Tag Archives: Microsoft

Which AI Software is Right for Your Business? An In-Depth Look

Artificial Intelligence
Artificial Intelligence & Machine Learning, Digital Marketing, , , , , , , , , , , , , , , ,

In the rapidly evolving world of tech, AI emerges as a crucial innovation catalyst, offering businesses worldwide groundbreaking advantages. The proliferation of AI platforms provides organizations with the tools to leverage AI’s power, yet the sheer variety complicates the selection process for tech developers and business leaders. Assessing these platforms’ strengths, weaknesses, user experience, scalability, and integration potential is essential. Our guide offers a detailed comparison of leading AI software platforms to support you in choosing one that best aligns with your strategic objectives.

Amazon AI Services

Amazon Q AI

Features: Amazon AI, central to AWS, delivers a comprehensive suite of AI tools for various industries, featuring Amazon Lex for chatbots, Recognition for image/video processing, Polly for speech synthesis, SageMaker for easy model building, and Forecast for accurate time-series forecasting. This cohesive ecosystem is designed to meet a wide range of business needs.

Pros: Amazon AI Services excels by providing scalable, deep learning technologies that enable businesses to start small and grow efficiently. Their pay-as-you-go pricing ensures cost-effectiveness, aligning expenses with usage. This advantage, supported by AWS’s extensive infrastructure, makes Amazon AI an essential tool for competitive innovation without large initial investments.

Cons: The breadth of Amazon AI’s offerings, while beneficial, can be daunting for beginners, and integrating with non-AWS systems can be complicated. This highlights the need for strategic planning when adopting Amazon AI, especially for businesses not already utilizing AWS.

Primary Programming Languages: Python, Java, JavaScript, C++, Go

TensorFlow

TensorFlow

Features: TensorFlow shines in AI with its support for complex deep-learning tasks. Its flexible architecture allows use across multiple computing platforms via a unified API, widening its usability. TensorBoard, a key feature, provides a visual representation of models’ performance, simplifying the process of debugging and optimizing machine learning projects. 

Pros: TensorFlow excels as a powerful, open-source AI framework perfect for large-scale computations and complex AI projects. It provides numerous pre-built models and efficient processes, significantly reducing development time. Backed by a vibrant community and continuous updates, its compatibility with Google Cloud further boosts its scalability and ease of deployment, making it a premier choice in the AI sector.

Cons: TensorFlow’s complexity and extensive capabilities can be daunting for machine learning novices, requiring a solid foundation in math and coding. It’s more suited for experts or large-scale projects due to its rich feature set and scalability. Beginners might find the learning curve steep, emphasizing the need for thorough evaluation based on the project’s scale and complexity to avoid unnecessary hurdles.

Primary Programming Languages: Python, C++

Microsoft Azure AI

Azure AI

Features: Microsoft Azure AI uses AI to transform business processes and customer interactions. It employs Azure Cognitive Services for comprehensive data analysis and Azure Machine Learning for easier model development. Azure Bot Services introduces intelligent bots for improved customer service. Combined, these tools create a powerful AI ecosystem for business innovation.

Pros: Microsoft Azure AI excels in its seamless integration within the Microsoft ecosystem, facilitating easier AI adoption through its user-friendly interface and compatibility with widely used software such as Office 365 and Dynamics 365. It significantly lowers the barrier to AI entry with the Azure Machine Learning Studio’s no-code/low-code options, all while maintaining high standards of security, compliance, and scalability.

Cons: Microsoft Azure AI’s tight integration with its own ecosystem may limit flexibility and third-party service compatibility, presenting a hurdle for those seeking extensive customization. Its wide but complex array of offerings might also be daunting for AI novices, possibly requiring significant training or external support.

Primary Programming Languages: Python, C#, C++, JavaScript/Node.js, Java, and TypeScript

Petuum

Petuum

Features: Petuum revolutionizes AI with its specialized operating system, crafted for modern AI demands. It democratizes AI, ensuring it’s easily adaptable for various industries. Central to its innovation is making AI software industrial-scale, streamlining everything from creation to deployment. Its scalable, hardware-independent design offers flexibility in AI deployment, setting new industry standards.

Pros: Petuum offers a unique approach to AI adoption with its scalable platform, hardware-agnostic design, and easy IT integration. These features cater to businesses of any size, provide deployment flexibility, and facilitate smooth technology transitions, making advanced AI applications more accessible across various industries.

