Moodle is a widely used open-source learning management system (LMS) designed to help educators create and manage online courses and training programs. It provides a platform for delivering educational content, facilitating online discussions, and tracking learner progress.

Key Features of Moodle:

1. Course Management: Educators can create and organize courses, add learning materials (such as documents, videos, and quizzes), and structure the course layout.
2. User Management: Moodle allows for user registration and enrollment. Educators can manage user roles (e.g., students, teachers, administrators) and permissions within the platform.
3. Interactive Learning: It supports various interactive tools, including forums, wikis, assignments, quizzes, and workshops, which enhance collaboration and engagement among learners.
4. Assessment and Grading: Educators can create assessments, track student performance, and provide feedback. Moodle has built-in grading tools to simplify this process.
5. Customization and Plugins: Being open-source, Moodle is highly customizable. Users can modify its appearance and functionality using themes and plugins that add features like gamification, analytics, or integration with third-party tools.
6. Mobile Compatibility: Moodle has mobile applications that allow learners to access courses and materials on their smartphones and tablets.

How Moodle Works:

1. Installation: Moodle can be installed on a web server. Users need to configure server requirements (PHP, MySQL, etc.) and set up the system.
2. Course Creation: Educators create courses by adding resources (files, links, etc.) and activities (quizzes, assignments) within the Moodle interface.
3. Enrollment: Students can enroll in courses either manually (by the teacher) or automatically (using self-enrollment options).
4. Learning Experience: Students access their courses, interact with materials, complete activities, and participate in discussions. They can also track their progress and grades through their dashboards.
5. Administration: Administrators manage the overall platform, including user accounts, course settings, and site-wide configurations.
6. Community Support: Being open-source, Moodle has a large community of users and developers who contribute to its development, provide support, and share resources.

Moodle is widely adopted in educational institutions, corporate training programs, and organizations seeking to deliver online learning experiences effectively. Its flexibility and robust features make it suitable for various learning environments.

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What is Visual Studio Code?

Visual Studio Code (VS Code) is a free, open-source code editor developed by Microsoft. It is widely used for programming, supporting multiple programming languages with features such as debugging, syntax highlighting, intelligent code completion, snippets, and code refactoring. It is lightweight, yet powerful, and runs on Windows, macOS, and Linux.

Top Use Cases of Visual Studio Code

1. Web Development: Supports HTML, CSS, JavaScript, and modern frameworks and libraries like React, Angular, and Vue.js.
2. Cloud-Native Development: Integrated with Azure and supports Docker and Kubernetes for developing and deploying microservices.
3. Data Science: Supports Python with extensions for Jupyter Notebooks, data visualization, and machine learning.
4. Application Development: Supports languages like C#, Java, Python, and others, useful for both desktop and mobile application development.
5. Extension Development: Developers can create their own extensions to add new languages, themes, debuggers, and to connect to additional services.

Features of Visual Studio Code

• IntelliSense: Provides smart completions based on variable types, function definitions, and imported modules.
• Debugging: Built-in debugging support for Node.js, JavaScript, and TypeScript, with extensions for other languages like Python and PHP.
• Git Integration: Offers built-in Git commands for committing, pulling, and pushing changes to a repository.
• Extensions: A rich ecosystem of extensions to enhance functionality for different languages and tools.
• Customizable: Highly customizable through JSON settings, allowing users to tweak the editor’s appearance and behavior.

Workflow of Visual Studio Code

1. Setup: Install VS Code and relevant extensions for your development environment.
2. Project Initialization: Open or create a new project and configure workspace settings.
3. Coding: Write code with IntelliSense assistance for more efficient coding.
4. Version Control: Use integrated Git support for version control.
5. Debugging: Use the built-in debugger to set breakpoints, inspect variables, and step through code.
6. Testing and Deployment: Utilize extensions for deploying applications or running tests.

How Visual Studio Code Works & Architecture

Visual Studio Code operates on an architecture that separates the frontend (the editor itself, which runs as a web application) from the backend services. The backend is powered by Node.js, which handles extensions and integrations. This separation ensures that VS Code remains responsive, regardless of what operations are being executed in the background.

