A guide to IBM’s complete set of data & AI tools and services
So you can be a fearless wielder of AI
Before you scroll down through this page and think “There is no way I’m getting into all this, no ma’am,” just hold up a sec and let me tell you one, er… four, very quick things first:
As someone who was trained in Swiss grids, rubylith, and letterpress — and has had to make those archaic skills work through 20+ years of design jobs evolving from print, branding, websites, apps, platform strategies, software and most recently AI — I 100% promise if you, if you follow these tried-and-true rules, you’ll be fine no matter how much of the tech stuff you do or don’t absorb. Ready? Burn this into your brain:
No matter what new technology comes along…
- Designing for any medium will always be, first and foremost, about satisfying user and business needs.
- Starting with why and designing with the end in mind always leads to the right results.
- And finally, good design is still good design. Poop is still poop.
And the one tip I’ll add to this list specifically for AI…
- For every single step of every AI use case you come up with, ask yourself, “Couldn’t AI just do that for the user?” and “How does the user know if what the AI is doing/recommending is right?”
All this said, the more of the tech stuff you DO understand, the more confidently you’ll be able to push your concepts and your team to build better, cooler AI features. So here it is my design lovelies…
the shortest, sweetest summary of all of IBM’s data & AI tools and services and what you can use them for that you’ll find on the interwebs as of 3:27pm, Monday, October 12th, 2020!
What we’re about to cover:
The AI Ladder
Cloud Pak for Data
Watson Studio
— Watson Natural Language Classifier in Watson Studio
— Data Refinery in Watson Studio
— Cognos Dashboard Embedded Service in Watson Studio
Watson Assistant
Watson Discovery
Watson Natural Language Understanding
Watson OpenScale
Watson Knowledge Catalog
Watson AIOps
Recommended links
- IBM Data & AI’s YouTube channel
Overviews, tutorials, thought leader insights, impact stories, etc. - IBM Watson YouTube Channel
How Watson services work, tutorials, demos - IBM’s Data & AI Client Stories
Stories from IBM’s partners and clients using Data & AI’s services - For IBMers: Watson Master Class Box set
IBM Box folder containing Watson Master Class 1, which covers the content in this article +how to make using AI part of your daily design process, supporting IBM documents, and all links provided in this article.
— —
Note: The vast majority of this content is consolidated and simplified from other sources that I’ve pointed to in the Recommended Links sections. Can’t take any credit for real authorship here, just a clean-up for your speed learning enjoyment :)
IBM’s AI Strategy: The AI Ladder
Let’s start from the top with a quick pass on IBM’s AI strategy — the AI Ladder. It’s how IBM’s data and AI tools and services are organized by the steps it takes to build and manage AI, so it’s easier to understand what services to use when.
What it is
from The AI Ladder, by Rob Thomas
The AI Ladder is an information architecture designed for AI that allows businesses to automate and govern the data and AI lifecycle with a unified approach, so that they can ultimately operationalize AI with trust and transparency.
The AI Ladder has been developed by IBM to provide organizations with an understanding of where they are in their AI journey as well as a framework for helping them determine where they need to focus. It is a guiding principle for organizations to transform their business by providing four key areas to consider: how they collect data, organize data, analyze data, and then ultimately infuse AI into their organization.
Breaking an AI strategy down into pieces — or rungs of a ladder — serves as a guiding principle for organizations, regardless of where they are on their journey. It allows them to simplify and automate how they turn data into insights by unifying the collection, organization and analysis of data, regardless of where it lives. By using the ladder to AI as a guiding framework, enterprises can build the foundation for a governed, efficient, agile, and future-proof approach to AI.
- Step 1: Collect
Make data simple and accessible. Collect data of every type, regardless of where it resides, and bring flexibility in the face of ever-changing data sources. - Step 2:Organize
Create a business-ready analytics foundation. Organize all data into a trusted, business-ready foundation with built-in governance, protection, and compliance. - Step 3: Analyze
Build and scale AI with trust and transparency. Analyze data in smarter ways and benefit from AI models that empower organizations to gain new insights and make better, smarter decisions. - Step 4: Infuse
Operationalize AI throughout the business. Apply AI across the enterprise in multiple departments and within various processes — drawing on predictions, automation, and optimization.