Cons: Petuum’s innovative AI framework faces adoption barriers due to its new market presence and smaller community. Its distinctive platform struggles without a strong ecosystem or the reliability established by competitors. The lack of community support and integration options hinders easy innovation, while its specialized system may overwhelm newcomers, especially those with limited resources.

Primary Programming Languages: C++

Oracle Cloud Infrastructure (OCI) AI Services

Oracle AI

Features: OCI AI Services streamline business processes by integrating AI and machine learning, enabling effective data analysis, pattern recognition, and predictive modeling under one ecosystem. This integration allows for swift implementation and operational upgrades, minimizing the need for external support and manual coding. OCI AI Services’ cloud-based design further enhances its scalability.

Pros: OCI AI Services notably excels in security, safeguarding client data with advanced measures. They also offer high-performance computing for complex AI tasks and seamlessly integrate with Oracle’s ERP solutions, enhancing operational efficiency and decision accuracy.

Cons: Oracle’s OCI AI services may be too costly and intricate for small businesses or those on limited budgets. Its complex platform can also be less intuitive compared to competitors, making it difficult for newcomers to exploit its full AI and machine learning potential without substantial training. This aspect might deter organizations from looking for a simpler AI solution.

Primary Programming Languages: Python, Java, JavaScript, GO, C++

DataRobot

DataRobot AI

Features: DataRobot revolutionizes data science with a platform that makes analysis and model development straightforward. It supports a wide range of machine learning algorithms, enabling users to create and deploy predictive models without extensive technical knowledge. This accessibility empowers both data experts and business analysts alike, streamlining data science processes.

Pros: DataRobot’s chief benefit lies in its advanced AutoML technology, speeding up the creation of models for precise predictions. It emphasizes understanding the model-building process through detailed explanations of its decisions, fostering transparency and trust essential for businesses to justify their data-driven choices to stakeholders.

Cons: However, DataRobot’s advanced features could be cost-prohibitive for small businesses or those with tight budgets. Additionally, its comprehensive toolkit may exceed the needs of organizations with simpler data science requirements, making it an expensive choice for basic projects.

Primary Programming Languages: Python, R, Java, JavaScript, R, SQL

Tencent

Tencent

Features: Tencent leverages AI to boost business and consumer interactions through web services. Key focuses include facial recognition for enhanced security, natural language processing to improve communication, and cutting-edge online payment systems for better digital commerce efficiency and engagement.

Pros: Tencent’s AI services stand out due to their robust data handling and innovative applications, such as AI-driven gaming and digital content. These capabilities are crucial in our data-centric world, providing Tencent a competitive edge by optimizing data analysis and expanding AI’s potential in entertainment.

Cons: Tencent’s AI solutions, while robust within China, may face challenges in global markets due to their local focus. The customization for China’s unique environment can complicate international adoption, requiring significant modifications to align with different market requirements and regulations.

Primary Programming Languages: C++, Java, JavaScript, Python, Go

PredictionIO

PredicitionIO

Features: PredictionIO shines in the AI and machine learning field with its open-source server, giving developers full reign for more flexible AI application management and deployment. It seamlessly meshes with existing apps, bolstered by a strong community that enriches its resources with practical insights and constant updates.

Pros: PredictionIO is notably adaptable and cost-effective, perfect for startups and tech enterprises looking to economically incorporate AI capabilities. Its compatibility with a wide range of data sources and software, combined with a strong, community-driven support system, streamlines AI integration and fosters innovation.

Cons: PredictionIO might not meet the needs of organizations looking for an extensive AI solution. Its feature set, while broad, doesn’t match the depth offered by giants like Google, Amazon, or IBM, which deliver advanced deep learning, analytics, and tailored services.

Primary Programming Languages: Scala, Python, Java

IBM Watson

IBM Watson

Features: IBM Watson represents a pinnacle of innovation in AI, designed for enterprises. It offers a wide-ranging suite of AI services, including language processing and data analysis. In addition to language processing and data analysis, IBM Watson’s suite of AI services extends to visual recognition, enabling businesses to interpret and analyze images and videos for various applications. This capability is particularly beneficial in sectors such as retail, where it can enhance customer engagement through personalized recommendations based on visual cues.  Its diverse capabilities enable businesses in healthcare, finance, and beyond to enhance efficiency, gain insights, and personalize customer experiences, transforming industries with actionable data.

