In the past few years, you might have noticed the increasing pace at which vendors are rolling out platforms that serve the AI ecosystem, namely addressing data science and machine learning (ML) needs. The Data Science Platform and Machine Learning Platform are at the front lines of the battle for the mind share and wallets of data scientists, ML project managers, and others that manage AI projects and initiatives. If youre a major technology vendor and you dont have some sort of big play in the AI space, then you risk rapidly becoming irrelevant. But what exactly are these platforms and why is there such an intense market share grab going on?

The core of this insight is the realization that ML and data science projects are nothing like typical application or hardware development projects. Whereas in the past hardware and software development aimed to focus on the functionality of systems or applications, data science and ML projects are really about managing data, continuously evolving learning gleaned from data, and the evolution of data models based on constant iteration. Typical development processes and platforms simply dont work from a data-centric perspective.

It should be no surprise then that technology vendors of all sizes are focused on developing platforms that data scientists and ML project managers will depend on to develop, run, operate, and manage their ongoing data models for the enterprise. To these vendors, the ML platform of the future is like the operating system or cloud environment or mobile development platform of the past and present. If you can dominate market share for data science / ML platforms, you will reap rewards for decades to come. As a result, everyone with a dog in this fight is fighting to own a piece of this market.

However, what does a Machine Learning platform look like? How is it the same or different than a Data Science platform? What are the core requirements for ML Platforms, and how do they differ from more general data science platforms? Who are the users of these platforms, and what do they really want? Lets dive deeper.

What is the Data Science Platform?

Data scientists are tasked with wrangling useful information from a sea of data and translating business and operational informational needs into the language of data and math. Data scientists need to be masters of statistics, probability, mathematics, and algorithms that help to glean useful insights from huge piles of information. A data scientist creates data hypothesis, runs tests and analysis of the data, and then translates their results for someone else in the organization to easily view and understand. So it follows that a pure data science platform would meet the needs of helping craft data models, determining the best fit of information to a hypothesis, testing that hypothesis, facilitating collaboration amongst teams of data scientists, and helping to manage and evolve the data model as information continues to change.

Furthermore, data scientists dont focus their work in code-centric Integrated Development Environments (IDEs), but rather in notebooks. First popularized by academically-oriented math-centric platforms like Mathematica and Matlab, but now prominent in the Python, R, and SAS communities, notebooks are used to document data research and simplify reproducibility of results by allowing the notebook to run on different source data. The best notebooks are shared, collaborative environments where groups of data scientists can work together and iterate models over constantly evolving data sets. While notebooks dont make great environments for developing code, they make great environments to collaborate, explore, and visualize data. Indeed, the best notebooks are used by data scientists to quickly explore large data sets, assuming sufficient access to clean data.

However, data scientists cant perform their jobs effectively without access to large volumes of clean data. Extracting, cleaning, and moving data is not really the role of a data scientist, but rather that of a data engineer. Data engineers are challenged with the task of taking data from a wide range of systems in structured and unstructured formats, and data which is usually not clean, with missing fields, mismatched data types, and other data-related issues. In this way, the role of a data engineer is an engineer who designs, builds and arranges data. Good data science platforms also enable data scientists to easily leverage compute power as their needs grow. Instead of copying data sets to a local computer to work on them, platforms allow data scientists to easily access compute power and data sets with minimal hassle. A data science platform is challenged with the needs to provide these data engineering capabilities as well. As such, a practical data science platform will have elements of data science capabilities and necessary data engineering functionality.

What is the Machine Learning Platform?

We just spent several paragraphs talking about data science platforms and not even once mentioned AI or ML. Of course, the overlap is the use of data science techniques and machine learning algorithms applied to the large sets of data for the development of machine learning models. The tools that data scientists use on a daily basis have significant overlap with the tools used by ML-focused scientists and engineers. However, these tools arent the same, because the needs of ML scientists and engineers are not the same as more general data scientists and engineers.

Rather than just focusing on notebooks and the ecosystem to manage and work collaboratively with others on those notebooks, those tasked with managing ML projects need access to the range of ML-specific algorithms, libraries, and infrastructure to train those algorithms over large and evolving datasets. An ideal ML platforms helps ML engineers, data scientists, and engineers discover which machine learning approaches work best, how to tune hyperparameters, deploy compute-intensive ML training across on-premise or cloud-based CPU, GPU, and/or TPU clusters, and provide an ecosystem for managing and monitoring both unsupervised as well as supervised modes of training.