Recommended links
• Awesome visualization of the AI Ladder story
• IBM’s Journey to AI Blog
• The AI Ladder, by Rob Thomas
• IBM Analytics: Data & AI’s products and client stories told in AI Ladder terms
• IBM Knowledge Center
• How Cloud Platforms Works
• How Watson Works
Cloud Pak for Data: Modernize, Collect, Organize, Analyze data and Infuse AI
Where the AI Ladder lives — Cloud Pak for Data holds all of the services that let companies collect, prep, build, connect, deploy, analyze and monitor their data and AI implementations.
What it is
An insight platform that combines data management with data science / AI development. Gives organizations the capabilities to take advantage of a broad set of data and AI services and integrate them into applications to accelerate time to value, time to insight, and time to market. The system is built on the Red Hat OpenShift Container Platform and designed to collect, organize and analyze data to infuse AI throughout your business. The system combines storage, compute, networking and software into plug-and-play nodes that let you expand your system’s footprint to meet your business needs. A single intuitive dashboard helps simplify software and hardware management. These insights let you use data and AI services to accelerate innovation. Deploy on IBM, Amazon, Google, or Microsoft.
What it does
- Containers and container management efficiencies. With Cloud Pak for Data, companies can improve their readiness for cloud migration, improve licensing flexibility with IBM, and reduce both hardware purchases and infrastructure management efforts.
- Data virtualization and governance benefits. Data virtualization “democratizes” data visibility across the organization, improves data governance and security, and can allow companies to avoid costly data migration projects.
- Data science, ML, and AI benefits. Data scientists are both more productive with Cloud Pak for Data and can deploy models to market faster. Additionally, due to Cloud Pak for Data’s integrated platform, companies avoided costs associated with legacy analytics tools or otherwise building a comparable solution internally.
- Platform benefits. There are potentially significant benefits related to different users within the organization being able to work in the same platform from a single vendor, from task automation capabilities (e.g., Augmented AI, Auto AI, and Auto Discovery) to improved documentation capabilities allowing easier transition of work between project teams or new project members. This allows for better collaboration across the company, in addition to simpler vendor management.
Example use cases…
• Merge data and AI services — from data preparation to building and deploying models to managing them at scale
• Provide governance of data and AI models including bias detection, drift detection and more
• Access data at scale across all enterprise data sources to explore self-service data capabilities in real time.
• Find and visualize insights that can fuel your business and create the right starting point for important AI and machine learning projects
• Provide a cloud-native system of insight for your enterprise’s most complex analytics in minutes rather than hours or days.
Recommended links
• Cloud Pak for Data
• Cloud Pak for Data documentation
• Cloud Pak for Data overview video
• Getting started with Cloud Pak for Data video library
• Cloud Paks explained video
• Forrester Report, Economic Impact of CloudPak for Data, Feb 2020
• Accelerate Your AI Journey, Feb 2020
• All available Cloud Pak for Data services and integrations
• Cloud Pak for Applications
• Cloud Pak for Automation
• Cloud Pak for Integration
• Cloud Pak for Multicloud Management
• Cloud Pak for Security
Watson Knowledge Catalog: Collect data
Watson Knowledge Catalog powers intelligent, self-service discovery of data, models and more. The cloud-based enterprise metadata repository activates information for artificial intelligence, machine learning and deep learning. Access, curate, categorize and share data, knowledge assets and their relationships, wherever they reside.
What it does
- Connects people to the data and knowledge that they need, including both relational data and unstructured data, such as PDF or Microsoft Office documents
- Collaborators have roles that control what activities they can perform in the catalog
- Assets in catalog consist of metadata about data, including how to access the data, the data format, the classification of the asset, which collaborators can access the data and other types of metadata that describe the data
- Advanced discovery Intelligent recommendations from IBM Watson and peers help you find relevant assets quickly
- End-to-end catalog Organize, define and manage enterprise data to provide the right context to drive value across imperatives like regulatory compliance and data monetization.
- Automated governance Active policy management and dynamic masking of sensitive data helps protect data to ensure compliance and audit-readiness, but most importantly, maintain client trust.
- Operationalized quality Track lineage and quality scores across structured data, unstructured data, AI models and notebooks.
- Self-service insights Consume and transform data at the speed of business with intuitive dashboards and flows that can be shared with peers or analytics tools.
- Flexible deployment Customize to use where your organization requires, whether on IBM Cloud Pak for Data, or as a service on IBM Cloud.
