Applied Machine Learning
Tuesday, October 27, 2020
Today, data is being generated from devices and containers living at the edge of networks, clouds and data centers. We need to run business logic, analytics and deep learning at the edge before we start our real-time streaming flows. Fortunately using the all Apache Mm FLaNK stack we can do this with ease! Streaming AI Powered Analytics From the Edge to the Data Center is now a simple use case. With MiNiFi we can ingest the data, do data checks, cleansing, run machine learning and deep learning models and route our data in real-time to Apache NiFi and/or Apache Kafka for further transformations and processing. Apache Flink will provide our advanced streaming capabilities fed real-time via Apache Kafka topics. Apache MXNet models will run both at the edge and in our data centers via Apache NiFi and MiNiFi. Our final data will be stored in Apache Kudu via Apache NiFi for final SQL analytics.
Wednesday, October 28, 2020
Nowadays “Artificial Intelligence” is everywhere! And rightly so, it does enable us to do really cool things, things we couldn’t even imagine doing just a decade ago. In fact, it sometimes just feels like magic. This ‘magic’ behind it is often powered by “Machine Learning”. But even “AI” has its limitations.
I’ll show examples where “AI” and ML have failed (sometimes with horrible consequences) and will explain why failures are unavoidable in ML but also mention what we can do to reduce them in the future.
Furthermore, I’ll showcase how current AI implementations discriminate against minorities and how that in some cases even leads to a higher risk of death for those groups.
I’ll cover the bias that humans introduce and I’ll explain how poor choice of data makes our world even more unjust than it already is.
The takeaway for the audience: AI can fail and sometimes it has horrible consequences. Why is AI so hard to “do right”? How can we make AI better?
Understandability is the most important concept in software, that most companies today aren’t tracking. Systems should be built and presented in ways that make it easy for engineers to comprehend them; the more understandable a system is, the easier it will be for engineers to change it in a predictable and safe manner. But with the rise of complex systems, it’s become all too common that we don’t understand our own code once we deploy it.
To deal with system complexity, developers are spending too much time firefighting and fixing bugs. In recent surveys, most devs say they spend at least a day per week troubleshooting issues with their code (sometimes, it can be a couple of days up to a full week trying to fix an elusive bug). This is hurting developer productivity and business results. It also creates a tough choice between flying slow or flying blind; as developers, we are too often making decisions without data in order to maintain velocity.
In this talk, I’ll highlight the importance of Understandability and how it has a huge impact on our day-to-day work. I’ll also discuss how it relates to popular concepts such as complexity, observability, and readability. Finally, I’ll share some tools and techniques to manage and optimize Understandability.
Applying AI to healthcare is a great opportunity — better predictions on who is more likely to develop diabetes, back pain, and other chronic diseases, better predictions on which patients will require hospital re-admissions — not only in saving money but also improving patient health. In this talk, we will discuss our technology solution and our challenges in building AI/ML solutions in this domain:
* We built a data ingestion and extraction process using Apache Beam and Google Cloud DataFlow. We will describe our obstacles around joining and normalizing disparate patient datasets and our heuristics to solve this problem. We will also talk about performance and scalability obstacles and our solutions.
* We built model training and serving pipelines using Kubeflow (TensorFlow on Kubernetes and Istio). We will talk about how we built a HIPAA/SOC2 compliant infrastructure with these technologies and our experience using Katib for model tuning.
As COVID-19 disrupts the retail industry worldwide, retailers like Walmart and Amazon are investing heavily in AI and in automating retail processes.
Daisy Intelligence Founder & CEO, Gary Saarenvirta, will discuss how AI, automation and shock proofing merchant process is key to not only survive, but thrive in a post-pandemic world.
Serving machine learning models is a scalability challenge at many companies. Most of the applications require a small number of machine learning models (often <100) to serve predictions. On the other hand, cloud platforms that support model serving, though they support hundreds of thousands of models, provision separate hardware for different customers. Salesforce has a unique challenge that only very few companies deal with, Salesforce needs to run hundreds of thousands of models sharing the underlying infrastructure for multiple tenants for cost effectiveness. In this talk we will explain how Salesforce hosts hundreds of thousands of models on a multi-tenant infrastructure, to support low-latency predictions.
Thursday, October 29, 2020
Breakthroughs in artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) have helped customers and call agents alike, to get more done in less time. It draws on multiple data sources to anticipate customer and company needs, handles interactions on its own where possible, and provides in-call support where needed.
The future of AI in the contact center is one where software tools make humans more efficient and allow the customers to have natural conversations with a bot via voice, webchat, social messaging app or other channels, handling requests, retrieving information and delivering answers to frequently asked questions. In short, creating the ultimate customer experience.
During this session, Tony Hung, senior software engineer at Vonage will discuss how enterprises with limited machine learning expertise can leverage communications APIs to unlock simple, secure and flexible solutions to deploy AI in their contact centers, elevating issues to experienced agents when needed to ensure personalized, emotive CX. He will draw on his experience to explain how enterprises can automate their agent-based live chats and streamline their support channels and operations, while offering a personalized human-like interaction. Most importantly, he will discuss how to find the right balance between seamless, intelligent self-service and efficient human intervention using integrated AI-driven communications - applications, APIs and the best of both.
OPEN TALK (AI): Abusing Your CI/CD: Running Abstract Machine Learning Frameworks Inside Github ActionsJoin on Hopin
We all love the conventional uses of CI/CD platforms, from automating unit tests to multi-cloud service deployment. But most CI/CD tools are abstract code execution engines, meaning that we can also leverage them to do non-deployment-related tasks. In this session, we'll explore how GitHub Actions can be used to train a machine learning model, then run predictions in response to file commits, enabling an untrained end-user to predict the value of their home by simply editing a text file. As a bonus, we'll leverage Apple's CoreML framework, which normally only runs in an OSX or iOS environment, without ever requiring the developer to lay their hands on an Apple device.
Determining the best and most suitable Machine Learning model for a given
data science problem isn't an easy task and it can be rather challenging at times.
It is like benchmarking sports cars created by different racing teams!
This presentation will show an easily extensible framework
that implements several Machine Learning models for supervised,
unsupervised and semi supervised learning to execute and/or compare models. Additionally, the talk will introduce the open source python scikit learn toolkit through several Machine Learning Models and the open source python Hydra package from Facebook and how they have been used in the framework.
The framework is extensible, generic, portable and easy to use.
In this talk, we will discuss Machine Learning practices in Software Testing stages in detail with a case study. This is an important study since nowadays, researches are looking for adaptation of Machine Learning algorithms to testing processes to reduce the manual effort and improve quality.
We start with a quick view of the machine learning types. Then, we list AI applications in testing these perspectives: test definition, implementation, execution, maintenance and grouping, and bug handling. What’s more, we do not only present existing AI applications but also what can be done in the future. Finally, we summarize the application areas with algorithms and discuss the advantages and potential risks of AI applications in software testing.