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cybersecurityict · 2 months ago

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AI Voice Cloning Market Size, Share, Analysis, Forecast, Growth 2032: Ethical and Regulatory Considerations

The AI Voice Cloning Market was valued at USD 1.9 Billion in 2023 and is expected to reach USD 15.7 Billion by 2032, growing at a CAGR of 26.74% from 2024-2032.

AI Voice Cloning Market is rapidly reshaping the global communication and media landscape, unlocking new levels of personalization, automation, and accessibility. With breakthroughs in deep learning and neural networks, businesses across industries—from entertainment to customer service—are leveraging synthetic voice technologies to enhance user engagement and reduce operational costs. The adoption of AI voice cloning is not just a technological leap but a strategic asset in redefining how brands communicate with consumers in real time.

AI Voice Cloning Market is gaining momentum as ethical concerns and regulatory standards gradually align with its growing adoption. Innovations in zero-shot learning and multilingual voice synthesis are pushing the boundaries of what’s possible, allowing voice clones to closely mimic tone, emotion, and linguistic nuances. As industries continue to explore voice-first strategies, AI-generated speech is transitioning from novelty to necessity, providing solutions for content localization, virtual assistants, and interactive media experiences.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/5923

Market Keyplayers:

Amazon Web Services (AWS) – Amazon Polly

Google – Google Cloud Text-to-Speech

Microsoft – Azure AI Speech

IBM – Watson Text to Speech

Meta (Facebook AI) – Voicebox

NVIDIA – Riva Speech AI

OpenAI – Voice Engine

Sonantic (Acquired by Spotify) – Sonantic Voice

iSpeech – iSpeech TTS

Resemble AI – Resemble Voice Cloning

ElevenLabs – Eleven Multilingual AI Voices

Veritone – Veritone Voice

Descript – Overdub

Cepstral – Cepstral Voices

Acapela Group – Acapela TTS Voices

Market Analysis The AI Voice Cloning Market is undergoing rapid evolution, driven by increasing demand for hyper-realistic voice interfaces, expansion of virtual content, and the proliferation of voice-enabled devices. Enterprises are investing heavily in AI-driven speech synthesis tools to offer scalable and cost-effective communication alternatives. Competitive dynamics are intensifying as startups and tech giants alike race to refine voice cloning capabilities, with a strong focus on realism, latency reduction, and ethical deployment. Use cases are expanding beyond consumer applications to include accessibility tools, personalized learning, digital storytelling, and more.

Market Trends

Growing integration of AI voice cloning in personalized marketing and customer service

Emergence of ethical voice synthesis standards to counter misuse and deepfake threats

Advancements in zero-shot and few-shot voice learning models for broader user adaptation

Use of cloned voices in gaming, film dubbing, and audiobook narration

Rise in demand for voice-enabled assistants and AI-driven content creators

Expanding language capabilities and emotional expressiveness in cloned speech

Shift toward decentralized voice datasets to ensure privacy and consent compliance

AI voice cloning supporting accessibility features for visually impaired users

Market Scope The scope of the AI Voice Cloning Market spans a broad array of applications across entertainment, healthcare, education, e-commerce, media production, and enterprise communication. Its versatility enables brands to deliver authentic voice experiences at scale while preserving the unique voice identity of individuals and characters. The market encompasses software platforms, APIs, SDKs, and fully integrated solutions tailored for developers, content creators, and corporations. Regional growth is being driven by widespread digital transformation and increased language localization demands in emerging markets.

Market Forecast Over the coming years, the AI Voice Cloning Market is expected to experience exponential growth fueled by innovations in neural speech synthesis and rising enterprise adoption. Enhanced computing power, real-time processing, and cloud-based voice generation will enable rapid deployment across digital platforms. As regulatory frameworks mature, ethical voice cloning will become a cornerstone in brand communication and media personalization. The future holds significant potential for AI-generated voices to become indistinguishable from human ones, ushering in new possibilities for immersive and interactive user experiences across sectors.

Access Complete Report: https://www.snsinsider.com/reports/ai-voice-cloning-market-5923

Conclusion AI voice cloning is no longer a futuristic concept—it's today’s reality, powering a silent revolution in digital interaction. As it continues to mature, it promises to transform not just how we hear technology but how we relate to it. Organizations embracing this innovation will stand at the forefront of a new era of voice-centric engagement, where authenticity, scalability, and creativity converge. The AI Voice Cloning Market is not just evolving—it’s amplifying the voice of the future.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

#AI Voice Cloning Market #AI Voice Cloning Market Scope #AI Voice Cloning Market Trends

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mobileappdevelopmentsolution · 2 years ago

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AI App Development: 5 Easy Steps to Get You Started

Have you ever wondered how smart assistant apps like Siri, Alexa and Google Assistant understand verbal requests so well? Or how apps like Snapchat and Pinterest recognize faces and objects in images?

The secret lies in artificial intelligence (AI)!!

AI refers to simulated intelligence in apps powered by technologies like machine learning, neural networks, natural language processing (NLP) and computer vision.

AI apps use these technologies to learn from data, analyze content, predict outcomes, automate tasks and interact intelligently with users. AI capabilities enable apps to continuously improve and provide personalized experiences not possible earlier.

If you want to build the next cutting-edge mobile app with features like virtual assistant, image recognition, recommendation engine or analytics, integrating AI is essential.

Read on to learn how to get started with AI app development in five simple steps.

Step 1 - Understand The Basics of AI Apps

The first step is developing core knowledge about what AI is, its popular techniques and use cases in mobile apps.

Some key highlights:

Machine learning- algorithms like regression and neural networks uncover patterns from training data to make predictions. ML powers recommendation engines.

Natural language processing (NLP) enables processing and interpreting text data like conversations. NLP is used in chatbots and virtual assistants.

Computer vision involves analyzing visual inputs like images and videos. It enables facial recognition, object identification and more.

Artificial intelligence app development refers to using such AI techniques to add intelligent features into apps.

Here are some common examples of AI apps:

Intelligent assistants like Siri, Alexa and Google Assistant use NLP to understand voice requests and have human-like conversations.

Recommendation systems on apps like Netflix and Amazon use ML to suggest personalized content and products to users.

Snapchat and Pinterest apply computer vision for features like facial filters and object recognition in images uploaded by users.

So in summary, AI apps leverage technologies like ML, NLP and computer vision to understand unstructured data, analyze information and mimic human intelligence when interacting with users.

Step 2 – Choose The Right AI Tools and Frameworks

Once you understand artificial intelligence app capabilities, the next step is choosing the right tools and frameworks aligned with your use case.

Here are some top AI app development options:

TensorFlow- Open-source library for dataflow programming and machine learning by Google. Used across products like Search, Photos and Translate.

PyTorch - Open-source ML library used by companies like Facebook and NVIDIA for computer vision and NLP models using Python.

Microsoft Cognitive Services - APIs and SDKs for vision, speech, knowledge and search APIs by Microsoft. Integrates well into Azure cloud.

IBM Watson - Toolkits by IBM including natural language, visual recognition and personality insights to build AI apps.

Amazon AI- Managed AI services like Lex, Polly and Rekognition from AWS to build conversational and visual apps.

Evaluate options based on your AI app capabilities, technical skillset and cloud platform to choose the right toolkit.

