monitron, that work with their cloud infrastructure to reduce these barriers to entry further. 

Along with the big names in cloud computing, we see very specialized companies entering this space and providing solutions that were hard to imagine 5 years ago. One great example of this is Edge Impulse. They are focused on bringing machine learning to edge devices, which has traditionally been incredibly difficult and required both a high level of expertise in embedded systems and machine learning. With their services, what used to take weeks of development time can be reduced down to days or even hours. 

With these types of technologies, it is no wonder that companies are further embracing machine learning into the future. A recent article in Forbes highlights some of the significant shifts in the industry and points to some of the challenges for ahead companies. 

With everyone scrambling to get a piece of the pie, it can leave companies with a lot of questions, including:

• Should I use a service platform or do the work in-house?
• If I do use a platform, which one is the best?
• Should I use a pre-canned specific solution or do something more general?
• How expensive is all of this?

While we can’t answer every question in this post, let’s look at a few high-level things to consider.

How to Get Started with MLaaS

There are a few high-level trade-offs to be considered.

• Generally, an MLaaS solution improves speed but tends to decrease flexibility in frameworks, versions, or the ability to adapt and tweak models to get the best solution.
• Depending on the amount of training you need to do, sometimes building in-house infrastructure may be a cheaper option.
• While MLaaS solutions tend to improve the speed of getting started, they can also be slower during actual development due to the large amounts of data moving around the cloud.
• Some solutions will promise the world but need to be vetted by someone with some machine learning experience and domain knowledge of your problem. Be wary of silver bullet sales pitches.
• Make sure you are considering the full machine learning process. If the service doesn’t work with the way you collect and store data, that is a problem. The ability to easily deploy a trained model from this service is an important consideration. If you have no way to monitor your model’s performance, that can be a significant issue for your solution’s long-term success.

There are also a few high-level questions to answer to help decide how to move forward. 

How Unique Is the Problem?

Some problems in machine learning are pretty well understood and have solutions to them. If you are looking to find people in an image, implement fraud detection, or recommend products to a user, you can probably find something off the shelf that will help you accelerate your solution quickly. However, if you work in a unique domain, such as optimizing feeding patterns on your grasshopper farm, you may struggle to find a solution that cleanly fits your needs. The more specific the service offering is, the more closely your problem will need to match it. More general services, such as using Amazon SageMaker to create your model from scratch, will take more time and expertise but will ultimately be more flexible.

Is In-House Technical Expertise Available?

If you have a team of data scientists and developers already working on the problem, their expertise may be able to provide better solutions at a lower cost than trying to move to an MLaaS solution. This is particularly true if the problem you are trying to solve has many nuances or needs a lot of flexibility. Often, an in-house expert will be able to quickly assess a service to know if it will work in your particular environment. If you don’t have this expertise, it will likely be wise to engage a third party to evaluate the right solution.

Is There In-House Infrastructure?

If you already have many in-house infrastructures to support data storage, training, and deployment, it is probably worth leveraging that. However, if you want to integrate some of this into other machine learning services, be careful about which tools allow external integration types. This can be a major headache, even if a solution claims that it offers 3rd party integration. Many times, these integrations can be cumbersome, buggy, and fragile. 

Cloud or Edge: Where Should the Model Run?

This question is going to drive a lot of decisions. Generally, running models in the cloud is easier. However, it comes with a lot of limitations that may or may not be an issue. For instance, if you have a model that inspects the quality of a part on a manufacturing line, you may not have enough time for data to be collected, sent up to the cloud, processed, and sent back while still maintaining your cycle time. If this is a safety-critical application, you can’t depend on a wireless connection all the time to get results. While cloud providers are working hard to make sure their services can move into this domain, it may be better to find tooling specifically targeted to your edge application. 

How Sensitive Is the Data or Application?

If you are working with highly sensitive data, this needs to be a significant consideration on how you choose to work with machine learning. Cloud platforms are becoming more and more secure and providing better options for end-to-end security than ever before. However, anytime data moves from one location to another, there is always increased risk. Each service being considered should be carefully scrutinized to know if or how it should be used in your scenario.

Will the Problem Statement Change Significantly in the Future Roadmap?

Rarely do you train up a model that will work perfectly from now into eternity. Inputs change. Business problems change. Customer’s needs change. Tying yourself to a very specific machine learning service might work great today but could be a hindrance down the road. Although we can’t predict the future, having a good roadmap of where you think your product or problem is going can help you make informed decisions today. 