How to Install and Configure Visual Studio Code

1. Download and Install: Go to the Visual Studio Code website, download the appropriate version for your OS, and install it.
2. Open and Setup: Open VS Code and install essential extensions (e.g., for Python, install the Python extension).
3. Configure Settings: Customize settings by accessing File -> Preferences -> Settings. Configure user and workspace settings as needed.

Step-by-Step Tutorials for Visual Studio Code: “Hello World” Program

Example with Python:

Install Python Extension: From the extensions tab, search for ‘Python’ and install it.

1. Create a New File:
   • Open VS Code, then go to File > New File and save it with an appropriate name, like hello_world.py if you are using Python.
2. Write the Code:
   • For a Python file, enter: print("Hello, World!")
3. Run the Program:
   • Open the integrated terminal (Terminal > New Terminal) and type python hello_world.py to execute the code.
4. View Output:
   • The message “Hello, World!” will be displayed in the terminal, indicating that your program has run successfully.

Thanks for visiting

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I want to learn Linux. Where do I start?”

If you want to learn Linux, here are some steps to help you get started:

1. Choose a Linux distribution: Select a Linux distribution that suits your needs. Popular choices include Ubuntu, Fedora, Debian, and CentOS. Each distribution has its own features and community support, so choose one that aligns with your goals.
2. Create a Linux environment: Install your chosen distribution on a computer or set up a virtual machine using virtualization software like VirtualBox or VMware. This will provide you with a dedicated environment to practice Linux without affecting your primary operating system.
3. Familiarize yourself with the Linux command line: Start by learning the basics of the Linux command line interface (CLI). Understand important commands like ls (list files and directories), cd (change directory), mkdir (create directory), rm (remove file/directory), etc. This knowledge will be key to working efficiently in a Linux environment.
4. Learn shell scripting: Shell scripting is an essential skill that allows you to automate tasks and create custom scripts. Start by understanding shell syntax, variables, loops, and conditionals. Bash is the most common shell used in Linux, so focusing on Bash scripting will be beneficial.
5. Understand the Linux file system: Learn how files and directories are organized in Linux. Understand the root directory (/), home directory (~), and important system directories like /bin, /etc, /usr, etc. Learn file permissions and ownership concepts to manage access to files and directories.
6. Learn package management: Familiarize yourself with the package management system of your chosen distribution. Commands like apt (Advanced Package Tool), yum, dnf, or zypper help you install, upgrade, and manage software packages in Linux.
7. Practice, practice, practice: The best way to learn Linux is by doing hands-on exercises and projects. Set up personal projects, solve coding challenges, or participate in open-source communities to gain practical experience and improve your skills.

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Once an enterprise Linux specialist, Red Hat is now providing answers to some of the biggest questions being asked of corporate and government IT.

The company’s OpenShift container platform, for instance, was key to the modernisation of New Zealand Customs’ legacy CusMod applications, taking them from a dependence on proprietary hardware into hybrid cloud. 

The fact that Datacom engineered that shift along with Section6 providing modernisation services points to another aspect of the vendor’s strategy — apart from having what country manager Derek Wilson says are a couple of direct customers, Red Hat remains a channel-based business.

Red Hat has two local distributors, Ingram Micro and Tech Data.

Key local partners include Spark, OSS, Enterprise IT, Section 6, Solnet as well as Datacom and a few others.

Global partners include, of course, parent company IBM as well as HP, DXC, Tech Mahindra and some of the big global systems integrators.

“If we look at where partners make money, which I guess is the important thing, firstly we sell a subscription, for instance, initially to Red Hat Enterprise Linux, which covers support, documentation, training IP, patches and downloads and so on,” Wilson told Reseller News.

“Our partners can make money from selling subscriptions and they can make some margins on that but where they really differentiate themselves is on services wrapped around our products.” 

These could be Implementation services, transformation services, project services and, in a lot of cases, managed services.

That was certainly part of the mix for Customs, with CusMod now served from Datacom’s Govt.Container platform-as-a-service (PaaS) service.

“It was the first implementation in the region, I think, certainly locally, of a local provider doing OpenShift as a service,” Wilson said.

IBM is a shareholder but also a partner. 