Step 3 – Collect and Prepare Training Data

The next crucial step is gathering quality training data to teach your AI algorithms.

For instance, a chatbot requires conversational data like sample dialogues to learn how to respond properly. A computer vision app needs many labelled images of objects to recognize them accurately.

Follow these best practices when curating training data:

- Accumulate data from reliable sources relevant to your problem statement.

- Clean data by fixing missing values, duplicates, errors and outliers.

- Structure and label data to train supervised ML models.

- Expand the dataset with techniques like cropping/rotating images for computer vision.

- Continuously test models and enrich training data to improve accuracy.

Properly preparing training data is key to developing accurate AI apps.

Step 4 – Build and Integrate Your AI Model

With the fundamentals covered, now you can develop a custom AI app development model for your app's use case.

Follow this model development process:

- Train model prototypes on prepared datasets using frameworks like TensorFlow executing on cloud GPUs.

- Optimize models by tweaking parameters and architectures to improve accuracy metrics like precision and recall.

- Select the best performing model and export it along with dependencies to integrate into app codebase.

- Call model APIs from app code to run AI predictions and return results to display in the app UI.

- Cache model responses to optimize latency and costs.

Be sure to continuously test model on new datasets and re-train regularly to maintain high accuracy as artificial intelligence app data evolves.

Step 5 – Optimize The AI Assistant Experience

The final step is rigorous testing and iteration to refine your AI app. Focus on:

- Performance - Measure and reduce model latency, traffic and costs by code and infrastructure optimizations.

- Scalability – Scale capacity seamlessly by deploying AI models on cloud platforms like AWS SageMaker.

- Accuracy – Regularly test model on new data and re-train to sustain high precision.

- UX- Integrate AI seamlessly into app UI with contextual and human-like responses.

- Security - Anonymize data, encrypt models and ensure transparency.

This comprehensive optimization will refine your AI assistant, chatbot or computer vision app to deliver secure, easy-to-use and intelligent experiences.

Conclusion

Developing AI apps has huge potential but can seem daunting initially. The key is breaking it down into smaller achievable steps:

Learn AI app development fundamentals

Select the optimal AI tools and frameworks

Curate quality training data

Build, train and integrate machine learning models

Optimize the AI app experience

At Consagous Technologies, our AI app developers help companies design, develop and deploy AI-powered mobile apps tailored for their needs.

#AI app development #AI app development service

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human-synthesys-studio-upsell · 4 years ago

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Human Synthesys Studio Upsell

Human Synthesys Studio Review: Who needs to pay a Spokesperson when you have this?

Every marketer knows that use of videos in marketing is ever-growing. And that’s the reason you see so many people now trying to capture this market…so much so that the market now seems to have become saturated.

But along with this exponential growth in the video marketing industry – there’s been another industry growing equally fast, but it has gone unnoticed. Want to tap in and make $750 for a 2-3-minute job?

Every video needs a voice over… talk about Sales Videos, Video Sales Letters, Animated Videos, Explainer Videos, Instagram & FB Marketing Videos, TV Commercials, Podcasts. There’s no doubt big money there to be made.

Automating voice-overs has been tried by applications like Google Wavenet and Amazon Polly – but the creepy sounding robotic voice overs drove away more customers than businesses could afford. ENTER Human Synthesys Studio Review now!

WHAT IS HUMAN SYNTHESYS STUDIO?

Practically everyone who’s on the internet has seen an “avatar” before. They’re the little cartoon people you see all over social media, blogs, and websites of all kinds! But, sometimes you just want to connect with your audience on an even deeper level… and that little avatar won’t cut it…

Well what if you could use REAL humans to act as a hyper-realistic avatar? Now, you can… INTRODUCING Human Synthesys Studio. Real humans, real human voices, saying exactly what you want them to say. All you have to do is type it!

If you need a professional spokesperson for your own videos, or you want to take advantage of the EXPLODING spokesperson service industry – Human Synthesys Studio will save you time, money, and even open lucrative opportunities to profit from the spokesperson service industry…

Introducing the first-ever REAL human spokesperson engine, where you can combine real humans with text-to-speech, and have them say exactly what you want with UNMATCHED life-like quality…

Boost Conversions With Real Humans

No Need To Be On Camera, Ever Again

No Need To Record Your Voice

No Learning Curve, So Easy To Use

Seamless AI And Cloud-Based Video Production

Unlimited Video Asset Editing

Repurpose Videos From One Language To Many (IMPORTANT)

Template Design And Management

Unlock Massive Business Opportunities

With Human Synthesys Studio, you’ll unlock the power of high-end, cutting edge technology: “Humatars”. This new kind of Avatar isn’t really an Avatar at all. They are real human beings who speak and pose for TEN HOURS. then the Artificial Intelligence involved goes to work examining 100,000 videos to get the lip-sync just right.

Humatars are REAL humans with REAL human voices. You type, they speak. Who needs to pay a spokesperson anymore with low-cost Humatars saying what you want, when you want it!

Human Synthesys Studio is a cloud based “Humatar” video creation software. It uses real humans, real human voices, and text-to-speech software to create high quality REAL spokespeople that say whatever you type.

Human Synthesys Studio creates completely life-like human spokespeople, that say exactly what you want them to say. They are not avatars. Instead, we’re calling them ‘Humatars’ and they are the new standard for spokespeople in video. Why use cartoon or video game-like animations, when you can accomplish the same thing with more personable, REAL humans?

And it’s not just being able to use actual humans in your videos, but actual human voices (English only) as well. This vastly expands how you can use Human Synthesys Studio in your business…

Human Synthesys Studio Is Different Than Anything You’ve Ever Seen – Including Google Wavenet and Amazon Polly With SYNTHESYS, You Can Now Create Voice-Overs On Demand!

By leveraging the power of the next-generation Synthetic Speech Technology, a state-of-the-art Instant Text-to-Speech converter, and total control over the tone of the voice over… you’ll get more engagement and higher revenue from your traffic than ever before!

Human Synthesys Studio gives you Real Professional English Voice Actors to convert your script into a highly engaging voice over!

They help you build trust by allowing you to use the same voice for a brand…without having to chase a professional voice-over artist on platforms like Fiverr or VoiceBunny. Boost engagement, reduce bounce rates, get more leads into your sales funnel, and most importantly… earn more profit!

So don’t hesitate to check the next parts of this Human Synthesys Studio Review as I’ll show you how powerful it is!

HUMAN SYNTHESYS STUDIO REVIEW OVERVIEW

Vendor Todd Gross

Product Human Synthesys Studio

Launch Date 2021-May-24

Launch Time 11:00 EST

Front-End Price $47

Bonuses >> CHECK MY ULTIMATE HUGE BONUSES <<

Refund YES, 14 Days Money-Back Guarantee

Product Type Video Marketing

Support Effective Response

Discount 👉VISIT OFFICIAL SITE

Recommended Highly Recommended

Skill Level Needed All Levels

ABOUT CREATORS

Before explaining in details the features of this product, my Human Synthesys Studio Review intends to briefly introduce the vendor. Along with his partner LuAnn Beckman and Oliver Goodwin, Mr. Todd Gross has come up with the idea to compile all of the first-of-its-kind video marketing solution

Previously, he has already been famous for releasing lots of helpful supporting tools, including Mugjam, VidSnatcher, Kaptiwa, etc. Personally, I have nothing to doubt this guy since his launches have been extremely successful in the field of Internet marketing. That is also the reason why I believe that Human Synthesys Studio will be another hit.