Once you’ve answered some of these questions, you are ready to explore the options that are out there. Keep in mind that there will be trade-offs with any service that you use. Understanding what you are gaining and what you are losing is key to finding the right solution.

Set Yourself Up for Machine Learning Success

To set yourself up for success in machine learning:

• Adopt a fail-fast methodology. What is the bare minimum you can try to see if a particular service will fit your need? Experiment quickly and move on quickly if things aren’t going in the right direction.
• Take advantage of free tier offerings, trials, and demos. Most machine learning service providers want you to buy their products and try to make the barrier to entry lower through low to no-cost trial periods. Try it out. If you don’t like it, try something different.
• Never trust a machine learning sales pitch. Machine learning can often be a black box that feels like magic. It can often be too easy for a sales demo to cherry-pick the right data to make their service look even more magical. Whenever you can, try the product for yourself and look for successful real-world use cases.
• Think about your problem holistically. If you are using multiple models, make sure the service you choose will support all of them. If you need other services like monitoring, data storage, or an API to hit a machine learning endpoint, it is probably better to choose a platform that provides all of these things, so you don’t have to learn more technologies and maintain different accounts.
• Try to understand what you don’t know and don’t be afraid to ask for help. If you don’t know how something works, it is better to understand it sooner rather than later. A few hours with an expert consultant could save you thousands or hundreds of thousands of dollars down the road. Know your limits and approach problems humbly.

Learn from Experience

Based on the trends over the last few years and the projections moving forward, I suspect many more machine learning services will hit the market in 2021 and beyond. Some will last. Some will fail. Navigating through all of them can be a big undertaking. Finding the right solution could be just what your business needs to get to market sooner or the golden ticket that sets you apart from the competition. There are risks, but the market is showing that there is also great reward. Picking the right service or set of services will start you off on the right foot and offer much greater efficiency than trying to do it yourself. 

The post What to Know About Machine Learning as a Service in 2021 appeared first on Artificial Intelligence.

Sluttrapport vil legge til analysen av virkningen av COVID-19 på denne bransjen.

Global Machine Learning Software markedet 2021-2025 forskningsrapport er en historisk oversikt og grundig studie av det nåværende og fremtidige markedet for Machine Learning Software-industrien. Rapporten representerer en grunnleggende oversikt over Machine Learning Softwares markedsandel, konkurrentesegment med en grunnleggende introduksjon av nøkkelleverandører, toppregioner, produkttyper og sluttindustri. Denne rapporten gir en historisk oversikt over Machine Learning Software-markedstrender, vekst, inntekter, kapasitet, kostnadsstruktur og nøkkeldriveranalyse. Rapporten videreforespørsler om markedet og vurderer det nåværende landskapet i den stadig utviklende næringslivet og dagens og fremtidige effekter av COVID-19 på Machine Learning Software-markedet.

Få en prøve-PDF av rapporten @ www.industryresearch.biz/enquiry/request-sample/16403642

De viktigste markedsaktørene inkluderer:
Microsoft
Google
TensorFlow
Kount
Warwick Analytics
Valohai
Torch
Apache SINGA
AWS
BigML
Figure Eight
Floyd Labs

Videre inneholder rapporten virksomhetstaktikken til ledende produsenter. Rapporten gir en detaljert analyse av det historiske og dagens scenariet i det globale Machine Learning Software-markedet for å måle vekstpotensialet. Den dekker også alle de avgjørende aspektene ved markedet, inkludert konkurranseanalyse, nøkkelaktører, bruttomarginer, markedsandeler. Avslutningsvis kartlegges oppnåelsen av fremtredende fremdrift, og generelt slutter forskningen annonsert.