“Red Hat also partner with a lot of IBM competitors, DXC for example,” Wilson said. “We have been very clear about keeping our independence and neutrality and working with all the other partners.”

Red Hat began supplying its flavour of enterprise Linux 25 years ago against some stiff competition. Portfolio changes added virtualisation, middleware and, more recently, the OpenShift and Ansible automation engines.

“What we’ve been doing over the years is growing the portfolio and growing the amount of customers that use that portfolio,” Wilson said.

While still an open source company, Red Hat is no longer just “the Linux company”.

Customers are embracing the power of OpenShift to break down monolithic legacy applications into microservices.

They are also using Ansible for automation and cost savings as they shift to hybrid cloud models.

“We are seeing a big push on the OpenShift products and the Ansible products,” Wilson said.

“We are an open source company so innovation is key to us. We are constantly releasing new versions of software.”

Last year, for instance, Red Hat announced it was including its Insights for Linux proactive monitoring platform in its enterprise Linux subscriptions to help improve customers operating environments. New tools have also been added to OpenShift to speed the development of customer environments.

It has been said that while IBM has bought Red Hat, it was actually Red Hat that had taken over IBM, but Wilson doesn’t go that far.

“We’re not taking over IBM, but they are learning about our culture,” he said. “They were making cultural changes already but we feel they are embracing our culture which is a good thing.”

So, being an enabler of hybrid cloud, what is Red Hat’s take on the much anticipated arrival of Microsoft’s local cloud region in New Zealand?

Short answer, excitement.

“We actually exist in multiple clouds, Revera/CCL, AWS, Google, Microsoft, Alibaba — all have our product in them from buying Red Hat Enterprise Linux to managed OpenShift,” Wilson said.

“We are really excited by it. We think it is a significant advantage. We’ve seen our footprint in Azure grow quite significantly over the last couple of years and with government and private clients it is going to be quite significant.

“From a cloud provider perspective, IBM has its own cloud, but we don’t just work with IBM cloud.”

Red Hat has been equally careful to keep its partner programmes separate from IBM’s, as well as its premises and facilities.

Red Hat has also launched an A/NZ regional innovation lab to help customers improve processes more quickly. Wilson said one process was reduced from six days to five minutes.

ANZ New Zealand was one that used the capability, to transition from routine, repeatable network operations tasks to an approach focused on automation.

There is a cultural aspect to the lab as well, Wilson explained.

“We bring disparate teams together who haven’t worked together before. We try and change the culture for collaboration, short sprints and much more openness.”

ANZ Bank, for instance, sought to support its commitment to talent acquisition and retention. To support these, the bank’s teams needed guidance on adopting agile development approaches and Ansible community-developed automation technology.

It was a similar story at Queensland’s Heritage Bank, where the goal was speeded development cycles. The teams there brought in a new payments system 10 months early. 

“It was about bringing groups of people together, in their case including the CEO as well.”

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Microsoft has released DeepSpeed, a new deep learning optimization library for PyTorch, that is designed to reduce memory use and train models with better parallelism on existing hardware.

According to a Microsoft Research blog post announcing the new framework, DeepSpeed improves PyTorch model training through a memory optimization technology that increases the number of possible parameters a model can be trained with, makes better use of the memory local to the GPU, and requires only minimal changes to an existing PyTorch application to be useful.

It’s the minimal impact on existing PyTorch code that has the greatest potential impact. As machine learning libraries grow entrenched, and more applications become dependent on them, there is less room for new frameworks, and more incentive to make existing frameworks more performant and scalable.

PyTorch is already fast when it comes to both computational and development speed, but there’s always room for improvement. Applications written for PyTorch can make use of DeepSpeed with only minimal changes to the code; there’s no need to start from scratch with another framework.

One way DeepSpeed enhances PyTorch is by improving its native parallelism. In one example, provided by Microsoft in the DeepSpeed documentation, attempting to train a model using PyTorch’s Distributed Data Parallel system across Nvidia V100 GPUs with 32GB of device memory “[ran] out of memory with 1.5 billion parameter models,” while DeepSpeed was able to reach 6 billion parameters on the same hardware.

Another touted DeepSpeed improvement is more efficient use of GPU memory for training. By partitioning the model training across GPUs, DeepSpeed allows the needed data to be kept close at hand, reduces the memory requirements of each GPU, and reduces the communication overhead between GPUs.