Now let’s switch to the next part of the Human Synthesys Studio Review to find out the content of the program!

KEY FEATURES

With Human Studio Synthesys:

“Talking head” video: Human Studio Synthesys platform includes more than 20 high quality avatars that can be used for producing spokesperson videos. They can be used as full profile avatars or circular avatars.

Fast voice-over process: Human Studio Synthesys platform comes prepackaged with the highest quality and variety of synthetic voices. Simply type your text and hear the result in real-time.

Fast corrections and re-edits: with Human Synthesys Studio, every business can create multiple takes and scripts, see how they work and adjust them – all within a few minutes.

Simple to make multi-lingual videos: Once you found a script that works, you can simply translate the script in any of the supported languages (we support 40+ languages) and generate multi-lingual videos. Again, all within minutes.

Human spokespeople and voice over professionals for hire demand top dollar for their services… Now, you can tap into each of these lucrative business opportunities on talent sites like upwork, fiverr, and more:

Human Synthesys Studio Is Packed With Amazing Features…

Text-To-Video: Transform simple text into professional-looking video for the first time ever. Videos can be up to 6 minutes long.

40+ Languages: We currently support 40+ different languages. It’s never been easier to engage global teams and customers in their native language

“Humatars” Options: With Human Synthesys Studio, you have the option of 5 Humatars with Personal OR 10 Humatars PLUS 3 BONUS Humatars with Commercial to use immediately.

Custom Backgrounds: Easily upload any image or video as a background to your videos. We’ve also created free slide templates for inspiration.

Background Music: We have prepared a selection of background music that you can freely add as a background sound to your videos.

MP4 Downloads: Access and download your completed video projects as mp4 files.

Multi-slide Videos: Combine several Human Synthesys Studio videos into one video. Now you can create rich slide deck video experiences in just a few minutes.

Update Video Content: With Human Synthesys Studio, you can simply duplicate the video, change the text and have an updated version of a video in just a few minutes.

Spokesperson Positioning: Move the position of your Humatar around on your videos for your desired position and look.

Intuitive, Easy To Use, Interface: Step-by-step process makes it quick and easy to produce your videos.

And many more other exclusive features:

Find THE perfect Voice for everything: Create engaging learning experiences, training, real estate videos, advertising audio promotion materials, product tours and more.

Cutting-edge technology: Deep learning researchers train a dataset of voice recordings from real-life voice actors to create a neural network. This neural network generates audio clips from text input by users. The voice-over generated sounds 100% real human-like.

Unmatched quality at an unmatched price: With the latest innovations in synthetic digital voicing, and super-fast text-to-speech conversion and render…there’s no other app, software or tool on the market that comes close – at any price!

Higher engagement and lower bounce rate – GUARANTEED: SYNTHESYS improves engagement with visitors. They are not repulsed by a creepy sounding robot telling them about the product or service.

Done-for-you voices: Get started instantly with ready-to-use voice overs. Use these for presentations and videos and save both time and money.

Bring your scripts to life: Professional voice-overs on your own terms, on your schedules and without any haggling for prices. Bring your scripts to life in just 3 clicks.

Never Hire A Voice Actor again: Both Male and Female voices from real-life actors are made available through the neural network. Special emphasis has been given to tone, breathing, sentence pauses etc. to ensure that your voice-overs sound 100% real.

Multi-purpose use: SYNTHESYS can be widely used for Gaming, Podcasts, Film & Animation, Real Estate Promotion Videos, Training Videos, Walkthrough Videos, Advertisements etc. to create professional personalized audio promotional material and more.

Easy-on-the-pocket: This stunning technology is budget-friendly. What’s more, it’s a fraction of the cost of studio-recorded narration. SYNTHESYS help you with a professional voice for your presentations and videos while helping you save time and money.

Easy-to-use: Simply type or copy and paste your script into the text box, make your voice selection, and click to render. Getting a high-quality voice-over has never been easier and faster.

No need to cut corners and compromise on quality: Voice-overs are needed right at the end of building a product and the related marketing material. By this time, most businesses have already over-spent on their budget. But now with SYNTHESYS… no more compromising on the quality of voice-overs to save money. Also, say goodbye to delays caused by script changes, casting talent, and booking recording studios.

Works seamlessly on any device: Access your SYNTHESYS secure dashboard via any web browser. It’s hosted on the cloud – no software installation or downloads required!

👉VISIT OFFICIAL SITE

HONEST HUMAN SYNTHESYS STUDIO REVIEW: IS IT WORTH USING?

This is an absolutely brilliant idea of using custom scripts with “Real People”. And how you implement it online, makes it incredibly easy to create professional spokesperson videos.

Imagine if you had the power to put together Human Spokesperson videos where you could actually direct what the all-human spokespersons say… Imagine how powerful it would be if you could have your own spokespersons on demand” where YOU are able to decide what they actually say!

This brand new form of video creation goes beyond what is currently possible. Finally, you can have real humans delivering the very message you want, line by line

We are talking about having your own professional spokespersons, practically on demand, so that you can build your own video scripts, line by line, by choosing from literally thousands of options, and create professional looking videos, in 90 seconds or less…

We wanted to find a way to truly give you the freedom to create practically any video that you want, using an actual live spokesperson, without having to hire a professional person to record it for you. This will allow you to make incredible, customized videos for your clients.

What is all the fuss about? Well, imagine a bank of thousands of clips that you can automatically choose from that essentially forces our HUMAN spokespersons to say what you WANT them to say!

That’s right! For example you can have Todd Gross as your spokesperson and have him coax someone to opt-into your list about Facebook, or if you want him to be the front-man for a local business… you can use him for that too – and he’ll say what YOU direct him to!

In fact I just made a 30 second Todd Gross product promotional with my feet up and just clicking the mouse in a few minutes. You’ve got a winner that ALL video marketers will love.

The software easily allows you to not only string together full videos by combining clips, but helps you to smooth out the “seams” between each clip, so that the result is exactly the same as if you hired a spokesperson yourself for hundreds of dollars!

You can make as many videos as you want, and as long as you want, although most will run between 30 and 60 seconds, PERFECT for marketing for yourself OR your clients! You see, commercial rights ARE included.

As an example, you can have a video made coaxing folks to find out more about “Facebook Marketing”, and to opt-into your list to learn more! The videos run across dozens of niches including local marketing for offline business as well. You really must see this to believe it!

Let’s take a minute to recap the huge benefits come up with:

World’s #1 Human Spokesperson Software Built By Marketers For Marketers

Select Exactly What Each Professional Spokesperson Says, Line By Line!

Produces Amazing HD Commercials Ready To Sell For $100’s of Dollars Each (Or Use Yourself)

Thousands Of Clips To Choose From. Incredible Amount Of Customization

100% Cloud-Based. Nothing to Install. Ever!

Featuring A Variety Of Spokespersons

Point-and-Click Software For Fast and Easy Video Creation

Dozens Of Videos, Backgrounds, Lower Thirds Graphics, Soundtracks, & More At Your Fingertips

Say Goodbye To Working With Difficult Designers & Spokespersons!