Pros: IBM Watson’s strength lies in its enterprise-focused AI solutions, designed to solve specific business challenges with industry-specific tools, backed by IBM’s trusted, decades-long legacy in technology.

Cons: IBM Watson’s complex AI features and comprehensive interface may pose challenges for newcomers and small businesses. The detailed integration process requires significant time and technical knowledge, potentially hindering those without extensive resources.

Primary Programming Languages: Python, Java, JavaScript/Node.js

Wipro Holmes

Features: Wipro Holmes leverages AI to enhance productivity and customer satisfaction through hyper-automation and cognitive computing. It streamlines complex tasks across infrastructure and maintenance, promoting the transition to automated enterprise environments. This evolving solution fosters continuous innovation and efficiency with reduced manual efforts.

Pros: Wipro Holmes distinguishes itself with strong automation and cognitive features, streamlining complex operations to enhance efficiency and lower costs. Its predictive analytics also support preemptive problem-solving, elevating both operational efficiency and client contentment, making it a vital tool for businesses aiming for innovation and competitiveness.

Cons: Wipro Holmes faces challenges with limited market visibility and a complex setup. Mainly known within Wipro’s client base, it struggles with broader market adoption. Organizations may find its full potential locked behind a need for direct partnerships with Wipro, adding logistical and financial complexities for those seeking standalone AI solutions.

Primary Programming Languages: Python, Java, JavaScript/Node.js, SQL

NVIDIA AI

Nvidia AI

Features: NVIDIA’s AI development, powered by robust GPUs, offers specialized suites for deep learning and analytics. Capable of managing extensive datasets and intricate algorithms, it aids in improving image and speech recognition, along with natural language processing. This integration of GPU technology with AI ensures rapid, efficient data handling, crucial for AI-focused ventures.

Pros: NVIDIA’s advanced GPUs provide immense computational power, crucial for AI innovation. Their technology enables quicker AI model development and complex computations, significantly benefiting data scientists and developers. This accelerates AI advancements and enhances productivity through tools like CUDA.

Cons: The primary drawback of NVIDIA’s AI offerings is their significant hardware and expertise investment, making them more suitable for large or specialized entities heavily engaged in AI research. This requirement may pose challenges for smaller businesses or those newer to AI, emphasizing a gap between high-level AI research and broader business applications.

Primary Programming Languages: CUDA, Python, C/C++, TensorRT 

OpenAI

Open AI

Features: OpenAI stands as a cutting-edge research laboratory in AI, focusing on ensuring that artificial general intelligence (AGI) benefits all of humanity. With projects like GPT (Generative Pre-trained Transformer) series, it’s at the forefront of natural language processing, offering tools that can understand, generate, and translate text with remarkable accuracy. OpenAI’s commitment to ethical AI development is also notable, aiming to advance AI technologies within a framework that prioritizes safety and societal benefits.

Pros: OpenAI’s innovations, such as GPT-3, have revolutionized the way businesses and individuals interact with AI, providing capabilities that range from drafting emails to generating code. Its open-source approach encourages widespread adoption and community-driven improvement, making cutting-edge AI more accessible to a broader audience.

Cons: While OpenAI democratizes access to advanced AI capabilities, its powerful models come with risks of misuse, including generating misleading information or automating tasks in a way that could disrupt job markets. Furthermore, the computational resources required to train and run these large models may pose accessibility challenges for smaller organizations or researchers with limited budgets.

Primary Programming Languages: Python, C++, and JavaScript

Conclusion

When assessing AI platforms, it’s crucial to align with your organization’s specific requirements, focusing on user experience, scalability, and smooth integration. Consider both the strengths and limitations of each option, bearing in mind the dynamic nature of AI technology. The ideal choice will not only meet your current needs but will also adapt and evolve, driving your business toward greater efficiency and innovation.

Harness AI with the Top Machine Learning Frameworks of 2021

Artificial Intelligence & Machine Learning, , , , , , , , , , ,

According to Gartner, machine learning and AI will create $2.29 trillion of business value by 2021. Artificial intelligence is the way of the future, but many businesses do not have the resources to create and employ AI from scratch. Luckily, machine learning frameworks make the implementation of AI more accessible, enabling businesses to take their enterprises to the next level.