På basis av produkt viser denne rapporten produksjon, inntekter, pris, markedsandel og vekstrate for hver type, hovedsakelig delt inn i:
På lokaler
Cloud Based

På bakgrunn av sluttbrukerne / applikasjonene fokuserer denne rapporten på status og utsikter for større applikasjoner / sluttbrukere, forbruk (salg), markedsandel og vekstrate for hver applikasjon, inkludert:
stor enterprised
SMB

Rapporten inkluderer:
– xx datatabeller (vedleggstabeller)
– Oversikt over det globale Machine Learning Software-markedet
– En detaljert nøkkelaktøranalyse på tvers av regioner
– Analyser av globale markedstrender, med historiske data, estimater for 2021 og anslag av sammensatte årlige vekstrater (CAGR) til 2025
– Innblikk i regulerings- og miljøutvikling
– Informasjon om tilbuds- og etterspørselsscenario og evaluering av teknologiske og investeringsmuligheter i Machine Learning Software-markedet
– Profiler av store aktører i bransjen

Spør før du kjøper denne rapporten – www.industryresearch.biz/enquiry/pre-order-enquiry/16403642

Forskningsmål
1. å studere og analysere det globale Machine Learning Software-forbruket (verdi og volum) etter viktige regioner / land, produkttype og applikasjon, historikkdata fra 2015 til 2019 og prognose til 2025.
2. å forstå strukturen i Machine Learning Software-markedet ved å identifisere de forskjellige undersegmentene.
3. Fokuserer på de viktigste globale Machine Learning Software-produsentene, for å definere, beskrive og analysere salgsvolum, verdi, markedsandel, markedskonkurranselandskap, Porter’s fem-kreftanalyse, SWOT-analyse og utviklingsplaner de neste årene.
4. å analysere Machine Learning Software med hensyn til individuelle veksttrender, fremtidsutsikter og deres bidrag til totalmarkedet.
5. å dele detaljert informasjon om de viktigste faktorene som påvirker veksten i markedet (vekstpotensial, muligheter, drivere, bransjespesifikke utfordringer og risikoer).
6. å projisere forbruket av Machine Learning Software-delmarkeder, med hensyn til nøkkelregioner (sammen med deres respektive nøkkelland).
7. å analysere konkurranseutvikling som utvidelser, avtaler, lanseringer av nye produkter og oppkjøp i markedet.
8. å strategisk profilere nøkkelaktørene og grundig analysere deres vekststrategier.

Kjøp denne rapporten (Pris 3160 USD (Three Thousand one Hundread Sixty USD) for en enkeltbrukerlisens) – www.industryresearch.biz/purchase/16403642

TOC nøkkelpunkter:
1 Markedsoversikt
1.2 Machine Learning Software segment etter type
1.3 Markedsanalyse etter søknad
1.4 Oversikt over det globale Machine Learning Software-markedet
1.5 Markedsdynamikk

1.6 Coronavirus sykdom 2019 (Covid-19): Innvirkning på Machine Learning Software-industrien
1.6.1 Hvordan Covid-19 påvirker Machine Learning Software-industrien
1.6.2 Machine Learning Software Konsekvensanalyse – Covid-19
1.6.3 Markedstrender og potensielle muligheter for Machine Learning Software i COVID-19-landskapet
1.6.4 Tiltak / forslag mot Covid-19

2 Global Machine Learning Software markedskonkurranse fra produsent

3 Analyse av viktige industriprodusenter Machine Learning Software
3.1 Bedrifter 1
3.1.1 Firmainformasjon
3.1.2 Produktinformasjon
3.1.3 Machine Learning Software Produksjon, pris, kostnad, bruttomargin og inntekter (2018-2021)
3.1.4 Hovedvirksomhetsoversikt

3.2 Bedrifter 2
3.2.1 Firmainformasjon
3.2.2 Produktinformasjon
3.2.3 Machine Learning Software Produksjon, pris, kostnad, bruttomargin og inntekt (2018-2021)
3.2.4 Hovedvirksomhetsoversikt
3.2.5 Machine Learning Software Nyheter

3.3 Bedrifter 3
3.4 Bedrifter 4
3.5 Bedrifter 5
……………
4 Global Machine Learning Software markedsstørrelse kategorisert etter region (2015-2021)
5 globale markedssegment av Machine Learning Software etter type
6 Global Machine Learning Software markedssegment etter søknad
7 Markedsanalyse relatert til Machine Learning Software
Fortsatt ………

Detaljert innholdsfortegnelse for det globale Machine Learning Software-markedet –

The post MACHINE LEARNING SOFTWARE MARKEDSSTØRRELSE 2021: BRANSJEANDEL, FREMTIDIGE KRAV, MARKEDSPOTENSIAL, HANDELSMENN, REGIONAL OVERSIKT OG SWOT-ANALYSE TIL 2025 appeared first on Artificial Intelligence.