You will be getting the vendor’s greatest bonuses for your fast action (and also my ultimate huge bonuses at the last section of this Human Synthesys Studio Review):

HOW HUMAN SYNTHESYS STUDIO WORKS

Human Synthesys Studio is Newbie Friendly. No special skills, no learning curve required… We have step by step training videos that cover all aspects of the software to get you started right.

Human Synthesys Studio is 100% cloud based and works on any operating platform on any internet connected device. It runs inside your internet browser so you can access it on any computer with an Internet connection. Nothing to install; nothing to update.

It’s Never Been Easier To Create Human Spokesperson Videos That Say Exactly What You Want In Just Minutes

Step #1: Select Your Human

Step #2: Select Your Voice

Step #3: Input The Text You Want Them To Say

Step #4: Change Position, Add Backgrounds, Background Music, And More

Step #5: Render Your Video

Remember that Human Synthesys Studio is credit based. 100 renders for commercial and 50 renders for personal use. Additional video rendering packages are available. On either option, you can type up to 1,000 characters on each slide and create up to 6 slides for a total of up to 6 minutes per video.

We’ve selected the very best Google and Microsoft have to offer, totaling 40 languages (74 different voices). PLUS there are 5 synthetic HUMAN voices available in English language.

Upsells AND PRICE

For a limited time, you can grab Human Synthesys Studio with early bird discount price in these options below. Let’s pick the best suited options for you before this special offer gone!

FRONT-END: HUMAN SYNTHESYS STUDIO ($47)

First of its kind technology

REAL professional actors in your videos

Synthetic human voice text-to-speech (English Only)

PLUS Google text-to-speech (40 languages + 74 voices)

ONLY the BEST from Google and Microsoft have been selected..

Revolutionary lip-synching technology

Stunning life-like facial movements

Cloud based (no installation required)

Extremely easy to use (no learning curve)

Background and audio bank included

Simple editing interface

Tap into the HUGE spokesperson market

Commercial license available

10 Real Humatars + 3 Bonus Humatars (Only ONE Outfit Included)

5 Real Human Voices + ONLY The BEST Google & Microsoft TTS 40+ languages (74 different voices)

100 Total Rendered Videos (1,000 characters/slide; 6 slides; 6 minutes max) Credits Available For Additional Videos.

Upsell 1: PRO EDITION ($67)

Now MAXIMIZE Your Sales Potential By Unlocking The Pro Upgrade… Unlock 40 MORE Humatars With Different Outfits Covering Dozens Of Different Niches, PLUS 30 MORE Human Voices!

Boost Your Humatar Inventory And Appeal To More Clients For Added Sales Potential. Our Pro Upgrade Was Built With ONE Thing In Mind. To Give You What You Need To Land As Many Clients As Possible.

This Is The ULTIMATE Upgrade For Your Human Synthesys Studio Account – Especially For Commercial Owners!

You can more than quadruple the number of Humatars, and boost your voice options for maximum flexibility with Human Synthesys Studio Pro. As a commercial license owner, you’ll have maximum firepower to land more and more clients across dozens of different niches.

Introducing the first-ever REAL human spokesperson engine, where you can combine real humans with text-to-speech, and have them say exactly what you want with UNMATCHED life-like quality…

Upsell 2: ENTERPRISE EDITION ($47)

Offer More Services At Faster Speeds And Massively Boost Your Income Potential With The ENTREPRISE UPGRADE! Use Any Recorded Voice, Get 100 Niche Templates (+More Monthly), Get VIP Rendering Status, PLUS ALL Software Upgrades Going Forward!

Use Any Recorded Voice With Humatars

VIP Priority Rendering

New Niche Templates Delivered Monthly for One Year

100+ Niche Templates Immediately

Future Software Updates

Money Back Guarantee

Upload any Recorded Voice in Any Language

Lip-sync videos to any target speech with high accuracy

Works for any identity, voice, and language

Offer Personalized Services

Completely Natural with Advanced Lip Synch Technology

Demand Top Dollar for Your Services

Upsell 3: AUDIO SUITE ($67)

AudioSuite brings together not one, but TWO incredible tools:

Synthesys, the first software to introduce human synthetic voices, and

Infinitunes, the first AI-Powered Music Software.

Both include FREE Commercial Licenses. Both for a ONE-TIME payment.

Upsell 4: AGENCY LICENSE ($197)

Unlimited Humatar Videos, 25 Sub-User Accounts, Maximum Video Duration, PLUS 157 Additional Video Templates With The AGENCY Upgrade! This Is The ULTIMATE Upgrade For Serious Entrepreneurs

We’ve created this special Unlimited offer for one reason – to help you scale your Humatar creation business to a serious level. Agency gives you what you need to go from a service business to a full-fledged agency business with the potential to rake in massive profits of five figures or more!

Unlimited Video Renderings – Render unlimited videos with complete freedom.

Sub User Accounts – Have up to 25 additional users creating humatar videos for you. Outsource this work for maximum output!

12 minute Video Duration – Maximize your video length, charge more for longer videos, and expand your capabilities.

Video Templates – 157 More templates are always a good thing, increase your template inventory and appeal to even MORE prospective clients.

Limited Time Discount – The unlimited package will ONLY be available for a limited time, during this launch special.

We want to give you an opportunity to start your very own professional voice-over agency. That’s why we’ve created this Agency package. So you can have more hands-on deck, creating more awesome voice overs without restrictions.

HUMAN SYNTHESYS STUDIO REVIEW CONCLUSION AND ULTIMATE HUGE BONUSES

Thank you so much for reading my Human Synthesys Studio Review! I really hope it did help you with your buying decision. This system is coming out with many bonuses for the early bird. Take your action ASAP for the best deal.

REMEMBER! If you purchase through my link, you will be supported 24/7; That means you can contact me ANYTIME when you get trouble in using or can not contact with the authors/ product supporters. I will help you RIGHT AWAY!

Besides, if you buy this product through my link, you can also get these huge bonuses below (Please remember that these bonuses are not for the TRIAL or FREE versions):

👉VISIT OFFICIAL SITE

You can have an opportunity to receive extra bonuses if you finish 3 steps below:

Step 1: Order the product by Clicking here.

Step 2: Choose 1 of my huge Bonus Packages below! Remember that you can pick one more bonuses pack for each Upgrade you purchased!

Step 3: After your purchase goes through, email your receipt & your chosen bonuses pack to me at [email protected] so I can confirm your purchase and email you information about how to access to your bonus material.

I Will Always Update New Bonus

Now, Check your bonus below!