What Are Machine Learning Frameworks?

Machine learning frameworks are open source interfaces, libraries, and tools that exist to lay the foundation for using AI. They ease the process of acquiring data, training models, serving predictions, and refining future results. Machine learning frameworks enable enterprises to build machine learning models without requiring an in-depth understanding of the underlying algorithms. They enable businesses that lack the resources to build AI from scratch to wield it to enhance their operations.

For example, AirBNB uses TensorFlow, the most popular machine learning framework, to classify images and detect objects at scale, enhancing guests ability to see their destination. Twitter uses it to create algorithms which rank tweets.

Here is a rundown of today’s top ML Frameworks:

TensorFlow

TensorFlow

TensorFlow is an end-to-end open source platform for machine learning built by the Google Brain team. TensorFlow offers a comprehensive, flexible ecosystem of tools, libraries, and community resources, all built toward equipping researchers and developers with the tools necessary to build and deploy ML powered applications.

TensorFlow employs Python to provide a front-end API while executing applications in C++. Developers can create dataflow graphs which describe how data moves through a graph, or a series of processing nodes. Each node in the graph is a mathematical operation; the connection between nodes is a multidimensional data array, or tensor.

While TensorFlow is the ML Framework of choice in the industry, increasingly researchers are leaving the platform to develop for PyTorch.

PyTorch

PyTorch

PyTorch is a library for Python programs that facilitates deep learning. Like TensorFlow, PyTorch is Python-based. Think of it as Facebook’s answer to Google’s TensorFlow—it was developed primarily by Facebook’s AI Research lab. It’s flexible, lightweight, and built for high-end efficiency.

PyTorch features outstanding community documentation and quick, easy editing capabilities. PyTorch facilitates deep learning projects with an emphasis on flexibility.

Studies show that it’s gaining traction, particularly in the ML research space due to its simplicity, comparable speed, and superior API. PyTorch integrates easily with the rest of the Python ecosystem, whereas in TensorFlow, debugging the model is much trickier.

Microsoft Cognitive Toolkit (CNTK)

71

Microsoft’s ML framework is designed to handle deep learning, but can also be used to process large amounts of unstructured data for machine learning models. It’s particularly useful for recurrent neural networks. For developers inching toward deep learning, CNTK functions as a solid bridge.

CNTK is customizable and supports multi-machine back ends, but ultimately it’s a deep learning framework that’s backwards compatible with machine learning. It is neither as easy to learn nor deploy as TensorFlow and PyTorch, but may be the right choice for more ambitious businesses looking to leverage deep learning.

IBM Watson

IBM-Watson

IBM Watson began as a follow-up project to IBM DeepBlue, an AI program that defeated world chess champion Garry Kasparov. It is a machine learning system trained primarily by data rather than rules. IBM Watson’s structure can be compared to a system of organs. It consists of many small, functional parts that specialize in solving specific sub-problems.

The natural language processing engine analyzes input by parsing it into words, isolating the subject, and determining an interpretation. From there it sifts through a myriad of structured and unstructured data for potential answers. It analyzes them to elevate strong options and eliminate weaker ones, then computes a confidence score for each answer based on the supporting evidence. Research shows it’s correct 71% of the time.

IBM Watson is one of the more powerful ML systems on the market and finds usage in large enterprises, whereas TensorFlow and PyTorch are more frequently used by small and medium-sized businesses.

What’s Right for Your Business?

Businesses looking to capitalize on artificial intelligence do not have to start from scratch. Each of the above ML Frameworks offer their own pros and cons, but all of them have the capacity to enhance workflow and inform beneficial business decisions. Selecting the right ML framework enables businesses to put their time into what’s most important: innovation.

Can Cloud Integration Take Your App to the Next Level?

Android Development, iOS Development, , , , , , , , , , , , , , , , ,

In continuation of our exploration of the top 5 app development trends for 2016, this week we will be exploring cloud integration. Check out our master list in our blog App to the Future and be on the lookout for next week’s blog when we explore Security.

While the ethereal nature of the cloud makes it a mystery to many consumers, the cloud has engrained itself in businesses across the globe. Studies show the global market for cloud equipment will reach $79.1 billion by the year 2018.