Services you can incorporate into Watson Knowledge Catalog
- Watson Studio
Prepare, analyze, and model data in a collaborative environment with tools for data scientists, developers, and domain experts.
Example use cases…
• Add data to a project
• Refine data
• Ingest streaming data
• Analyze data with notebooks or dashboards
• Analyze data with models or AI
Recommended links
• Watson Knowledge Catalog site
• Watson Knowledge Catalog documentation
• Getting Started with Watson Knowledge Catalog video library
Data Refinery in Watson Studio: Organize data
Available in IBM Watson Studio and Watson Knowledge Catalog, the data refinery tool saves data preparation time by quickly transforming large amounts of raw data into consumable, quality information that’s ready for analytics.
What it does
- Analyze and transform your data Interactively discover, cleanse, and transform your data with over 100 built-in operations. No coding skills are required.
- Profile and visualize data Understand the quality and distribution of your data using dozens of built-in charts, graphs, and statistics. Automatically detect data types and business classifications.
- Connect to data wherever it resides Access and explore data residing in a wide spectrum of data sources within your organization or the cloud.
- Governed self-service data preparation Automatically enforce policies set by data governance professionals.
- Schedule job execution Schedule data flow executions for repeatable outcomes. Monitor results and receive notifications.
- Serverless execution Easily scale out via Apache Spark to apply transformation recipes on full data sets. No management of Apache Spark clusters needed.
Services Data Refinery is available through
- Watson Studio
Prepare, analyze, and model data in a collaborative environment with tools for data scientists, developers, and domain experts. - Watson Knowledge Catalog
Intelligent data and analytic asset discovery cataloging and governance to fuel AI apps.
Example use cases…
• make raw, unstructured readable my machine learning models so insights and analytics can be derived from it with minimal prep time
Recommended links
• Data Refinery site
• Data Refinery documentation
Cognos Dashboard Embedded Service in Watson Studio: Analyze data
An API-based solution that lets developers easily add end-to-end data visualization capabilities to their applications.
What it does
- Utilizes JavaScript APIs to allow developers to quickly and efficiently embed visualizations into their application.
- Define the user workflow
- Provide fixed dashboards or let users build their own
Example use cases…
• Embed data visualizations directly into applications
Recommended links
• Cognos Dashboard Embedded site
• Cognos Dashboard Embedded documentation
• Embedding documentation
Watson Studio: Infuse AI
A collaborative environment with AI tools for data scientists, developers and domain experts to collect and prepare training data, and to design, train, and deploy machine learning models.
What it does
- Automates AI lifecycle management
- Governs and secures open source notebooks
- Visually prepare and build models
- Deploy and run models through one-click integration
- Manage and monitor models with explainable, trusted AI
Services inside of Watson Studio
FYI: Watson Studio has A LOT packed into it. I’ve included more about the tools/capabilities you should know about as their own sections rather than pack them all into this one. You’ll notice them titled “Something X for Watson Studio”
Watson Machine Learning
- AutoAI automatically analyzes your data and generates candidate model pipelines customized for your predictive modeling problem. AutoAI tutorial video >
- SPSS Modeler uses the SPSS flow editor so that you can quickly develop predictive models using business expertise and deploy them into business operations to improve decision making. SPSS Modeler demo video >
- Experiment builder automates running hundreds of training runs while tracking and storing results. Experiment builder demo video >
- Machine learning command line interface lets you build and work with models in your local environment. Watson Machine Learning demo video >
- Decision Optimization model builder guides you through building and solving prescriptive models. Decision Optimization demo video >
Notebooks
- Provide an interactive programming environment for working with data, testing models, and rapid prototyping.
Visual recognition
- Built-in models you can use to analyze images for scenes, objects, and many other categories without any training:
General model — Default classification from thousands of classes
Explicit model — Whether an image is inappropriate for general use
Food model — Specifically for images of food items
Text model — Text extraction from natural scene images - A model builder makes it quick and easy to train a model to classify images according to classes you define.
- Core ML support for using your visual recognition custom models in iOS apps.
- Watson Studio Visual Recognition site
Watson Studio Visual Recognition documentation
Watson Studio Visual Recognition tutorial
Natural language classification
- A model builder makes it quick and easy to train a model to classify text according to classes you define.
- API for classifying text in notebooks or in apps you develop.
Data Annotation
- Improve the quality of your training data using third parties to crowd source data labelers
- The DefinedCrowd and Figure Eight platforms allows users to create annotation jobs for sentiment analysis on CSV files, and annotate and label image files.