»»Plugin Bonus Package««

»»Theme Bonus Package««

»»General Bonus Package««

1 backlink dofollow from my site

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evoldir · 5 years ago

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Fwd: Graduate position: UPlymouth.PollinatorGenomes

Begin forwarded message: > From: [email protected] > Subject: Graduate position: UPlymouth.PollinatorGenomes > Date: 14 November 2020 at 06:32:55 GMT > To: [email protected] > > > > ARIES (Doctoral Training Partnership) PhD opportunity at the University > of Plymouth, UK > > Hard-wired for Success? Unravelling Genomic Signatures in Pollinators > (KNIGHT_P21ARIES) > > link to ARIES website advertisemnt: > https://ift.tt/2UqmhcZ > > > Supervisors > - Professor Mairi Knight (School of Biological and Marine Sciences, > University of Plymouth) contact [email protected] > - Professor Andrew Bourke (School of Biological Sciences, University of > East Anglia) > - Dr Wilfried Haerty (The Earlham Institute) > - Dr Jonathan Ellis (School of Biological and Marine Sciences, > University of Plymouth) > - Dr Vanessa Huml (School of Biological and Marine Sciences, University > of Plymouth) > > Project Background > > Many pollinator species, recognised as essential for ecosystem function, > are undergoing rapid declines. One recent exception is the ‘Tree > Bumblebee’ Bombus hyponorum: expanding its range into and across the > UK in <20 years, it is now one of our most common species. > > Building on previous work from the supervisory team, and in collaboration > with the Earlham Institute, this project will investigate key genomic > differences between this and other bumblebee (Bombus) species to > substantially improve our understanding of the factors contributing to > its success, along with the declines of others. While focused on one > taxonomic group, the project has much broader relevance in understanding > organismal responses to environmental change. > > The project’s focus is a genomic comparison of Bombus species from > within the UK and continental Europe. Initial work has identified genomic > regions of interest in B. hypnorum that may be indicative of an enhanced > ability to adapt to anthropogenically altered landscapes. However, > current data are preliminary and lack essential phylogenetic comparison. > > This is an exciting opportunity to generate a substantial and highly > novel genomic dataset to test hypotheses as to whether the observed > genomic differences are unique to B. hypnorum, or shared among Bombus > species (some evidence suggests elevated resilience in the wider > Pyrobombus sub-genus). In addition to fulfilling the specific aims, > the data generated will offer the candidate significant scope to guide > the project’s further direction through characterisation of genomic > signatures and differences across this important group. > > Training > > The project will equip the successful candidate with state-of-the-art > genomic techniques as well as bioinformatic and modelling skills that > are highly transferable and increasingly essential across a wide range > of academic and applied biological disciplines. Full training will be > provided by the supervisory team. The candidate will also gain important > soft skills (e.g. communication, team working, problem solving). He/she > will be based in Plymouth, spending short periods at partner Institutions > as relevant. > > Person Specification > > The successful candidate will have a biology-based degree, an academic > interest in evolutionary ecology, and be enthusiastic about pursuing > a laboratory- and computer-based project. Ideally, he/she will > have some basic molecular ecology experience (e.g. DNA extraction, > PCR) and knowledge of, and interest in, genetic and evolutionary > analysis. Experience of genomic sequencing and bioinformatics is not > essential, although experience/interest in programming (e.g. Python) > would be an additional benefit. > > References > > 1. Huml JV, Ellis JS, Lloyd K, Benefer CM, Kiernan M, Brown MJF, Knight > ME (in review, MS available) Bucking the trend of pollinator decline: > the population genetics of a range expanding bumblebee. > 2. Crowther LP, Wright DJ, Richardson DS, Carvell C, Bourke AFG (2019) > Spatial ecology of a range-expanding bumble bee pollinator. Ecology > and Evolution 9: 986-997. > 3. Crowther LP, Hein P-L, Bourke AFG (2014) Habitat and forage > associations of a naturally colonising insect pollinator, the tree > bumblebee Bombus hypnorum. PLOS ONE 9(9): e107568 > 4. Theodorou P, Radzeviciute R, Kahnt B, Soro A, Grosse I, Paxton RJ > (2018) Genome-wide single nucleotide polymorphism scan suggests > adaptation to urbanization in an important pollinator, the red-tailed > bumblebee (Bombus lapidarius L.). Proceedings of the Royal Society B, > 285, 20172806. > 5. Arbetman MP, Gleiser G, Morales CL, Williams P, Aizen MA (2017) > Global decline of bumblebees is phylogenetically structured and > inversely related to species range size and pathogen incidence. > Proceedings of the Royal Society B, 284, 20170204. > > Key Information > > This project has been shortlisted for funding by the ARIES NERC DTP and > will start on 1st October 2021. The closing date for applications is 23:59 > on 12th January 2021. Successful candidates who meet UKRI’s eligibility > criteria will be awarded a NERC studentship, which covers fees, stipend > (£15,285 p.a. for 2020-21) and research funding. For the first time in > 2021/22 international applicants (EU and non-EU) will be eligible for > fully-funded UKRI studentships. Please note ARIES funding does not cover > visa costs (including immigration health surcharge) or other additional > costs associated with relocation to the UK. ARIES students benefit from > bespoke graduate training and ARIES provides £2,500 to every student for > access to external training, travel and conferences. Excellent applicants > from quantitative disciplines with limited experience in environmental > sciences may be considered for an additional 3-month stipend to take > advanced-level courses in the subject area. ARIES is committed to > equality, diversity, widening participation and inclusion in all areas > of its operation. We encourage enquiries and applications from all > sections of the community regardless of gender, ethnicity, disability, > age, sexual orientation and transgender status. Academic qualifications > are considered alongside significant relevant non-academic experience. > All ARIES studentships may be undertaken on a part-time or full-time > basis, visa requirements notwithstanding For further information, > please contact the supervisor. To apply for this Studentship click on > the “Apply now” link below. > > https://ift.tt/32GZNJc > > > > Vanessa Huml > via IFTTT