A third benefit is allowing for more parameters during model training to improve prediction accuracy. Hyperparameter optimization, which refers to tuning the parameters or variables of the training process itself, can improve the accuracy of a model but typically at the cost of manual effort and expertise.

To eliminate the need for expertise and human effort, many machine learning frameworks now support some kind of automated hyperparameter optimization. With DeepSpeed, Microsoft claims that “deep learning models with 100 billion parameters” can be trained on “the current generation of GPU clusters at three to five times the throughput of the current best system.”

DeepSpeed is available as free open source under the MIT License. Tutorials in the official repo work with Microsoft Azure, but Azure is not required to use DeepSpeed.

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Netflix has open sourced Metaflow, an internally developed tool for building and managing Python-based data science projects. Metaflow addresses the entire data science workflow, from prototype to model deployment, and provides built-in integrations to AWS cloud services. 

Machine learning and data science projects need mechanisms to track the development of the code, data, and models. Doing all of that manually is error-prone, and tools for source code management, like Git, aren’t well-suited to all of these tasks.

Metaflow provides Python APIs to the entire stack of technologies in a data science workflow, from access to the data through compute resources, versioning, model training, scheduling, and model deployment.

According to Metaflow’s introductory documentation, Netflix built Metaflow to provide its own data scientists and developers with “a unified API to the infrastructure stack that is required to execute data science projects, from prototype to production,” and to “focus on the widest variety of ML use cases, many of which are small or medium-sized, which many companies face on a day to day basis.”

Metaflow does not favor any particular machine learning framework or data science library. Metaflow projects are just Python code, with each step of a project’s data flow represented by common Python programming idioms. Each time a Metaflow project runs, the data it generates is given a unique ID. This lets you access every run—and every step of that run—by referring to its ID or user-assigned metadata.

Netflix recommends running Metaflow on AWS. The company offers a sandboxed version of Metaflow there (with restrictions on storage and data lifetime) for developers to experiment with the framework.

The first public release of Metaflow, Metaflow 2.0, lacks some of the features Netflix uses internally, such as support for the R language or in-memory processing of large data by way of DataFrames. But Netflix is willing to make those features available if their corresponding GitHub issues attract enough support.

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China and the US are the two leading AI superpowers that have dominated research

Microsoft Corp co-founder Bill Gates spoke out against protectionism in technological research around topics like artificial intelligence, arguing that open systems will inevitably win out over closed ones.

In conversation with Bloomberg News editor-in-chief John Micklethwait at the New Economy Forum in Beijing on Thursday, Gates was skeptical about the idea that ongoing United States (US)-China trade tensions could ever lead to a bifurcated system of two internets and two mutually exclusive strands of tech research and development.

AI is very hard to put back in the bottle, Gates said, and whoever has an open system will get massively ahead by virtue of being able to integrate more insights from more sources. Citing Microsoft’s AI research in Beijing, Gates pondered the rhetorical question of whether it was producing Chinese AI or American AI. In the case of Microsoft’s UK research campus in Cambridge and the findings it produces, he said that almost every one of those papers is going to have some Chinese names on it, some European names on it and some Americans names on it.

China and the US are the two leading AI superpowers that have dominated research, however cooling political relations between them have slowed the international collaboration that underpins innovation. Huawei Technologies Co, Beijing’s tech champion, has been subject to a variety of sanctions from Washington, in part because Chinas rapid AI development is perceived as a rising threat.

Gates said he was more worried today than five years ago about the rise of nationalist and protectionist political tendencies across the globe, and that he now wonders whether that will prove a cyclical trend or a more permanent change. Still, as far as the US and China were concerned, he said hes even more passionate about the value of engagement than ever.
The other key takeaways from the talk

Gates said there is no doubt solar and wind are key parts of a new energy mix needed to battle climate change. “Quite a bit of nuclear may be required to fill in for fossil fuels as we move to zero carbon. But he doubts a carbon tax would be realistic in the US Republicans have largely sworn off the idea and, by and large, he said, Democrats are not pushing it as a key priority, either. The ability of political leaders to convince their electorates of the benefits and value of globalization has gone down,” said Gates.

