Voxxed Athens 2018 - IBM Watson Machine Learning – Build and train AI models on the Cloud
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The document details IBM Watson's machine learning capabilities, emphasizing the integration of AI in data handling and decision-making processes. It covers the tools and infrastructure necessary for effective AI model development, deployment, and management, highlighting roles such as data scientists and developers within AI teams. Additionally, it outlines the challenges organizations face in leveraging AI, including data accessibility, skill shortages, and security concerns.
Voxxed Athens 2018 - IBM Watson Machine Learning – Build and train AI models on the Cloud
- 1. IBM Watson Machine Learning Build and train AI models on the Cloud Haris Linardakis IBM Cloud Leader, Greece & Cyprus haris.linardakis@gr.ibm.com https://www.linkedin.com/in/harislinardakis/
- 2. Data, Data Everywhere! As the volume of data, digital transformation, and the pace of technological change accelerate, the ability of organizations and professionals to keep up and capitalize on the opportunity is becoming more challenging. Data proliferation Capacity and ability to adapt TIME RATE Knowledge and capability AI provides an opportunity to help professionals close the gap and harness the full potential of data by creating new tools to improve their work and outcomes.
- 3. AI enhances, scales, and accelerates human expertise Understand Understands data– structured and unstructured, text-based or sensory–in context and meaning, at astonishing speeds and volumes. Learn Ingests and accumulates data and insight from every interaction continuously. Is trained, not programmed, by experts who enhance, scale and accelerate their expertise. Reason Has the ability to form hypotheses, make considered arguments and prioritize recommendations to help humans make better decisions. Interact Responds and communicates with people in a natural way that allows cognitive solutions to see, talk and hear.
- 4. Tools & Infrastructure • Need an environment that enables a “fail fast” approach • Discrete tools present barriers to productivity Governance • If the data isn’t secure, self-service isn’t a reality • Challenge understanding data lineage and getting to a system of truth Skills • Data Science skills are in low supply and high demand • Nurturing new data professionals is challenging Data • Data resides in silos & difficult to access • Unstructured and external data wasn’t considered 4 Which are challenges to capture the value of AI?
- 5. Watson: AI for Smarter Business Watson Knowledge Catalog Watson Business Solutions Compliance Assist Customer Care Expert Assist Voice of the Customer Watson Enriched Data & Analytical Assets Watson Powered Search & Social Collaboration Active Policy Enforcement Model Governance, Traceability & Lineage Data Kits Learns from Small data Watson Machine Learning and Deep Learning as a Service Watson Studio Search & Find Relevant Data Connect & Access Data Prepare Data (Ingest, Curate, & Enrich) Build & Train AI Models Deploy AI Models Monitor, Analyze, Manage Continuous Learning Watson APIs Watson Assistant Watson Cybersecurity Compare & Comply Watson Applications ... ...... ISV & Third Party Applications
- 6. Watson APIs Integrate the world's most powerful AI into your application Tone Analyzer Identify tones at sentence and document level. Watson Assistant Build virtual agents that can integrate and communicate on any channel or device. Speech to Text & Text to Speech Create natural language interactions to better serve your end-users Visual Recognition Analyze images for scenes, objects, faces, and other content. Personality Insights Identify psychological traits Natural Language Understanding Analyze text to extract meta-data from content
- 7. Watson Studio Watson Studio accelerates the machine and deep learning workflows required to infuse AI into your application to drive innovation. It provides a suite of tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data and use that data to build, train and deploy models at scale. AI Requires Teamwork • AI is not magic • AI is algorithms + data + team
- 8. Her Job: Builds AI application that meet the requirements of the business. What she does: • Starts PoCs which includes gathering content, dialog building and model training • Focus is on app building for the team or company to use. Will handle ML Ops as needed Sometimes known as: Front-end, back-end, full stack, mobile or low-code developer Tanya Domain Expert Her Job: To transfer knowledge to Watson for a successful user experience. What she does: • Range of domain knowledge and uses that to teach Watson and develop a custom models • As Tanya gains more experience she optimizes her knowledge to teach Watson to design better end-user experiences. Sometimes known as: Subject matter expert, content strategist. His Job: Transform data into knowledge for solving business problems. What he does: •Runs experiments to build custom models that solve business problems. •Use techniques such as Machine Learning or Deep Learning and works with Tanya to validate success of trained models. Watson Studio Built for AI teams – enabling team productivity and collaboration Sometimes known as: ML/DL engineer, Modeler, Data Miner Ed Data Engineer His Job: Architects how data is organized and ensures operability What he does: • Builds data infrastructure and ETL pipelines. Works with Spark, Hadoop, and HDFS. • Works with data scientist to transform research models into production quality systems. Sometimes known as: Data infrastructure engineer Mike Data Scientist Deb The Developer