The Python Integrated Development Environment Ide Software report provides a detailed analysis of the major factors influencing the market growth, including the drivers, restraints, lucrative opportunities, technology advancements, industry-specific challenges, recent developments, and competitive analysis, CAGR, market share, revenue, gross margin, value, volume, industry size, share, growth, segmentation, main trends, standardization, deployment models, strategies, future roadmaps, and Annual forecast for the year 2027 and other key market figures that give an accurate picture of the growth of the Python Integrated Development Environment Ide Software market. 

The report includes an elaborate executive summary, along with a snapshot of the growth behavior of various segments included in the scope of the study. Furthermore, the report sheds light on the changing competitive dynamics in the global Python Integrated Development Environment Ide Software market. These indices serve as valuable tools for existing market players as well as for entities interested in entering the global Python Integrated Development Environment Ide Software market.

The historical and forecast information provided in the report span between 2020 and 2027. To provide a stronger and stable business outlook, the regional outlook has been presented by inspecting several industries across different global regions.

**This market research report was prepared after considering the COVID-19 impacts**

Analytical Insights Included from the Python Integrated Development Environment Ide Software Market Report:

• Estimated earnings rise of the marketplace throughout the forecast period.
• Factors expected to aid the rise of the Python Integrated Development Environment Ide Software marketplace.
• The growth potential of this market in a variety of regions.
• Consumption, pricing arrangement, and adoption routine of this market.
• Company profiles of top players in the market.

The report includes Competitor’s Landscape:

➊ Major trends and growth projections by region and country
➋ Key winning strategies followed by the competitors
➌ Who are the key competitors in this industry?
➍ What shall be the potential of this industry over the forecast tenure?
➎ What are the factors propelling the demand for the Python Integrated Development Environment Ide Software Industry?
➏ What are the opportunities that shall aid in the significant proliferation of the market growth?
➐ What are the regional and country-wise regulations that shall either hamper or boost the demand for Python Integrated Development Environment Ide Software Industry?
➑ How has the covid-19 impacted the growth of the market?
➒ Has the supply chain disruption caused changes in the entire value chain?

enefits of buying the report:

The report is compiled using a vigorous and thorough research methodology. Stratagem Market Insights is also known for its data accuracy and granular market reports.

• A complete picture of the competitive scenario of the Python Integrated Development Environment Ide Software market is depicted by this report.
• The report consists of a vast amount of data about the recent product and technological developments in the markets.
• The extensive spectrum of analysis regarding the impact of these advancements on the future of market growth.
• SMI is keeping a track of the market since 2015 and has blended the necessary historical data and analysis in the research report. Therefore, any additional data requirement can be easily fulfilled.
• The insights in the report are easy to understand and include a graphical representation of the numbers in the form of histograms, bar graphs, pie charts, etc.
• Components such as market drivers, restraints, challenges, and opportunities for the Python Integrated Development Environment Ide Software market are explained in detail.
• It also provides a complete assessment of the expected behavior about the future market and changing market scenario.
• Making an informed business decision is a tough job; this report offers several strategic business methodologies to support you in making those decisions.

Important Features that are under offering & key highlights of the report:

1) Does the study cover COVID-19 Impact Analysis and its effect on Growth %?

Yes, the overall industry has seen quite a big impact due to slowdown and shutdown. The study covers a separate qualitative chapter on COVID-19 Impact analysis. Additionally, it also provides before and after the scenario of COVID-19 on sales growth & market size estimation to better analyze the exact scenario of the industry.

2) How companies are selected or profiled in the report?

A list of some players that are profiled in the report includes,  PyCharm, Eclipse, AWS Cloud9, The Jupyter Notebook, Kite, Codenvy, Selenium IDE, Wing Python IDE, ActiveState, Aptana Studio, Ninja IDE, Kdevelop, Koding, Eclipse IoT, UEStudio, Codeanywhere.

Usually, we follow NAICS Industry standards and validate company profile with product mapping to filter relevant Industry players, furthermore the list is sorted to come up with a sample size of at least 50 to 100 companies having greater topline value to get their segment revenue for market estimation.

3) Can we add or profiled a new company as per our needs?

Yes, we can add or profile a new company as per client need in the report, provided it is available in our coverage list as mentioned in answer to Question 1 and after feasibility run, final confirmation will be provided by the research team checking the constraints related to the difficulty of survey. 

4) Can a specific country of interest be added? What all regional segmentation covered?

