AI & Machine Learning
Tuesday, April 27, 2021
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 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. We add microservices in Kafka Streams.
Apache Flink, Apache Kafka, Apache NiFi, MiNiFi, DJL.ai Apache MXNet, Apache Kudu, Apache Impala, Apache HDFS
Source Code: https://github.com/tspannhw/MmFLaNK
Getting Machine Learning to work let alone turn it into a sustainable business is a real pain in the ass. It sucks really... But you can do amazing stuff with it!
As a Machine Learning consultant I trained a lot of people to build their own Machine Learning algorithms and turn them into a customer benefit. And although it is not that hard in and of itself, it is really easy to make mistakes, even for the best of us. In this talk I will highlight some of the most common mistakes and how to avoid them. But if you think you will be able to stop making mistakes if you do everything right, you are wrong! Because Machine Learning sucks!
Where is your data? What data is available? What are its size and quality? Who can give you access to the necessary tables for the amazing project with your sales department (or customer service, or recruitment, whatever)? Any idea about how long your AI project is going to take? How much money it will generate? How will it impact important KPIs (revenue, CSAT, time to hire)?
These questions might sound overwhelming and "not in my job description!" for a data scientist. Well, fair enough, and good luck! Without answering them, you'll never manage a successful AI project, meaning not the one where you've built a fancy model, but the one that actually matters for your organization.
A necessary prerequisite for that is an understanding of your company's Data Maturity. Simply put, it's the company's ability to generate value with data. Four dimensions - Strategy, People, Technology, and Data, and several basic scenarios to start with.
In this session, I'll briefly present a summary of our Data Maturity Assessment and how it can help you build AI solutions that matter.
Leverage AWS AI/ML/DL services in you Application :
Are you looking for ways to add new AI/ML/DL technologies to your existing applications but don't know where to start?
In this session, learn how to leverage AWS machine-learning services for your .NET applications to do things like text translation, text to speech, transcription, sentiment analysis, and image analysis. Learn AWS Support for .NET Workloads and also understand .NET Application modernization on AWS.
Based on Gartner's research, 85% of AI projects fail. In this talk, we show the most typical mistakes made by the managers, developers, and data scientists that might make the product fail. We base on ten case studies of products that failed and explain the reasons for each fail. On the other hand, we show how to avoid such mistakes by introducing a few lifecycle changes that make an AI product more probable to success.
Wednesday, April 28, 2021
In 2020, OpenAI has launched GPT-3, an autoregressive language model that uses deep learning to produce human-like text which was trained on 175 Billion parameters.
In 2021, Google AI has open sourced Switch Transformer, an artificial intelligence language model which was trained on 1.6 Trillion parameters.
How do these developments affect the tech industry?
It’s a safe bet that the state of tech as we know it will change to be AI-driven. This is already affecting a wide range of fields, from meditation apps, through investment robots to the cloud infrastructure. This won’t stop there, as new fields that are being disrupted by tech, like legal-tech, med-tech and others, also have an AI component.
Keeping up with these changes requires changes across all the tech teams - product, software, DevOps, QA, etc. In this talk we will cover the current state of AI and how you can make your product and teams future-compatible.