Example use cases…
• Analyze data and identify patterns and anomalies
• Predict future data trends and potential outcomes
• Explore a huge range of possible scenarios, then suggest the best way to respond to a present or future situation
• Optimize planning, scheduling, pricing, inventory, or resource management
• Recommend actions
• Label and recognize objects in images
• Identify words in labeled documents
Recommended links
• Watson Studio site
• Watson Studio documentation
• Getting started with Watson Studio video library
• Getting started with Watson Machine Learning video library
Watson Natural Language Classifier in Watson Studio: Infuse AI
At the core of natural language processing (NLP) lies text classification. Classifier helps your application understand the language of short texts and make predictions about how to handle them. A classifier uses machine learning to analyze text, and label and organize data into custom categories. Classifier learns from your example data so it can return predictions and confidence scores when presented with new text.
What it does
- Classify data your way Get started with natural language processing and machine learning in 15 minutes or less. Easily categorize text with custom labels to automate workflows, extract insights, and improve search and discovery.
- Achieve high accuracy with little data Generate higher accuracy on less training data through NLC’s ensemble of machine learning techniques. NLC models include multiple Support Vector Machines (SVMs) and a Convolutional Neural Network (CNNs), using IBM’s Deep Learning-as-a-Service (DLaaS).
- Multi-lingual support Classify text in multiple languages, including English, Arabic, French, German, Italian, Japanese, Korean, Portuguese (Brazilian), and Spanish.
- Tooling and API functionality Build, train, and manage classifiers, regardless of technical skills. Access NLC capabilities through the API or an easy-to-use interface in Watson Studio.
Prerequisite services
- Watson Studio
Prepare, analyze, and model data in a collaborative environment with tools for data scientists, developers, and domain experts.
Example use cases…
• flag inappropriate comments on social media
• understand sentiment in customer reviews
• determine whether email is sent to the inbox or filtered into the spam folder
• keep up with questions and complaints in a timely fashion
• enable guests and hotels to communicate seamlessly and leverage staff expertise as well as cognitive intelligence to deliver an unparalleled guest experience
• enable pharmaceutical companies and doctors to extract value from previously dark data, including several years’ worth of clinical studies, drug research and genomics data using natural language to ask the system about symptoms and find rare cases
Recommended links
• Watson NLC site
• Watson NLC documentation
Watson Assistant: Infuse AI
Build and deploy a branded assistant (chatbot) into any device, application, or channel. Connect Assistant to customer support resources to deliver unified problem-solving experience to customers.
How it works
Assistant analyzes the message from a customer, then routes the message to the appropriate skill:
- The dialog skill gathers any information it needs to respond or perform a transaction on the user’s behalf. The skill can connect with other IBM services or interact with your own back-end systems to extract information or perform transactions that are based on the user’s intent or other information.
- The search skill routes complex customer inquiries to IBM Watson Discovery. Discovery treats the user input as a search query. It finds information that is relevant to the query from the configured data sources and returns it so the assistant can share the information with the user as its response.
With the help of the skills that you build, assistant can answer simple or complex questions, and even perform tasks, such as opening tickets, updating account information, or placing orders.
Related services
- Watson Discovery
AI-powered search engine to extract answers from complex business documents. - Watson Language Translator
Identify the language of text and translate it into different languages programmatically. - Watson Knowledge Studio
Create a model that understands the linguistic nuances, meaning, and relationships specific to an industry. - Watson Speech to Text / Text to Speech
Convert audio and voice to text. Convert written text to natural-sounding speech.
Watson Speech to Text video demo >
Watson Text to Speech video demo >
Example use cases…
• Create conversational interfaces that allow users to navigate and interact with applications using natural language text or voice
• Provide conversational help and support in text or voice format throughout a product experience
• Send notifications and insights from an application to a user through messaging channels like Slack
• When Watson Discovery is added, Assistant can crawl existing web pages, support ticket, or files, and use them to answer customer questions. Assistant will automatically suggest text passages and articles when it sees a question it doesn’t have a pre-written answer for.
Recommended links
• Watson Assistant site
• Watson Assistant documentation
Watson Discovery: Infuse AI
An AI-powered search engine that extracts answers and insights to natural language user questions from complex, unstructured documents.