A lot has changed since we last tackled the cloud in our 2013 blog What Can Cloud-Based Mobile App Development Do For Your Company? The aspects which haven’t changed are the basics: the cloud reduces operating costs by taking the computing load off of local and dedicated servers, it improves collaboration, and it fosters a streamlined collaborative environment.

CLOUD FOR CONSUMER USE

In the past 5 years, Amazon, Google and Apple have all monetized clouds for consumer use. These cloud offerings break up into two categories:

PUBLIC CLOUD: iCloud, Google Drive and Amazon Web Services, in addition to Dropbox and Box, all offer public cloud storage offerings. These clouds are accessible to the public through the Internet.

PRIVATE CLOUD: While the public cloud is available on the public web, the private cloud is designed for a single organization. These offerings are often crafted by cloud providers such as Rackspace, VMware, Microsoft and OpenStack.

HYBRID CLOUD ADOPTION

One of the major cloud trends in the past few years has been the adoption of the hybrid cloud. The hybrid cloud uses a mix of on-premises, private cloud and the public cloud to customize storage needs. Hybrid cloud adoption jumped from 74% in 2014 to 82% in 2015. The hybrid cloud has gained popularity because it allows businesses to section off their storage needs and leverage the advantages of the private cloud and the public cloud while keeping the disadvantages at bay.

CLOUD INTEGRATION

One of  the most interesting trends in the cloud for mobile developers is cloud integration. Device integration entails that multiple applications share data in the cloud and communicate either directly or through third-party software.

A great test case is Apple’s use of the iCloud to promote device continuity. Apple uses the iCloud to make sure files and apps which are synced to the user’s MacBook are also synced to iPhone, iPads, and Apple Watch. This allows the user to alternate between devices seamlessly and encourages them to buy Apple products.

For app developers, cloud integration is a major trend within the industry. With Apple leading the charge, many app developers and their clients want their iOS apps to integrate with iCloud with the intention of luring users to download apps on multiple devices and use them seamlessly. Cloud-integrated apps represent an investment in both time and money for developers.  In order to justify the effort, the app should appeal to tablet, smartphone, and perhaps also desktop users. Cloud integration represents a major opportunity for developers to increase brand continuity by creating a user experience which is consolidated across devices and informed by data across the enterprise.

Learn more about device integration with the iCloud via Apple’s Developer website.

For those with their sights set on cloud integration, here are some of the best third-party cloud data integration software options on the market:

Jitterbit

Zapier

Microsoft Azure

Informatica Cloud Integration

Samsung and Microsoft Team Up for Galaxy S6–What It Means for Apple, Android, and Windows

Android Development, iOS Development, , , , , , , , , , , , , , , , ,

Samsung is looking to step up its smartphone game.

Recently, there have been reports that the Samsung Galaxy S6 and S6 Edge devices will come pre-packaged with Microsoft apps despite running on the Android OS. Coming only a few weeks after the two tech titans settled a royalty dispute out of court, the move is sure to raise eyebrows–especially those at Android.

At the Mobile World Congress, Samsung revealed the Galaxy S6 and S6 Edge will come bundled with Skype, OneDrive, and OneNote applications preinstalled in a “Microsoft Apps” folder. The Galaxy S6 also comes with 115GB of free OneDrive storage for two years as part of a new partnership with Microsoft. It’s possible Samsung may also include Microsoft Office mobile with their phones, but they did not reveal this during the demo.

Windows Central alleges Samsung is looking to make more phones on the Windows OS in 2015. They have had limited success doing so in the past, but perhaps the reputation for efficiency of the Windows OS is drawing them to expand their repertoire, or perhaps they intend to create more affordable hardware on Windows.

Samsung has been attempting to reduce their reliance on the Android OS for some time now. They created Tizen, their own operating system, back in with the intent of competing with Android, iOS, and Windows. After several delays, Samsung recently launched their first smartphone utilizing Tizen: Samsung Z1, to middling results.

For Apple, the move certainly increases stock in Samsung’s direct iPhone 6 and 6 Plus competitor, but regardless, there’s no denying Samsung is shamelessly playing catch-up. The S6 features an updated fingerprint scanner and Samsung has recently announced their Apple Pay competitor: Samsung Pay.

For Microsoft, this is a huge win. The move represents Microsoft’s first time striking a deal with a non-Windows phone to preload any of its apps.