Clearly a collaborative, interactive, visual system for developing and managing ML models in a data science platform is necessary, but its not sufficient for an ML platform. As hinted above, one of the more challenging parts of making ML systems work is the setting and tuning of hyperparameters. The whole concept of a machine learning model is that it requires various parameters to be learned from the data. Basically, what machine learning is actually learning are the parameters of the data, and fitting new data to that learned model. Hyperparameters are configurable data values that are set prior to training an ML model that cant be learned from data. These hyperparameters indicate various factors such as complexity, speed of learning, and more. Different ML algorithms require different hyperparameters, and some dont need any at all. ML platforms help with the discovery, setting, and management of hyperparameters, among other things including algorithm selection and comparison that non-ML specific data science platforms dont provide.

The different needs of big data, ML engineering, model management, operationalization

At the end of the day, ML project managers simply want tools to make their jobs more efficient and effective. But not all ML projects are the same. Some are focused on conversational systems, while others are focused on recognition or predictive analytics. Yet others are focused on reinforcement learning or autonomous systems. Furthermore, these models can be deployed (or operationalized) in various different ways. Some models might reside in the cloud or on-premise servers while others are deployed to edge devices or offline batch modes. These differences in ML application, deployment, and needs between data scientists, engineers, and ML developers makes the concept of a single ML platform not particularly feasible. It would be a jack of all trades and master of none.''

As such, we see four different platforms emerging. One focused on the needs of data scientists and model builders, another focused on big data management and data engineering, yet another focused on model scaffolding and building systems to interact with models, and a fourth focused on managing the model lifecycle - ML Ops. The winners will focus on building out capabilities for each of these parts.

The Four Environments of AI (Source: Cognilytica)

The winners in the data science platform race will be the ones that simplify ML model creation, training, and iteration. They will make it quick and easy for companies to move from dumb unintelligent systems to ones that leverage the power of ML to solve problems that previously could not be addressed by machines. Data science platforms that dont enable ML capabilities will be relegated to non-ML data science tasks. Likewise, those big data platforms that inherently enable data engineering capabilities will be winners. Similarly, application development tools will need to treat machine learning models as first-class participants in their lifecycle just like any other form of technology asset. Finally, the space of ML operations (ML Ops) is just now emerging and will no doubt be big news in the next few years.

When a vendor tells you they have an AI or ML platform, the right response is to say which one?. As you can see, there isnt just one ML platform, but rather different ones that serve very different needs. Make sure you dont get caught up in the marketing hype of some of these vendors with what they say they have with what they actually have.

Excerpt from:

Theres No Such Thing As The Machine Learning Platform - Forbes

Even before autonomous vehicles become a regular sight on our streets, modern cars are quickly resembling sophisticated computers on wheels.Increasingly connected vehicles come with as many as 150 million lines of code, far exceeding the 145,000 lines of code required to land Apollo 11 on the Moon in 1969. Self-driving cars could require up to one billion lines of code.For manufacturers, passengers and repair shops alike, vehicles running on software rather than just machines represent an unprecedented world of highly complex mobility. Checking the engine, tires and brakes to find a fault will certainly no longer suffice.Seeking to build trust in the new generation of automotive innovation, Tel Aviv-based start-up Aurora Labs has developed software for what it calls the self-healing car a proactive and remote system to detect and fix potential vehicle malfunctions, and update and validate in-car software without any downtime.(From left) Aurora Labs co-founder & CEO Zohar Fox; co-founder & COO Ori Lederman; and EVP Marketing Roger Ordman (Credit: Aurora Labs)The automotive industry is facing its biggest revolution to date, Aurora Labs co-founder and chief operating officer Ori Lederman told The Jerusalem Post. The most critical aspect of all that sophistication and software coming into the car is whether you can trust it, even before you hand over complete autonomy to the car. It poses a lot of challenges to car-makers.New challenges, Lederman added, include whether software problems can be detected after selling the vehicle, whether problems can be solved safely and securely, and whether defects can be solved without interrupting car use. In 2018, some eight million vehicles were recalled in the United States due to software-based defects alone.The human body can detect when something is not quite right before you pass out, said executive vice president of marketing Roger Ordman. The auto-immune system indicates something is wrong and what can be done to fix it: raise your temperature or white blood count. Sometimes the body can do a self-fix, and sometimes thats not enough and needs an external intervention.Our technology has the same kind of approach detecting if something has started to go wrong before it causes a catastrophic failure, indicating exactly where that problem is, doing something to fix it, and keeping it running smoothly.The companys Line-Of-Code Behavior technology, powered by machine learning and artificial intelligence, creates a deep understanding of what software is installed on over 100 vehicle Engine Control Units (ECU), and the relationship between them. In addition to detecting software faults, the technology can enable remote, over-the-air software updates without any downtime.Similar to silent updates automatically implemented by smartphone applications, Ordman added, car manufacturers will be able to update and continuously improve software running on connected vehicles. Of course, manufacturers will be required to meet stringent regulations, developed by bodies including the UNECE, concerning cybersecurity and over-the-air updates.When we joined forces and started developing the idea, we knew our technology was applicable to any connected, smart device or Internet of Things device, said Lederman. The first vertical we wanted to start with is the one that needs us the most, and the biggest market. The need for detecting, managing, recovering and being transparent about software is by far the largest need in the automotive industry as they move from mechanical parts to virtual systems run by lines of code.Rather than requiring mass recalls, Aurora Labs self-healing software will be able to apply short-term fixes to ensure continued functionality and predictability, and subsequently implement comprehensive upgrades to the vehicles systems.The company, which has raised $11.5 million in fund-raising rounds since it was founded in 2016 by Lederman and CEO Zohar Fox, is currently working to implement its technology with some of the worlds leading automotive industry players, including major car-makers in Germany, the United States, Korea and Japan.The fast-growing start-up also has offices in Michigan and the North Macedonian capital of Skopje, and owns a subsidiary near Munich.Customers ought to start being aware of how sophisticated their cars are, said Lederman. When they buy a new car, they should want to ask the dealership that they have the ability to detect, fix and recover so they dont need to go the dealership. Its something they would want to have. Just as the safety performance of cars in Europe are ranked according to the five-star NCAP standard, Ordman believes there should be an additional star for software safety and security.There should be as many self-healing systems in place as possible to enable that, when inevitably something does go wrong, there are systems in place to detect and fix them and maintain uptime, said Ordman.Does the software running in the vehicle have the right cybersecurity in place? Does it have right recovery technologies in place? Can it continuously and safely improve over time?With these functionalities, youre not just dealing with five stars of the physical but adding another star for the software safety and security. It is about giving the trust to the consumer. Im getting a car that will safeguard me and my family as I move forward.