What it does
- Allows users to train a Watson to understand complex documents and answer users’ questions about them without having to write queries and responses like you would in Watson Assistant
- Intuitive tooling empowers your subject-matter experts to teach Watson the language of your industry without deep technical or coding skills required
- Automatically creates a custom dictionary from your content
- Sophisticated natural language processing capabilities enable users to analyze text and extract metadata from content
- Learns over time from business information like customer behavior and usage to improve relevant responses
- Leverages out-of-the-box natural language processing training to understand of contracts, invoices, and purchase orders
- Connect to a data source and Watson Discovery ingests structured and unstructured data, including private, licensed, or public data.
Tools inside Discovery
- Content mining A data mining capability that allows you to search across your documents and explore text analytics results, relationships, and how different elements of your content change over time.
- Document Conversion Easily ingest and convert all your documents from any data source with a simplified API, or use the out-of-the-box data set of the latest news articles on the web.
- Multi-Label-Classifier Assign several labels to a single document to provide more control over your enterprise search results. Filter your document collections by category, allowing you to narrow the scope of results.
- Natural Language Query (NLQ) Find relevant answers to complex questions through natural language and structured queries, and continuously improve your results with relevancy training.
- Smart Document Understanding (SDU) Visually label the text within your enterprise documents (from headers and footers, to tables, and so on). This allows Watson Discovery to fully understand the structure of your documents. As a result, Watson Discovery will provide you with more accurate information and precise passages. Smart Document Understanding demo video >
Example use cases…
• Evaluate massive volumes of complex structured and unstructured documents to surface answers and rich insights to natural language questions with minimal data labeling or training
• Analyze relationships and patterns buried in data to help users make more informed decisions
• Find answers from public data sets like news, social media, publications, etc.
Recommended links
• Watson Discovery site
• Watson Discovery documentation
• Video tutorial
Watson OpenScale: Infuse AI
OpenScale makes it easier for data scientists, application developers, IT and AI operations teams, and business-process owners to collaborate in building, running, and managing production AI. Users can monitor deployed AI models, provide explainability behind outcomes, make predictions, and maintain fairness and accuracy of the data and models. By providing full explainability and monitoring, the solution helps businesses ensure fair outcomes, remain compliant with regulations, and increase confidence in the value of AI.
What it does
- Builds trust by making insights and recommendations explainable
- Ensures the model and the data are fair and unbiased
- Measures performance of production AI and its impact on business goals
- Tracks actionable metrics and alerts in a single console
- Enables the business user or project manager to understand AI outcomes
- Applies business results to create a feedback loop that sustains AI outcomes
- Governs and explains AI to maintain regulatory compliance
- Automatically detects and mitigates harmful bias to improve outcomes
- Accelerates the integration of AI into existing business applications
Related services
- Db2
Work with a relational database that delivers advanced data management and analytics capabilities for transactional workloads. Db2 video demo > - Db2 Warehouse
Get in-memory processing and integrated database analytics with this high-performing analytics engine. Db2 Warehouse video demo > - Speech to Text
Convert audio and voice to text. Convert written text to natural-sounding speech. Watson Speech to Text video demo > - Watson Assistant
Build your own branded assistant into any device, application, or channel. Users interact with your application through the user interface that you implement. - Watson Machine Learning
Build, train, and deploy machine learning models with a full range of tools. - Watson Studio
Prepare, analyze, and model data in a collaborative environment with tools for data scientists, developers, and domain experts.
Example use cases…
• Monitor risk models for performance, bias and explainability to limit risk exposure from regulations and create more fair and explainable outcomes for customers
• More consistently and accurately assess claims risk, ensure fair outcomes for customers and explain AI recommendations for regulatory and business intelligence purposes
• Allow data scientists to build machine-learning models and work with their IT operations teams to confidently recommend proactive asset maintenance for communications service providers
Recommended links
• Watson OpenScale site
• Watson OpenScale documentation
Watson Natural Language Understanding: Infuse AI
A cloud native text analytics service that can be integrated into an existing data pipeline. Allows for the extraction of insights from unstructured data. Supports both public and private network endpoints.
What it does
Analyzes documents and webpages with text, HTML, or a public URL, for any specified features, including:
- Categories Recognize pre-trained and custom sets of words describing any topic or domain. See full list ≥
- Concepts Identify high-level concepts that aren’t necessarily directly referenced in the text based on recognized categories.
- Emotion Analyze emotion conveyed by specific target phrases, by the document as a whole, or by keywords.