- 9. Watson Studio Supporting the end-to-end AI workflow Prepare Data for Analysis Build and Train ML/DL Models Deploy Models Monitor, Analyze and Manage Search and Find Relevant Data Connect & Access Data Connect and discover content from multiple data sources in the cloud or on premises. Bring structured and unstructured data to one toolkit. Clean and prepare your data with Data Refinery, a tool to create data preparation pipelines visually. Use popular open source libraries to prepare unstructured data. Democratize the creation of ML and DL models. Design your AI models programmatically or visually with the most popular open source and IBM ML/DL frameworks or leverage transfer learning on pre-trained models using Watson tools to adapt to your business domain. Train at scale on GPUs and distributed compute Deploy your models easily and have them scale automatically for online, batch or streaming use cases Monitor the performance of the models in production and trigger automatic retraining and redeployment of models. Build Enterprise Trust with Bias Detection, Mitigation Model Robustness and Testing Service Model Security. Find data (structured, unstructured) and AI assets (e.g., ML/DL models, notebooks, Watson Data Kits) in the Knowledge Catalog with intelligent search and giving the right access to the right users.
- 10. Watson Studio Tools for supporting the end-to-end AI workflow Model Lifecycle Management Machine Learning Runtimes Deep Learning Runtimes Authoring Tools Cloud Infrastructure as a Service • Most popular open source frameworks • IBM best-in-class frameworks • Create, collaborate, deploy, and monitor • Best of breed open source & IBM tools • Code (R, Python or Scala) and no-code/visual modeling tools • Fully managed service • Container-based resource management • Elastic pay as you go CPU/GPU power
- 11. Deploying Trained Models Download your trained models or deploy your models within Watson Machine Learning artifact repository train infrastructure deploy hosted models Appprediction App App App REST API REST API REST API REST API prediction prediction prediction Watson Machine Learning
- 12. 3 Watson Studio Open Source tools – Jupyter and RStudio Watson Visual Recognition – retrain Watson Elastic and customizable compute environments Create ML flows and design Neural Networks visually
- 13. 13 Watson Knowledge Catalog Unlock tribal knowledge and unleash your knowledge workers
- 14. 3 Data Refinery Making data fit for use Self-service data refinement and cleaning Comprehensive profiling Interactive visualization Scheduling and monitoring
- 15. 3 Watson Machine Learning Simplifying deployment & management of ML models in production apps Train neural networks in parallel across NVIDIA GPUs. Pay only for what you use. Auto- deallocation means no more remembering to shutdown your cloud training instances. Monitor batch training experiments then compare cross-model performance without worrying about log transfers and scripts to visualize results. You focus on designing your neural networks. We’ll manage and track your assets. Python client, command line interface (CLI) or UI? You choose the tooling that best fits your existing workflows. Training history and assets are tracked then automatically transferred to the user’s Object Storage for quick access. Deploy models into production then monitor them to evaluate performance. Capture new data for continuous learning and retrain models so they continually adapt to changing conditions.
- 16. Neural Network Modeler An intuitive drag-and-drop, no-code interface for designing neural network structures using the most popular deep learning frameworks. Quickly capture your network design then single click export for experimental optimization. Real-time validation of network flow Drag-and-drop network layers • Define layer configuration • Choose optimizer params • Save as popular framework code • Export as a python notebook • Execute as batch experiment • Generate CPU or GPU compatible code Supported Frameworks
- 18. 3 Dynamic Dashboards Making insights available to all
- 19. IBM Cloud https://www.ibm.com/cloud/ https://www.ibm.com/watson/developer-resources/ https://www.ibm.com/developerworks/ https://www.ibm.com/cloud/garage Lite (Free) No time limit No credit card required Free plans
- 20. IBM Global Entrepreneur Program https://developer.ibm.com/startups/
- 21. Deep learning is neural network design Machine Learning is algorithm selection AI is systems architecture
- 22. Watson Build 2018 Be a disrupter! Leverage your IP to create a next gen solution. Fast track your build with IBM Watson and Cloud services technology credits, exclusive access to IBM experts, and rich learning resources. https://www-01.ibm.com/events/wwe/watson/wbc2018.nsf/nomination.xsp?open
- 23. Get started today www.ibm.com/watson