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Israelis develop 'self-healing' cars powered by machine learning and AI - The Jerusalem Post

If we talk about recent breakthroughs in the software community, machine learning and deep learning is a major contender the usage, adoption, and experimentation of deep learning has exponentially increased. Especially in the areas of computer vision, speech, natural language processing and understanding, deep learning has made unprecedented progress. GANs, variational autoencoders and deep reinforcement learning are also creating impressive AI results.

To know more about the progress of deep learning, we interviewed Ivan Vasilev, a machine learning engineer and researcher based in Bulgaria. Ivan is also the author of the book Advanced Deep Learning with Python. In this book, he teaches advanced deep learning topics like attention mechanism, meta-learning, graph neural networks, memory augmented neural networks, and more using the Python ecosystem. In this interview, he shares his experiences working on this book, compares TensorFlow and PyTorch, as well as talks about computer vision, NLP, and GANs.

Computer Vision and Natural Language processing are two popular areas where a number of developments are ongoing. In his book, Advanced Deep Learning with Python, Ivan delves deep into these two broad application areas. One of the reasons I emphasized computer vision and NLP, he clarifies, is that these fields have a broad range of real-world commercial applications, which makes them interesting for a large number of people.

The other reason for focusing on Computer Vision, he says is because of the natural (or human-driven if you wish) progress of deep learning. One of the first modern breakthroughs was in 2012, when a solution based on convolutional network won the ImageNet competition of that year with a large margin compared to any previous algorithms. Thanks in part to this impressive result, the interest in the field was renewed and brought many other advances including solving complex tasks like object detection and new generative models like generative adversarial networks. In parallel, the NLP domain saw its own wave of innovation with things like word vector embeddings and the attention mechanism.

There are two popular machine learning frameworks that are currently at par TensorFlow and PyTorch (Both had new releases in the past month, TensorFlow 2.0 and PyTorch 1.3). There is an ongoing debate that pitches TensorFlow and PyTorch as rivaling tech and communities. Ivan does not think there is a clear winner between the two libraries and this is why he has included them both in the book.

He explains, On the one hand, it seems that the API of PyTorch is more streamlined and the library is more popular with the academic community. On the other hand, TensorFlow seems to have better cloud support and enterprise features. In any case, developers will only benefit from the competition. For example, PyTorch has demonstrated the importance of eager execution and TensorFlow 2.0 now has much better support for eager execution to the point that it is enabled by default. In the past, TensorFlow had internal competing APIs, whereas now Keras is promoted as its main high-level API. On the other hand, PyTorch 1.3 has introduced experimental support for iOS and Android devices and quantization (computation operations with reduced precision for increased efficiency).

