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ibm-ai.png IBM for AI WatsonX

Overview

IBM ID, IBM Cloud, Watson Studio

Setup:

  1. IBMID
  2. Download

IBM Skills Network Labs

Hands-on labs use the IBM Skills Network Labs (SN Labs)</a> hosting virtual lab environments. Upon clicking “Launch App” your Username and Email is passed to Skills Network Labs.

IBM Watsonx

Watsonx is an AI and data platform with a set of AI assistants designed to help you scale and accelerate the impact of AI with trusted data across your business.

The core components include a studio for new foundation models, generative AI, and machine learning; a fit-for-purpose data store built on an open data lakehouse architecture; and a toolkit, to accelerate AI workflows that are built with responsibility, transparency, and explainability.

The Watsonx AI assistants empower individuals in your organization to do work without expert knowledge across a variety of business processes and applications, including automating customer service, generating code, and automating key workflows in departments such as HR.

IBM Watson® Speech Libraries for Embed is a set of containerized text-to-speech and speech-to-text libraries designed to offer our IBM partners greater flexibility to infuse the best of IBM Research® technology into their solutions. These technologies allow the assistant to communicate with users through voice input and output.

IBM AI Product Manager Professional Certificate consists of 10 courses which Coursera estimates to take 3 months. Created by SkillUp EdTech.

  1. 13 hr Product Management: An Introduction
  2. 15 hr Product Management: Foundations & Stakeholder Collaboration
  3. 18 hr Product Management: Initial Product Strategy and Plan
  4. 18 hr Product Management: Developing and Delivering a New Product

IBM Generative AI for Cybersecurity Professionals Specialization

Same as others:

IBM AI Developer Professional Certificate consists of 10 courses which Coursera estimates to take 6 months.

  1. 14 hr Introduction to Software Engineering
  2. 13 hr Introduction to Artificial Intelligence (AI)
  3. 06 hr Generative AI: Introduction and Applications

  4. 07 hr Generative AI: Prompt Engineering Basics

  5. 10 hr Introduction to HTML, CSS, & JavaScript

  6. 25 hr Python for Data Science, AI & Development - by Joseph Santarcangelo
    1. 2 hr Python Basics (Jupyter, Types, Expressions, Variables, String Operations)
    2. 3 hr Python Data Structures (Lists, Tuples, Dictionaries, Sets)
    3. 6 hr Python Programming Fundamentals (Conditions, Branching, Loops, Exception Handling, Objects and Classes)
    4. 5 hr Working with Data in Python (Files, Pandas, 1D & 2D Numpy)
    5. 6 hr APIs and Data Collection (REST APIs & HTTP Requests, Web Scraping)

  7. 13 hr Developing AI Applications with Python and Flask

  8. 13 hr Building Generative AI-Powered Applications with Python - by Sina Nazeri
    1. 2 hr Image Captioning with Generative AI
    2. 2 hr Create Your Own ChatGPT-Like Website
    3. 1 hr Create a Voice Assistant
    4. 1 hr Generative AI-Powered Meeting Assistant
    5. 1 hr Summarize Your Private Data with Generative AI and RAG
    6. 2 hr Babel Fish (Universal Language Translator) with LLM and STT TTS (Speech To Text, Text To Speech)
    7. 1 hr Build an AI Career Coach

  9. 17 hr Generative AI: Elevate your Software Development Career

  10. 11 hr Software Developer Career Guide and Interview Preparation

Understanding Watsonx.ai

Building Voice Assistant with GPT-3 and IBM Watson

IBM AI Engineering Professional Certificate

  1. 13 hr Machine Learning with Python
  2. 08 hr Introduction to Deep Learning & Neural Networks with Keras
  3. 07 hr Building Deep Learning Models with TensorFlow
  4. 17 hr Introduction to Neural Networks and PyTorch
  5. 16 hr Deep Learning with PyTorch

  6. 16 hr AI Capstone Project with Deep Learning

  7. 05 hr Generative AI and LLMs: Architecture and Data Preparation
  8. 07 hr Gen AI Foundational Models for NLP & Language Understanding
  9. 08 hr Generative AI Language Modeling with Transformers
  10. 8 hr Generative AI Engineering and Fine-Tuning Transformers
  11. 8 hr Generative AI Advance Fine-Tuning for LLMs
  12. 6 hr Fundamentals of AI Agents Using RAG and LangChain (PyTorch, HuggingFace)
  13. 9 hr Project: Generative AI Applications with RAG and LangChain. Load docs from pdf, url, txt. Apply text-splitting for model responsiveness. Embed documents using watsonx’s embedding model. Setup a Gradio interface for model interaction and construct a QA bot to answer questions

IBM AI Enterprise Workflow Specialization by Mark J Grover and Ray Lopez, Ph.D.

  1. 07 hr AI Workflow: Business Priorities and Data Ingestion
  2. 10 hr AI Workflow: Data Analysis and Hypothesis Testing
  3. 12 hr AI Workflow: Feature Engineering and Bias Detection
  4. 13 hr AI Workflow: Machine Learning, Visual Recognition and NLP
  5. 09 hr AI Workflow: Enterprise Model Deployment
  6. 17 hr AI Workflow: AI in Production
    1. 04 hr Feedback loops and Monitoring
    2. 03 hr Hands on with Openscale and Kubernetes
    3. 03 hr Capstone: Pulling it all together (Part 1)
    4. 05 hr Capstone: Pulling it all together (Part 2)

IBM Applied DevOps Engineering Professional Certificate by Upkar Lidder, John Rofrano, Alex Parker, Ramanujam Srinivasan

  1. 09 hr Introduction to DevOps
  2. 11 hr Introduction to Agile Development and Scrum
  3. 17 hr Introduction to Containers w/ Docker, Kubernetes & OpenShift
  4. 14 hr Application Development using Microservices and Serverless
  5. 19 hr Introduction to Test and Behavior Driven Development
  6. 12 hr Continuous Integration and Continuous Delivery (CI/CD)
  7. 17 hr Application Security for Developers and DevOps Professionals
  8. 16 hr Monitoring and Observability for Development and DevOps
  9. 18 hr DevOps Capstone Project

IBM Skills Network Team Instructors are:

OpenScale

OpenScale aims to foster trust and transparency in AI systems, making it easier for businesses to adopt and scale AI technologies across their operations.

OpenScale is designed to work with AI models regardless of where they are deployed - on-premises, in the cloud, or in hybrid environments.

It can be integrated with various AI platforms and tools, including Amazon SageMaker, demonstrating its flexibility and openness.