- Entities Find people, places, events, and other types of entities mentioned in your content. See full list >
- Keywords Search your content for relevant keywords
- Metadata For HTML and URL input, get the author of the webpage, the page title, and the publication date.
- Relations Recognize when two entities are related, and identify the type of relation
- Semantic roles Parse sentences into subject-action-object form, and identify entities and keywords that are subjects or objects of an action.
- Sentiment Analyze the sentiment toward specific target phrases and the sentiment of the document as a whole. You can also get sentiment information for detected entities and keywords
- Syntax Identify the sentences and tokens in your text.
Supported languages
Natural Language Understanding supports a variety of languages depending on which features you analyze. Currently, English is the only language that is supported across all features. See full list >
HIPPA
US Health Insurance Portability and Accountability Act (HIPAA) support is available for Premium plans in the Washington, DC location. See details >
Services you can incorporate into NLU
- Watson Knowledge Studio
Extend Natural Language Understanding with custom models for supported feature and language combinations.
Example use cases…
• Perform advertising optimization, content recommendation, voice-of-customer analysis, audience segmentation, data mining, and more.
• Spot trends in customer feedback to address concerns, reduce churn, improve the customer experience and drive revenue.
• Ensure proper placement of advertisements based on page content, viewer patterns, and sentiment analysis of social media and other content.
• Identify key customer groups for market research and campaign personalization for different segments.
• Provide relevant insights from public and private texts to better inform decisions and foresee potential causes for change in data or system behaviors based news, events, policy changes, weather, etc.
Recommended links
• Watson NLU site
• Watson NLU documentation
Watson AIOps: Infuse AI
Watson AIOps provides a holistic view of your entire IT environment by pulling together data across your siloed IT stacks and tools so you can resolve complex IT issues, find hidden insights, diagnose application problems, and help you identify incident root causes faster — eliminating the need for multiple application performance monitoring (APM) dashboards. Insights and recommendations are fed directly into existing workflows so you can rapidly resolve IT incidents and achieve business outcomes such as improved revenue and reduced cost and risk.
What it does
- Diagnose problems faster Uncover hidden insights and determine root causes faster by correlating a vast amount of data (unstructured and structured) across silos and tools in real time
- Connect the dots across data Watson AIOps ties signals across structured and unstructured data from multiple sources to provide a clear view of anomalies, with linkages to sources for faster investigation and resolution.
- Find anomalies in real time By monitoring data in real time, Watson AIOps is able to provide rich insight and visibility as complex problems evolve to allow teams to more quickly diagnose and resolve mission-critical issues.
- Leverage your existing tools Watson AIOps uses pre-trained AI models tuned by data from your existing IT monitoring tools to give your teams valuable new insights specific to their environments.
- Get insights where you work Surface insights and next-best-action recommendations into your existing ChatOps workflows to enhance collaboration and speed decision making.
- Be confident in your decisions Empower your team to focus on higher value work with transparent and clearly explained AI decision making.
- Cut through the noise Avoid notification fatigue with intelligent alert grouping and find the source of the problem with topology insights.
- Integrate with your toolchain Augment your current environment with AI that easily integrates with best-in-class IT monitoring and ChatOps tools.
- Deploy where you want Get access to innovative AI capabilities on your cloud of choice or preferred deployment option.
Example use cases…
• The delivery of goods requires precision in timing and logistics. Watson AIOps helps keep these critical workloads running 24/7 to meet global supply chain needs and customer expectations.
• Financial transactions and trades require high-speed IT delivered across companies to customers. Watson AIOps helps address issues before they occur, or solve them in real time.
• Any break in online purchasing availability means unhappy customers and a loss of revenue. With Watson AIOps, monitor complex e-commerce platforms in real time to ensure they’re back up and running after outages as quickly as possible.
Recommended links
• Watson AIOps site
• Watson AIOps demo and use cases
• Watson AIOps overview + how it works
• Watson AIOps video advertisement
• Watson AIOps documentation
That’s a lot to take in — I know. I’ve been learning this stuff for nearly five years and I still have to work to keep it all straight. Pleaseeeee add questions in the comments re: anything you want to better understand!
Jennifer Sukis is a Design Director and Distinguished Designer for AI Transformation at IBM, based in Austin, TX. The above article is personal and does not necessarily represent IBM’s positions, strategies or opinions.