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tak4hir0 · 5 years ago

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Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist. What is Deep Learning and Artificial Neural Networks? Deep Learning is the modern buzzword for artificial neural networks, one of many concepts and algorithms in machine learning to build analytics models. A neural network works similar to what we know from a human brain: You get non-linear interactions as input and transfer them to output. Neural networks leverage continuous learning and increasing knowledge in computational nodes between input and output. A neural network is a supervised algorithm in most cases, which uses historical data sets to learn correlations to predict outputs of future events, e.g. for cross selling or fraud detection. Unsupervised neural networks can be used to find new patterns and anomalies. In some cases, it makes sense to combine supervised and unsupervised algorithms. Neural Networks are used in research for many decades and includes various sophisticated concepts like Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) or Autoencoder. However, today’s powerful and elastic computing infrastructure in combination with other technologies like graphical processing units (GPU) with thousands of cores allows to do much more powerful computations with a much deeper number of layers. Hence the term “Deep Learning”. The following picture from TensorFlow Playground shows an easy-to-use environment which includes various test data sets, configuration options and visualizations to learn and understand deep learning and neural networks: If you want to learn more about the details of Deep Learning and Neural Networks, I recommend the following sources: “The Anatomy of Deep Learning Frameworks”– an article about the basic concepts and components of neural networks TensorFlow Playground to play around with neural networks by yourself hands-on without any coding, also available on Github to build your own customized offline playground “Deep Learning Simplified” video series on Youtube with several short, simple explanations of basic concepts, alternative algorithms and some frameworks like H2O.ai or Tensorflow While Deep Learning is getting more and more traction, it is not the silver bullet for every scenario. When (not) to use Deep Learning? Deep Learning enables many new possibilities which were not possible in “mass production” a few years ago, e.g. image classification, object recognition, speech translation or natural language processing (NLP) in much more sophisticated ways than without Deep Learning. A key benefit is the automated feature engineering, which costs a lot of time and efforts with most other machine learning alternatives. You can also leverage Deep Learning to make better decisions, increase revenue or reduce risk for existing (“already solved”) problems instead of using other machine learning algorithms. Examples include risk calculation, fraud detection, cross selling and predictive maintenance. However, note that Deep Learning has a few important drawbacks: Very expensive, i.e. slow and compute-intensive; training a deep learning model often takes days or weeks, execution also takes more time than most other algorithms. Hard to interpret: lack of understandability of the result of the analytic model; often a key requirement for legal or compliance regularities Tends to overfitting, and therefore needs regularization Deep Learning is ideal for complex problems. It can also outperform other algorithms in moderate problems. Deep Learning should not be used for simple problems. Other algorithms like logistic regression or decision trees can solve these problems easier and faster. Open Source Deep Learning Frameworks Neural networks are mostly adopted using one of various open source implementations. Various mature deep learning frameworks are available for different programming languages. The following picture shows an overview of open source deep learning frameworks and evaluates several characteristics: These frameworks have in common that they are built for data scientists, i.e. personas with experience in programming, statistics, mathematics and machine learning. Note that writing the source code is not a big task. Typically, only a few lines of codes are needed to build an analytic model. This is completely different from other development tasks like building a web application, where you write hundreds or thousands of lines of code. In Deep Learning – and Data Science in general – it is most important to understand the concepts behind the code to build a good analytic model. Some nice open source tools like KNIME or RapidMinerallow visual coding to speed up development and also encourage citizen data scientists (i.e. people with less experience) to learn the concepts and build deep networks. These tools use own deep learning implementations or other open source libraries like H2O.ai or DeepLearning4j as embedded framework under the hood. If you do not want to build your own model or leverage existing pre-trained models for common deep learning tasks, you might also take a look at the offerings from the big cloud providers, e.g. AWS Polly for Text-to-Speech translation, Google Vision API for Image Content Analysis, or Microsoft’s Bot Framework to build chat bots. The tech giants have years of experience with analysing text, speech, pictures and videos and offer their experience in sophisticated analytic models as a cloud service; pay-as-you-go. You can also improve these existing models with your own data, e.g. train and improve a generic picture recognition model with pictures of your specific industry or scenario. Deep Learning in Conjunction with Visual Analytics No matter if you want to use “just” a framework in your favourite programming language or a visual coding tool: You need to be able to make decisions based on the built neural network. This is where visual analytics comes into play. In short, visual analytics allows any persona to make data-driven decisions instead of listening to gut feeling when analysing complex data sets. See “Using Visual Analytics for Better Decisions – An Online Guide” to understand the key benefits in more detail. A business analyst does not understand anything about deep learning, but just leverages the integrated analytic model to answer its business questions. The analytic model is applied under the hood when the business analyst changes some parameters, features or data sets. Though, visual analytics should also be used by the (citizen) data scientist to build the neural network. See “How to Avoid the Anti-Pattern in Analytics: Three Keys for Machine ...” to understand in more details how technical and non-technical people should work together using visual analytics to build neural networks, which help solving business problems. Even some parts of data preparation are best done within visual analytics tooling. From a technical perspective, Deep Learning frameworks (and in a similar way any other Machine Learning frameworks, of course) can be integrated into visual analytics tooling in different ways. The following list includes a TIBCO Spotfire example for each alternative: Embedded Analytics: Implemented directly within the analytics tool (self-implementation or “OEM”); can be used by the business analyst without any knowledge about machine learning (Spotfire: Clustering via some basic, simple configuration of a input and output data plus cluster size) Native Integration: Connectors to directly access external deep learning clusters. (Spotfire: TERR to use R’s machine learning libraries, KNIME connector to directly integrate with external tooling) Framework API: Access via a Wrapper API in different programming languages. For example, you could integrate MXNet via R or TensorFlow via Python into your visual analytics tooling. This option can always be used and is appropriate if no native integration or connector is available. (Spotfire: MXNet’s R interface via Spotfire’s TERR Integration for using any R library) Integrated as Service via an Analytics Server: Connect external deep learning clusters indirectly via a server-side component of the analytics tool; different frameworks can be accessed by the analytics tool in a similar fashion (Spotfire: Statistics Server for external analytics tools like SAS or Matlab) Cloud Service: Access pre-trained models for common deep learning specific tasks like image recognition, voice recognition or text processing. Not appropriate for very specific, individual business problems of an enterprise. (Spotfire: Call public deep learning services like image recognition, speech translation, or Chat Bot from AWS, Azure, IBM, Google via REST service through Spotfire’s TERR / R interface) All options have in common that you need to add configuration of some hyper-parameters, i.e. “high level” parameters like problem type, feature selection or regularization level. Depending on the integration option, this can be very technical and low level, or simplified and less flexible using terms which the business analyst understands. Deep Learning Example: Autoencoder Template for TIBCO Spotfire Let’s take one specific category of neural networks as example: Autoencoders to find anomalies. Autoencoder is an unsupervised neural network used to replicate the input dataset by restricting the number of hidden layers in a neural network. A reconstruction error is generated upon prediction. The higher the reconstruction error, the higher is the possibility of that data point being an anomaly. Use Cases for Autoencoders include fighting financial crime, monitoring equipment sensors, healthcare claims fraud, or detecting manufacturing defects. A generic TIBCO Spotfire template is available in the TIBCO Community for free. You can simply add your data set and leverage the template to find anomalies using Autoencoders – without any complex configuration or even coding. Under the hood, the template uses H2O.ai’s deep learning implementation and its R API. It runs in a local instance on the machine where to run Spotfire. You can also take a look at the R code, but this is not needed to use the template at all and therefore optional. Real World Example: Anomaly Detection for Predictive Maintenance Let’s use the Autoencoder for a real-world example. In telco, you have to analyse the infrastructure continuously to find problems and issues within the network. Best before the failure happens so that you can fix it before the customer even notices the problem. Take a look at the following picture, which shows historical data of a telco network: The orange dots are spikes which occur as first indication of a technical problem in the infrastructure. The red dots show a constant failure where mechanics have to replace parts of the network because it does not work anymore. Autoencoders can be used to detect network issues before they actually happen. TIBCO Spotfire is uses H2O’s autoencoder in the background to find the anomalies. As discussed before, the source code is relative scarce. Here is the snipped of building the analytic model with H2O’s Deep Learning R API and detecting the anomalies (by finding out the reconstruction error of the Autoencoder): This analytic model – built by the data scientist – is integrated into TIBCO Spotfire. The business analyst is able to visually analyse the historical data and the insights of the Autoencoder. This combination allows data scientists and business analysts to work together fluently. It was never easier to implement predictive maintenance and create huge business value by reducing risk and costs. Apply Analytic Models to Real Time Processing with Streaming Analytics This article focuses on building deep learning models with Data Science Frameworks and Visual Analytics. Key for success in projects is to apply the build analytic model to new events in real time to add business value like increasing revenue, reducing cost or reducing risk. “How to Apply Machine Learning to Event Processing” describes in more detail how to apply analytic models to real time processing. Or watch the corresponding video recording leveraging TIBCO StreamBase to apply some H2O models in real time. Finally, I can recommend to learn about various streaming analytics frameworks to apply analytic models. Let’s come back to the Autoencoder use case to realize predictive maintenance in telcos. In TIBCO StreamBase, you can easily apply the built H2O Autoencoder model without any redevelopment via StreamBase’ H2O connector. You just attach the Java code generated by H2O framework, which contains the analytic model and compiles to very performant JVM bytecode: The most important lesson learned: Think about the execution requirements before building the analytic model. What performance do you need regarding latency? How many events do you need to process per minute, second or millisecond? Do you need to distribute the analytic model to a clusters with many nodes? How often do you have to improve and redeploy the analytic model? You need to answer these questions at the beginning of your project to avoid double efforts and redevelopment of analytic models! Another important fact is that analytic models do not always need “real time processing” in terms of very fast and / or frequent model execution. In the above telco example, these spikes and failures might happen in subsequent days or even weeks. Thus, in many use cases, it is fine to apply an analytic model once a day or week instead of just every second to every new event, therefore. Deep Learning + Visual Analytics + Streaming Analytics = Next Generation Big Data Success Stories Deep Learning allows to solve many well understood problems like cross selling, fraud detection or predictive maintenance in a more efficient way. In addition, you can solve additional scenarios, which were not possible to solve before, like accurate and efficient object detection or speech-to-text translation. Visual Analytics is a key component in Deep Learning projects to be successful. It eases the development of deep neural networks by (citizen) data scientists and allows business analysts to leverage these analytic models to find new insights and patterns. Today, (citizen) data scientists use programming languages like R or Python, deep learning frameworks like Theano, TensorFlow, MXNet or H2O’s Deep Water and a visual analytics tool like TIBCO Spotfire to build deep neural networks. The analytic model is embedded into a view for the business analyst to leverage it without knowing the technology details. In the future, visual analytics tools might embed neural network features like they already embed other machine learning features like clustering or logistic regression today. This will allow business analysts to leverage Deep Learning without the help of a data scientist and be appropriate for simpler use cases. However, do not forget that building an analytic model to find insights is just the first part of a project. Deploying it to real time afterwards is as important as second step. Good integration between tooling for finding insights and applying insights to new events can improve time-to-market and model quality in data science projects significantly. The development lifecycle is a continuous closed loop. The analytic model needs to be validated and rebuild in certain sequences.