Yes, Country-level splits can be modified in the study as per objectives. Currently, the research report gives special attention and focus on the following regions:

⇨ Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)
⇨ Europe (Turkey, Germany, Russia UK, Italy, France, etc.)
⇨ North America (the United States, Mexico, and Canada.)
⇨ South America (Brazil etc.)
⇨ The Middle East and Africa (GCC Countries and Egypt.)

“The report also covers the trade scenario, Porter’s Analysis, PESTLE analysis, value chain analysis, company market share, segmental analysis”

The post Python Integrated Development Environment Ide Software Market Projected to Discern Stable Expansion During 2020-2027 | Key Players: PyCharm, Eclipse, AWS Cloud9, The Jupyter Notebook appeared first on Artificial Intelligence.

Big data in GIS has critical ramifications for how we procure and leverage spatial data

In the midst of the surge of data we gather and fight with consistently, geospatial information possesses an interesting spot. Because of the networks of GPS satellites and cell towers and the rising Internet of Things, we’re able to track and correlate the location of people and items in exact manners that were impractical up to this point. Yet, putting this geospatial information to use is more difficult than one might expect.

It is frequently said that 80% of data has a spatial part. Once in a while it is a coordinate gathered from a GPS application, or essentially an address that gets geocoded to a location along a street centerline. Regardless, it is surprisingly simple to get the location of an item. With moving items, location and time are imperative to follow the article alongside some other applicable attributes (temperature, point, size, shading, and so forth). As sensors and devices become increasingly connected, data is being gathered at an uncommon rate.

The Big data pattern has drastically affected each industry, so it is little amazement that big data in GIS has critical ramifications for how we procure and leverage spatial data. Big data is definitely not a new pattern. Notwithstanding, it is turning into a bigger part of geographic data science.

Maybe perhaps the greatest change in the discussion around big data has been in the relationship between software, hardware, and expertise. One of the foremost utilizations of geospatial big data analytics has been in the humanitarian area. GIS IoT gadgets are currently being utilized across the world to gather information in conditions which were previously hard for aid workers to access and thus hard to work in.

For an illustration of the manner by which geospatial big data analytics can function admirably in this area, consider by DigitalGlobe, a non-profit organization that sources satellite information and coordinates it with different sources like social  media notion and aerial imagery, use a GIS machine learning algorithm to follow activity in explicit areas and identify anomalies.

Geospatial information is not simply an area, nonetheless. Geospatial information likewise tracks how things are connected and where they are in relation to other objects. Realizing how an object changes over the long run corresponding to different items can give critical insights. For instance, how truck maintenance recommendations change depending on where a truck is found and how it is driven in the field? Utilizing all of your data to drive more intelligent maintenance plans sets aside cash, time and assets.

Robots, or unmanned aerial vehicles (UAVs) as the business calls them, have been everywhere on the news of late. What’s more, as you may expect, there’s a big data angle to them, particularly with regards to location intelligence and geographic information systems (GIS) products.

UAVs are emerging as an astounding method to accumulate data from the air. As per the Flightline Geographics auxiliary of ESRI partner Waypoint Mapping, UAVs can capture pictures with goals down to one inch, and convey that data in no time, compared to the days regularly needed by manned aircraft.

A couple of years ago, it was hard to envision how the financial sector and geospatial information would cooperate – there seemed, by all accounts, to be little value to a bank or other financial services company in knowing where their customers traveled and when.

Incidentally, this data is just as valuable to the financial sector as it is in other sectors. Truth be told, geospatial big data in the financial area presently plays a role in the progressing startup boom that plans to bring geospatial analysis procedures to the core of business decisions.

The applications are as yet being explored, however, as of now appear to be encouraging. Geospatial information has already been valuable, for example, in figuring out which branches to merge, as well as how satellite imagery over time can all the more likely foresee a property’s risk of flooding when it comes time to decide insurance rates.

Financial services firms are driving with regards to utilizing GIS and business intelligence tools together. For the financial industry, geospatial big data is playing a part in making a blast of a boom of startup companies. So many financial startups here have been advertising themselves for their capacity to use non-traditional data sources, for example, satellite imagery, for deciding the possible danger of offering insurance or a loan. For instance, satellite imagery throughout a range of time could more readily anticipate a property’s risk of flooding for determining insurance rates.

The post SIGNIFICANT BENEFITS OF GEOSPATIAL INFORMATION AND BIG DATA ANALYTICS appeared first on Artificial Intelligence.