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The team is testing robots that can help out in workplace environments, though right now, these early robots are focused on learning how to sort trash. Here’s what one of them looks like — it reminds me of a very tall, one-armed Wall-E (ironic, given what the robots are tasked to do):

Here’s a GIF of a robot actually sorting a recyclable can from a compost pile to a recycling pile. This is wild — check out how the arm actually grasps the can:

The concept of grasping something comes pretty easily to most humans, but it’s a very challenging thing to teach a robot, and Everyday Robot’s robots get their practice in both the physical world and the virtual world. In a tour of X’s offices, Wired described how a “playpen” of nearly 30 of the robots (supervised by humans) spend their daytime hours sorting trash into trays for compost, landfill, and recycling. At night, Everyday Robot has virtual robots practice grabbing things in simulated buildings, according to Wired. That simulated data is then combined with the real world data, which is given to the robots in a system update every week or two.

With all that practice, X says the robots are actually getting pretty good at sorting, apparently putting less than 5 percent of trash in the wrong place (X’s humans put 20 percent of trash in the wrong pile, according to X).

That doesn’t mean they’re remotely ready to replace human janitors, though. Wired observed one robot grasping thin air instead of the bowl in front of it, then attempting to put the “bowl” down. Another lost one of its “finger” during the demo. Engineers also told Wired that, at one point, some robots weren’t moving through a building because some types of light caused their sensors to hallucinate holes in the floor.

There are whole startups dedicated to the problem of teaching a robot how to grasp, such as Embodied Intelligence and the nonprofit OpenAI. And Google, also owned by Alphabet, has done research into grasping — check out this 2016 video of some Google-made robot arms trying to grab differently-sized objects:

But progress is being made beyond the work X and Google are doing. For example, Boston Dynamics (formerly owned by Google) released this video in 2018 of its SpotMini robot grabbing a doorknob to open a door for a friend:

And research from Google from this March showed off a robot that could pick up objects and, over time, learn the best way to throw a specific shape:

Despite all this research, Google and Alphabet have a troubled history with robotics. Google’s last serious attempt at robotics work started in 2013 in a division led by Android co-founder Andy Rubin. Though that division made some high-profile acquisitions, including Boston Dynamics, nothing concrete came from it, and Rubin departed from Google in 2014 following allegations of sexual harassment. Google is apparently dipping its toes back into robotics, though, based on a report from March of this year, and its new robots are also learning how to grab, but it seems Google’s work is different from that of Everyday Robot’s.

Everyday Robot lead Hans Peter Brondmo told Wired that he hopes to one day make a robot that can assist the elderly. But he also acknowledged something like that might be a few years out — so for now, it seems the robots will keep getting better at sorting trash.

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It’s a fact that Artificial technology is increasingly making our lives easier. If we think about it, every second component is now attached with some sort of machine learning tool that makes it work by minimum human interference. 

AI technology is transforming every sequence of our lives, therefore machine learning is also growing with a newer speed, and so are the innovations of artificial intelligence development companies. 

Transportation has grown a lot more than the commutation methods and assisting the communication requirements of the clients. The customers are gradually becoming addicted to handling complex tasks from mobile phones. 

Best Machine Learning Frameworks To Use In 2020

The proliferation of various machine learning frameworks has justified the huge demand of industries to hire app AI developers who can work with their esteemed AI-enabled apps and solutions.

Below are some of the best machine learning frameworks that every Artificial Intelligence Development Company should be aware of: 

1. Keras

For simplifying the deep learning model creation, the open-source software library Keras was built in 2015. The software framework is written in Python and is perfect to be deployed over other AI technologies such as TensorFlow, Theano and Microsoft Cognitive Toolkit. 

Keras is wooing users with modularity and ease of extensibility for a better mobile app development solution. The framework is suitable for the need for machine learning libraries as an artificial intelligence testing tool, which enables fast prototyping and supports recurring and convolutional networks. 
Also for the machine learning library which runs optimally on Graphics processing units and Central processing units. Keras patronizes the recurring layer, supporting convolution and a combination of both.