If you’re an Android fanboy, this cannot be pleasing. Although plenty of iPhones receive installs of Google Maps and other Google apps, they do not come prepackaged. Microsoft effectively poached a major Android device, and there’s virtually nothing Google can do about it.

Of course, we won’t know the ultimate impact until the Galaxy S6 and S6 Edge are released in April. Until then, we’ll chock it up to a win for Microsoft.

Mystic Media is an app development, web design, and strategic marketing firm located in Salt Lake City, Utah. Contact us today by clicking here or by phone at 801.994.6815

Making the Most of Your Tablet Design Part 1: The Top Tablets in 2014

Android Development, iOS Development, , , , , , , , , , , , , ,

In Part One of our two part series on tablets, we’ll explore the top tablets on the market in 2014 and what they have to offer both consumers and developers. Part Two will intensively explore the perks of cross-platform tablet application design and optimization.

The tablet market has experienced unprecedented growth with sales growing by over 50% in 2013. Although tablets have existed for over 20 years, the iPad revolutionized the industry when Steve Jobs suggested that a tablet was not simply a personal computer, but a game machine, music player and movie player as well. Rather than a laptop in a different shape, the iPad represented a portable media consumption device with a longer battery life and seemingly endless possibilities for applications. The top tablets for 2014 indicate the future of a business already in an upswing.

iPads are the gold standard when it comes to tablets. They represent the combination of mainstream consumer satisfaction with high-class IOS design and delivery. The top two iPads on the market are the iPad Air and the iPad Mini with Retina display. Retina display entails that the graphics on iPad Minis are so clear, one cannot decipher the pixels at a typical viewing distance. iPad Air has been widely praised as the best tablet on the market. It’s sleek and fast with a 1.4 GHz processor and Apple A7 chip, and while it is on the pricey side, you get what you pay for. IOS designed apps generally yield the most pleasant user experience, although, at Mystic Media, our experienced design team are highly capable of designing aesthetically impeccable cross-platform apps in  IOS, Android, Windows & Blackberry.

The top Android tablets include the Amazon Kindle Fire HDX, Samsung Galaxy Note 10.1, and the Google Nexus. Android’s open-source programming code allows for a greater variety of devices, as well as a more crowded app store. Developers love the Android OS because their apps are more easily approved and reach a broader audience. Many hail Samsung’s Galaxy Note 10.1 as the premier Android tablet on the market. With a 10.1 inch screen, 1.9 GHz processor speed, and 2560 x 1600 pixels, the Galaxy Note 10.1 is bigger, faster, and also more crisp than both iPads currently on the market. It’s also $100 cheaper than the iPad Air. Its main competitor within the Android tablet sphere is the Amazon Kindle Fire, which has the fastest processor speed among mainstream tablets with 2.2 GHz. Its graphics are also on par with the Galaxy Note, and it offers 24/7 video tech support. What sets the Amazon Kindle Fire apart is its compatibility with Amazon, which led cnet.com to label it a must-have tablet for Amazon Prime members.

The Microsoft Windows 8.1 OS is the middle child of Operating Systems, ignored but gifted in its own right. Microsoft’s Surface 2 is generally regarded as THE Windows tablet. The Surface 2 sets itself apart by offering a full contractible keyboard, making it a practical device for getting work done. The Asus Transformer Book T100 also offers a contractible keyboard, along with a 10.1 inch screen and the longest battery life of a mainstream tablet with almost 11 hours. While Windows tablets have major processing power and practicality, they are much less popular, and thus have fewer applications, which also works to the advantage of developers who will have less competition in the App store.

In deciding upon your tablet, you must evaluate the size of the screen, the processor speed, screen resolution, operating system, storage capacity, and battery life. You must ask how you’re going to use it–is it for media consumption, or productivity? Where you’re going to take it, will it be around the house or are you bringing it to work? Will you listen to music on it, or make music? For the casual user, the iPad generally fits the bill, for the developer, Android tablets tend to take the cake, and for the workhorse, Windows offers the best processing power and functionality.

At Mystic Media, we’re experts in the application design field and are highly capable designers for both tablets and smartphones of all Operating Systems. Contact us today at www.mysticmediasoft.com or by phone at 801.994.6815

Stay tuned next week for Part 2 of our two part series on Tablet design focusing on cross-platform optimization.