#Data Science Central

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simplemlmsponsoring · 7 years ago

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Amazon Web Services: How Big Is AWS?

Working with technology companies, I’m amazed at how many are hosting their platforms on Amazon Web Services (AWS). Netflix, Reddit, AOL, and Pinterest are now running on Amazon services. Even GoDaddy is moving a majority of its infrastructure there.

Key to the popularity is the combination of the high availability and low cost. Amazon S3, for example, is designed to deliver 99.999999999% availability, serving trillions of objects worldwide. Amazon is notorious for its aggressive pricing and AWS’ is no different. That high availability and low cost has been attractive to startups who wish to scale quickly and efficiently.

$18 billion in revenue for 2017 and nearly 50% growth in the second quarter of 2018 show that the Amazon Cloud solution continues to attract new customers left and right.

Nick Galov, HostingTribunal

The downside, in my opinion, has been user experience and support. Sign into your Amazon Web Services panel and you’re met with dozens of options with very little detail on what platforms actually do and how they work together. Check out the list of products below the infographic… everything from hosting to AI have their own platforms on AWS.

Sure, you can dig and educate yourself. However, I’ve found that simple processes like setting up a website take way too much effort there. Of course, I’m not a full-time web developer. Many of the companies I work with give me a strange look when I tell them about the issues I have.

This infographic from HostingTribunal, AWS Web Hosting, does a great job in documenting the history of AWS, current growth statistics, alliances and partnerships, major outages, why you should host with AWS, key web hosting solutions on AWS, and success stories:

List of Amazon Web Services

AWS Server Solutions:

Amazon EC2 – Virtual Servers in the Cloud Amazon EC2 Auto Scaling – Scale Compute Capacity to Meet Demand Amazon Elastic Container Service – Run and Manage Docker Containers Amazon Elastic Container Service for Kubernetes – Run Managed Kubernetes on AWS Amazon Elastic Container Registry – Store and Retrieve Docker Images Amazon Lightsail – Launch and Manage Virtual Private Servers AWS Batch – Run Batch Jobs at Any Scale AWS Elastic Beanstalk – Run and Manage Web Apps AWS Fargate – Run Containers without Managing Servers or Clusters AWS Lambda – Run your Code in Response to Events AWS Serverless Application Repository – Discover, Deploy, and Publish Serverless Applications VMware Cloud on AWS – Build a Hybrid Cloud without Custom Hardware AWS Outposts – Run AWS services on-premises

AWS Storage Solutions

Amazon S3 – Scalable Storage in the Cloud Amazon EBS – Block Storage for EC2 Amazon Elastic File System – Managed File Storage for EC2 Amazon Glacier – Low-cost Archive Storage in the Cloud AWS Storage Gateway – Hybrid Storage Integration AWS Snowball – Petabyte-scale Data Transport AWS Snowball Edge – Petabyte-scale Data Transport with On-board Compute AWS Snowmobile – Exabyte-scale Data Transport Amazon FSx for Lustre – Fully managed compute-intensive file system Amazon FSx for Windows File Server – Fully managed Windows native file system

AWS Database Solutions

Amazon Aurora – High Performance Managed Relational Database Amazon RDS – Managed Relational Database Service for MySQL, PostgreSQL, Oracle, SQL Server, and MariaDB Amazon DynamoDB – Managed NoSQL Database Amazon ElastiCache – In-memory Caching System Amazon Redshift – Fast, Simple, Cost-effective Data Warehousing Amazon Neptune – Fully Managed Graph Database Service AWS Database Migration Service – Migrate Databases with Minimal Downtime Amazon Quantum Ledger Database (QLDB) – Fully managed ledger database Amazon Timestream – Fully managed time series database Amazon RDS on VMware – Automate on-premises database management

AWS Migration and Transfer Solutions

AWS Application Discovery Service – Discover On-Premises Applications to Streamline Migration AWS Database Migration Service – Migrate Databases with Minimal Downtime AWS Migration Hub – Track Migrations from a Single Place AWS Server Migration Service – Migrate On-Premises Servers to AWS AWS Snowball – Petabyte-scale Data Transport AWS Snowball Edge – Petabyte-scale Data Transport with On-board Compute AWS Snowmobile – Exabyte-scale Data Transport AWS DataSync – Simple, fast, online data transfer AWS Transfer for SFTP – Fully managed SFTP service

AWS Networking and Content Delivery Solutions

Amazon VPC – Isolated Cloud Resources Amazon VPC PrivateLink – Securely Access Services Hosted on AWS Amazon CloudFront – Global Content Delivery Network Amazon Route 53 – Scalable Domain Name System Amazon API Gateway – Build, Deploy, and Manage APIs AWS Direct Connect – Dedicated Network Connection to AWS Elastic Load Balancing – High Scale Load Balancing AWS Cloud Map – Application resource registry for microservices AWS App Mesh – Monitor and control microservices AWS Transit Gateway – Easily scale VPC and account connections AWS Global Accelerator – Improve application availability and performance

AWS Developer Tools

AWS CodeStar – Develop and Deploy AWS Applications AWS CodeCommit – Store Code in Private Git Repositories AWS CodeBuild – Build and Test Code AWS CodeDeploy – Automate Code Deployment AWS CodePipeline – Release Software using Continuous Delivery AWS Cloud9 – Write, Run, and Debug Code on a Cloud IDE AWS X-Ray – Analyze and Debug Your Applications AWS Command Line Interface – Unified Tool to Manage AWS Services