2. TensorFlow

TensorFlow was released in 2015 and is an open-source ML framework. TensorFlow is compatible with a variety of platforms and can be used and deployed easily. The framework is the most extensively used framework by AI developers for the machine learning tasks. 

It is created by Google for augmenting research work and production tasks. Tensorflow is widely used by well-known companies such as Dropbox, Intel, Twitter, Uber, and Intel. The framework is available in many languages such as C++. Haskell, Go, Rust, Python, and JavaScript. 

It also supports third-party packages for other extensively used programming languages. Every AI developer can use the framework for developing neural networks and other computational models with FlowGaphs. 

3. Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit, an AI framework solution, was released in 2016, empowering machine learning projects with new capabilities. It’s an open-source that can train deep learning algorithms for functions similar to the human brain. In other words, it’s been so effectual and flawless. 

Among its several features, some include highly optimized and enriched components focusing on the introduction of artificial intelligence technology. These components are capable of handling data from C++, Python or BrainScript, ability in providing productive use of resources, easy integration with Microsoft Azure, and interoperation with NumPy.

4. Apache Mahout

Apache Mahout is a machine learning framework, which makes use of linear algebra. It also does use Scala DSL. The framework is equally suitable for the majority of modern Artificial Intelligence Problems. 

5. Accord.NET

Another machine learning framework, Accord.NET was released in 2010. It is dedicatedly written in C#. Being a popular framework, it encompasses a large range of libraries where it becomes easy to build numerous apps in statistical data processing, image processing, artificial neural networks, and many others. 

6. Theano

It’s another prominent open-source Python machine learning framework that was released in 2007. Being one of the prominent libraries, it’s been regarded as a benchmark that has transformed numerous advancements in deep learning. 

It allows the user to easily fashion numerous machine learning mobile app development solution models. Theano is empowered to ease the due process of interpretation, optimization, and assessment of mathematical expressions. Furthermore, being optimized for GPUs, it also offers efficient symbolic differentiation.

7. Scikit-learn

It’s an open-source library that is developed specifically for machine learning. It was first introduced in 2007. Scikit-learn has been designed for Matplotlib, SciPy, and NumPy, as well as other open-source projects. It duly focuses on data analysis and data mining. 

The imperative aspect to be considered is that it’s written in Python. It encompasses numerous machine learning models. These models include clustering, regression, classification, and dimensionally reduction.

8. Amazon Machine Learning

Amazon Web Services has a wide machine learning framework. It is used by thousands of businesses and enterprises around the globe. The platform works with major AI frameworks and is known for offering ready-to-use artificial intelligence solutions.

9. Torch

It’s one of the preferential options available today. The torch was released in 2002, a machine learning library offering a high range of algorithms for deep learning. It comes with optimized speed and flexibility while handling your machine learning projects. 

By mitigating undesirable complexities in between a dedicated process, it supports effectively.  It comes with Lua – scripting language and underlying C implementation for AI developers. Furthermore, it encapsulates enriched features like N-dimensional arrays, linear algebra routines, efficient GPU support for Android and iOS platforms, etc. 

10. Caffe

The current developments of open source AI have emboldened consistent R&D in relevant dimensions. Caffe, released in 2017, is known as a smaller machine learning framework for an artificial intelligence development company focusing on speed, modularity, and expressiveness. Convolutional Architecture for Fast Feature Embedding (Caffe) introduces the Python interface and is written in C++. 

Apart from being an ideal framework, it is enriched with valuable features. These include extensive code facilitating active development, vibrant community stimulating growth, expressive architecture inspiring innovation and fast performance accelerating industry deployment.

Final Thoughts 

Today, machine learning is an integral part of any software development task. Every device is built considering the possible integration with AI tools. Therefore, it becomes necessary to select the right framework and evaluate that for the optimum results. 

Before initiating the machine learning application, the selection of one technology from many options is a difficult task. It is imperative to evaluate a few options before building the final decision. Furthermore, one should also learn how the machine learning frameworks work, though hiring app developers is the inevitable need of businesses today. 

There are also other machine learning frameworks available in the market, but the choice entirely depends on the need of the project. In addition to this, if you still have some questions regarding how to use machine learning and artificial in a mobile app, just leave a comment below and our experts will get back to you at the earliest. 