Ivan discusses his venture into the field of financial machine learning, being the author of an ML-oriented event-based algorithmic trading library. However, financial machine learning (and stock price prediction in particular) is usually not in the focus of mainstream deep learning research. One reason, Ivan states, is that the field isnt as appealing as, say, computer vision or NLP. At first glance, it might even appear gimmicky to predict stock prices.

He adds, Another reason is that quality training data isnt freely available and can be quite expensive to obtain. Even if you have such data, pre-processing it in an ML-friendly way is not a straightforward process, because the noise-to-signal ratio is a lot higher compared to images or text. Additionally, the data itself could have huge volume.

However, he counters, using ML in finance could have benefits, besides the obvious (getting rich by trading stocks). The participation of ML algorithms in the stock trading process can make the markets more efficient. This efficiency will make it harder for market imbalances to stay unnoticed for long periods of time. Such imbalances will be corrected early, thus preventing painful market corrections, which could otherwise lead to economic recessions.

Ivan has also given a special emphasis to Generative adversarial networks in his book. Although extremely useful, in recent times GANs have been used to generate high-dimensional fake data that look very convincing. Many researchers and developers have raised concerns about the negative repercussions of using GANs and wondered if it is even possible to prevent and counter its misuse/abuse.

Ivan acknowledges that GANs may have unintended outcomes but that shouldnt be the sole reason to discard them. He says, Besides great entertainment value, GANs have some very useful applications and could help us better understand the inner workings of neural networks. But as you mentioned, they can be used for nefarious purposes as well. Still, we shouldnt discard GANs (or any algorithm with similar purpose) because of this. If only because the bad actors wont discard them. I think the solution to this problem lies beyond the realm of deep learning. We should strive to educate the public on the possible adverse effects of these algorithms, but also to their benefits. In this way we can raise the awareness of machine learning and spark an honest debate about its role in our society.

Awareness and Ethics go in parallel. Ethics is one of the most important topics to emerge in machine learning and artificial intelligence over the last year. Ivan agrees that the ethics and algorithmic bias in machine learning are of extreme importance. He says, We can view the potential harmful effects of machine learning as either intentional and unintentional. For example, the bad actors I mentioned when we discussed GANs fall into the intentional category. We can limit their influence by striving to keep the cutting edge of ML research publicly available, thus denying them any unfair advantage of potentially better algorithms. Fortunately, this is largely the case now and hopefully will remain that way in the future.

I dont think algorithmic bias is necessarily intentional, he says. Instead, I believe that it is the result of the underlying injustices in our society, which creep into ML through either skewed training datasets or unconscious bias of the researchers. Although the bias might not be intentional, we still have a responsibility to put a conscious effort to eliminate it.

The field of ML exploded (in a good sense) a few years ago, says Ivan, thanks to a combination of algorithmic and computer hardware advances. Since then, the researches have introduced new smarter and more elegant deep learning algorithms. But history has shown that AI can generate such a great hype that even the impressive achievements of the last few years could fall short of the expectations of the general public.

So, in a broader sense, the challenge in front of ML is to sustain the current pace of innovation. In particular, current deep learning algorithms fall short in some key intelligence areas, where humans excel. For example, neural networks have a hard time learning multiple unrelated tasks. They also tend to perform better when working with unstructured data (like images), compared to structured data (like graphs).

Another issue is that neural networks sometimes struggle to remember long-distance dependencies in sequential data. Solving these problems might require new fundamental breakthroughs, and its hard to give an estimation of such one time events. But even at the current level, ML can fundamentally change our society (hopefully for the better). For instance, in the next 5 to 10 years, we can see the widespread introduction of fully autonomous vehicles, which have the potential to transform our lives.

This is just a snapshot of some of the important focus areas in the deep learning ecosystem. You can check out more of Ivans work in his book Advanced Deep Learning with Python. In this book you will investigate and train CNN models with GPU accelerated libraries like TensorFlow and PyTorch. You will also apply deep neural networks to state-of-the-art domains like computer vision problems, NLP, GANs, and more.

Ivan Vasilev started working on the first open source Java Deep Learning library with GPU support in 2013. The library was acquired by a German company, where he continued its development. He has also worked as a machine learning engineer and researcher in the area of medical image classification and segmentation with deep neural networks. Since 2017 he has focused on financial machine learning. He is working on a Python based platform, which provides the infrastructure to rapidly experiment with different ML algorithms for algorithmic trading. You can find him on Linkedin and GitHub.

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The challenge in Deep Learning is to sustain the current pace of innovation, explains Ivan Vasilev, machine learning engineer - Packt Hub