AWS Management and Governance Solutions

Amazon CloudWatch – Monitor Resources and Applications AWS Auto Scaling – Scale Multiple Resources to Meet Demand AWS CloudFormation – Create and Manage Resources with Templates AWS CloudTrail – Track User Activity and API Usage AWS Config – Track Resource Inventory and Changes AWS OpsWorks – Automate Operations with Chef and Puppet AWS Service Catalog – Create and Use Standardized Products AWS Systems Manager – Gain Operational Insights and Take Action AWS Trusted Advisor – Optimize Performance and Security AWS Personal Health Dashboard – Personalized View of AWS Service Health AWS Control Tower – Set up and govern a secure, compliant, multi-account environment AWS License Manager – Track, manage, and control licenses AWS Well-Architected Tool – Review and improve your workloads

AWS Media Services

Amazon Elastic Transcoder – Easy-to-use Scalable Media Transcoding Amazon Kinesis Video Streams – Process and Analyze Video Streams AWS Elemental MediaConvert �� Convert File-based Video Content AWS Elemental MediaLive – Convert Live Video Content AWS Elemental MediaPackage – Video Origination and Packaging AWS Elemental MediaStore – Media Storage and Simple HTTP Origin AWS Elemental MediaTailor – Video Personalization and Monetization AWS Elemental MediaConnect – Reliable and secure live video transport

AWS Security, Identity, and Compliance Solutions

AWS Identity & Access Management – Manage User Access and Encryption Keys Amazon Cloud Directory – Create Flexible Cloud-native Directories Amazon Cognito – Identity Management for your Apps AWS Single Sign-On – Cloud Single Sign-On (SSO) Service Amazon GuardDuty – Managed Threat Detection Service Amazon Inspector – Analyze Application Security Amazon Macie -Discover, Classify, and Protect your Data AWS Certificate Manager – Provision, Manage, and Deploy SSL/TLS Certificates AWS CloudHSM – Hardware-based Key Storage for Regulatory Compliance AWS Directory Service – Host and Manage Active Directory AWS Firewall Manager – Central Management of Firewall Rules AWS Key Management Service – Managed Creation and Control of Encryption Keys AWS Organizations – Policy-based Management for Multiple AWS Accounts AWS Secrets Manager – Rotate, Manage, and Retrieve Secrets AWS Shield – DDoS Protection AWS WAF – Filter Malicious Web Traffic AWS Artifact – On-demand access to AWS compliance reports AWS Security Hub – Unified security and compliance center

AWS Analytics Solutions

Amazon Athena – Query Data in S3 using SQL Amazon CloudSearch – Managed Search Service Amazon Elasticsearch Service – Run and Scale Elasticsearch Clusters Amazon EMR – Hosted Hadoop Framework Amazon Kinesis – Work with Real-time Streaming Data Amazon Redshift – Fast, Simple, Cost-effective Data Warehousing Amazon Quicksight – Fast Business Analytics Service AWS Data Pipeline – Orchestration Service for Periodic, Data-driven Workflows AWS Glue – Prepare and Load Data Amazon Managed Streaming for Kafka – Fully managed Apache Kafka service AWS Lake Formation – Build a secure data lake in days

AWS Machine Learning Solutions

Amazon SageMaker – Build, Train, and Deploy Machine Learning Models at Scale Amazon Comprehend – Discover Insights and Relationships in Text Amazon Lex – Build Voice and Text Chatbots Amazon Polly – Turn Text into Lifelike Speech Amazon Rekognition – Analyze Image and Video Amazon Translate – Natural and Fluent Language Translation Amazon Transcribe – Automatic Speech Recognition AWS DeepLens – Deep Learning Enabled Video Camera AWS Deep Learning AMIs – Quickly Start Deep Learning on EC2 Apache MXNet on AWS – Scalable, High-performance Deep Learning TensorFlow on AWS – Open-source Machine Intelligence Library Amazon Personalize – Build real-time recommendations into your applications Amazon Forecast – Increase forecast accuracy using machine learning Amazon Inferentia – Machine learning inference chip Amazon Textract – Extract text and data from documents Amazon Elastic Inference – Deep learning inference acceleration Amazon SageMaker Ground Truth – Build accurate ML training datasets AWS DeepRacer – Autonomous 1/18th scale race car, driven by ML

AWS Mobile Solutions

AWS Amplify -Build and deploy mobile and web applications Amazon API Gateway – Build, Deploy, and Manage APIs Amazon Pinpoint – Push Notifications for Mobile Apps AWS AppSync – Real-time and Offline Mobile Data Apps AWS Device Farm – Test Android, FireOS, and iOS Apps on Real Devices in the Cloud AWS Mobile SDK – Mobile Software Development Kit

AWS Augmented Reality and Virtual Reality Solutions

Amazon Sumerian – Build and Run VR and AR Applications

AWS Application Integration Solutions

AWS Step Functions – Coordinate Distributed Applications Amazon Simple Queue Service (SQS) – Managed Message Queues Amazon Simple Notification Service (SNS) – Pub/Sub, Mobile Push and SMS Amazon MQ – Managed Message Broker for ActiveMQ

AWS Customer Engagement Solutions

Amazon Connect – Cloud-based Contact Center Amazon Pinpoint – Push Notifications for Mobile Apps Amazon Simple Email Service (SES) – Email Sending and Receiving

AWS Business Applications

Alexa for Business – Empower your Organization with Alexa Amazon Chime – Frustration-free Meetings, Video Calls, and Chat Amazon WorkDocs – Enterprise Storage and Sharing Service Amazon WorkMail – Secure and Managed Business Email and Calendaring

AWS Desktop and Application Streaming Solutions

Amazon WorkSpaces – Desktop Computing Service Amazon AppStream 2.0 – Stream Desktop Applications Securely to a Browser

AWS Internet of Things (IoT) Solutions

AWS IoT Core – Connect Devices to the Cloud Amazon FreeRTOS – IoT Operating System for Microcontrollers AWS Greengrass – Local Compute, Messaging, and Sync for Devices AWS IoT 1-Click – One Click Creation of an AWS Lambda Trigger AWS IoT Analytics – Analytics for IoT Devices AWS IoT Button – Cloud Programmable Dash Button AWS IoT Device Defender – Security Management for IoT Devices AWS IoT Device Management – Onboard, Organize, and Remotely Manage IoT Devices AWS IoT Events – IoT event detection and response AWS IoT SiteWise – IoT data collector and interpreter AWS Partner Device Catalog – Curated catalog of AWS-compatible IoT hardware AWS IoT Things Graph – Easily connect devices and web services

AWS Game Development Solutions

Amazon GameLift – Simple, Fast, Cost-effective Dedicated Game Server Hosting Amazon Lumberyard – A Free Cross-platform 3D Game Engine with Full Source, Integrated with AWS and Twitch

AWS Cost Management Solutions

AWS Cost Explorer – Analyze Your AWS Cost and Usage AWS Budgets – Set Custom Cost and Usage Budgets Reserved Instance Reporting – Dive Deeper into Your Reserved Instances (RIs) AWS Cost and Usage Report – Access Comprehensive Cost and Usage Information

AWS Blockchain Solutions

Amazon Managed Blockchain – Create and manage scalable blockchain networks

AWS Robotics Solutions

AWS RoboMaker – Develop, test, and deploy robotics applications

AWS Satellite Solutions

AWS Ground Station – Fully managed ground station as a service

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