The post Top Machine Learning Frameworks For AI Development Company [2020] appeared first on Artificial Intelligence.

Machine learning (ML) continues to amaze us with its abilities and is set to transform the economic structure of many industries — from producers of widgets to financial analysts and health care providers. But many IT and operations practitioners are struggling to put the rapid advances in ML to work for their organizations.

To take advantage of ML in your environment, you should start with an understanding of the benefits you aim to achieve, such as improving efficiency, accuracy and safety, or cutting the cost of delivery of goods/services. Next, how will you get there? Roll your own? Integrate open-source code bases? Buy a product or service? Use a public cloud?

These decisions need to reflect the realities of the ML domain, your business needs and the skills you have available.

ML Is One Ingredient, Not The Whole Solution

Because ML is developing so fast, I believe solutions should be able to take advantage of the powerful techniques being made available in open-source. Ensure your solution is “ML agile” and able to upgrade to newer algorithms easily. An application architecture that allows you to plug in the major open-source frameworks could save years of effort and get your solution into production quicker.

The principal challenge with adopting any ML-based solution is the effort needed to build and tune models to your environment — which demands highly skilled engineers/data scientists. For that reason, insist that your integrators manage a solution throughout its life, and don’t sign off on a bespoke app until you have proof that it works. Remember that you may not know if a solution delivers significant benefits until you have seen it work in practice for long enough to establish the operational costs of false positives and false negatives.

The Power Of Open-Source

Today, the leading edge of innovation in ML algorithms and tools is to be found in open-source code bases that enjoy broad support. The community development model appeals to researchers, end users and developers – users can be sure they won’t be stranded with a dead-end proprietary stack, and developers can confidently invest time and effort into widely used code bases, developing skill sets that are portable across projects, employers and even clouds. The open-source ML tools are not only the leading edge of algorithm development but also embody the de facto work practice of many data scientists.

Worth noting is that the near-universal collaboration on a common set of open-source tools does not commoditize ML per se. Sure, the code is free, but there has been a sea change in the community development model: Leading researchers and practitioners pool their efforts to deliver a common code base of great value, freely available to all. It’s a fascinating trend that also serves the competitive interests of major contributors – like Google, Amazon and Microsoft – that gain advantage by ensuring competitors with proprietary solutions cannot keep up. For the cloud providers, ML-based workloads are a great way to monetize their cloud infrastructure, from storage through central processing unit, memory and graphics processing unit/tensor processing unit (TPU) resources.

Finally, note that the free availability of algorithms has not killed the value chain. Chip vendors, including Google with its TPU, NVIDIA and over 40 startups working on hardware acceleration for ML, aim to monetize the resource-hungry training and inference with proprietary acceleration hardware for clouds or on-prem devices.

What’s Next For ML?

Successful open-source projects attract developers and researchers, and successful ML open-source software projects become focal points of innovation for the industry, accelerating the state-of-the-art and delivering the power of collective contributions to all stakeholders. Contrast the strong community support for Google TensorFlow and the almost complete absence of a community around the proprietary IBM Watson. The integration of TensorFlow into a comprehensive set of consumer and enterprise solutions will build preference for Google services and TPUs, keep developers focused on Google technologies and give Google immense bragging rights – promoting itself in every ML success on the part of its community.

Cloud providers have massive marketing budgets and immense reach, and they already use ML to differentiate their packaged services – embedding AI smarts into applications they monetize via a subscription licensing model. This approach saves customers from having to understand the technology. Providers also win by building strong affinity with the user by incorporating ML-powered features that quickly make their way into SaaS apps.

Open-source ML is fuel for a race favoring execution and development efficiency. Winners will capitalize on the ready availability of powerful tools to deliver economic benefits quickly and at a reasonable cost. Those that adhere to the mantra of proprietary secret sauce may succeed tactically, but I believe they are doomed to eventual failure – slower adoption and less developer support.

Although the algorithms in major open-source frameworks form an immensely valuable community commons, it is the complete solution that is of value for your use case. There is plenty of room for proprietary innovation – delivering vertically integrated packages for specific industries, and infrastructure and packaging that makes these powerful technologies simple enough for non-data scientists to easily adopt and scale.

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