IBM Watson Assistant API

With IBM API Connect, you can now develop better APIs more quickly with the help of a generative AI API Assistant tool.

Grounded on experimentation, generating artificial intelligence (gen AI) has emerged as a major player in numerous software applications in the last few years, disrupting industries and changing the way we tackle activities that were previously assumed to require human creativity and judgment. Gen AI has welcomed in a new era of efficiency, productivity, and creativity, spanning from digital art and music production to personalized customer care and software code creation.

Growing popularity of AI personal assistants

3 out of 4 top-performing CEOs concur that having the most sophisticated generation AI is necessary to get a competitive edge, according to a recent survey. AI assistants that assist clients and staff are a crucial component of how companies are operationalizing modern AI. Artificial intelligence (AI) assistants can enhance customer experience, IT operations, productivity, and application modernization by streamlining information access and automating tasks across enterprises. A different survey revealed that by 2025, 60% of executives predicted AI helpers would carry out the majority of traditional tasks. It is anticipated by nearly two-thirds (64%) that throughout the same time period, workers will communicate primarily with AI assistants for transactional tasks.

API administration is changing thanks to AI assistance

API management is now essential to guaranteeing that the numerous APIs inside a company’s ecosystem are regularly managed, secured, and governed due to the explosive rise in API usage in recent years. Developers may find it laborious and time-consuming to do manual tasks related to API maintenance, such as composing API documentation. Throughout the API lifespan, developers and users may accomplish API administration activities more quickly and easily by integrating Gen AI capabilities into API management through the use of AI assistants. This covers all aspects, such as developing and overseeing APIs as well as testing, developing, and integrating them.

Introducing the IBM API Connect’s API Assistant

IBM is announcing that IBM API Connect, its industry-leading and multi-award winning API management platform, now has the API Assistant feature globally available. Users of IBM API Connect will be able to construct better APIs more quickly with the aid of API Assistant, powered by Watsonx.ai.

Let’s examine the features that the IBM API Connect API Assistant now offer

Improve governance and increase API usage by updating your API documentation

API documentation requirements are nothing new. It is now imperative to provide comprehensive documentation for APIs, though, as AI and humans alike are consuming them.

For users human or artificial to quickly find, understand, and apply the different APIs available to them within their company, well-documented APIs are a must. To ensure consistency, compliance, and appropriate management of API usage throughout the organization, well-structured documentation is also essential to efficient API governance.

The problem lies in how time-consuming and laborious it is to write excellent API documentation. When it comes to producing comprehensive descriptions and examples for their API specifications, API developers would much rather solve complex coding and integration problems.

At this point, IBM API Connect‘s API Assistant becomes useful. The API Assistant scans the API definition in a matter of seconds, locates any gaps, and recommends context-specific examples and descriptions with a few clicks. By simply reviewing and implementing these suggestions, the developer may increase API discoverability, consistency, and adoption by both humans and other AI while also producing thorough documentation.

Smart error remediation can hasten API development and increase API dependability

All APIs need to be dependable, scalable, and quick to respond. Finding and correcting any mistakes and inconsistencies before deployment is a crucial step in the development process. To find and fix those mistakes, though, requires going through the API definition page by page. When an error occurs in the API definition, the IBM API Connect tool’s API Assistant may quickly identify and recommend ways to fix it.

An instance of a typical validation problem that the API Assistant can detect is when there are duplicate lines of code, missing parameters, or improper data types. Then, in order to swiftly correct the problems and reduce development time while enhancing code quality, you can examine and implement any or all of the recommended remedies.

With IBM API Connect Advanced tier (SaaS), the API Assistant is now accessible. Find out which plan best suits your needs by reading more about API Connect’s available options. To view the API Assistant in action, you may also register for a live IBM API Connect demo.

In a rapidly evolving digital landscape, enterprises are constantly seeking innovative solutions to harness the power of artificial intelligence (AI) and data to gain a competitive edge. Enter Pragma Edge's Watson X, a groundbreaking AI and Data Platform designed to empower enterprises with scalability and accelerate the impact of AI capabilities using trusted data. This comprehensive platform offers a holistic solution, encompassing data storage, hardware, and foundational models for AI and Machine Learning (ML). 

The All-in-One Solution for AI Advancement 

At the heart of Watson X is its commitment to providing an open ecosystem, allowing enterprises to design and fine-tune large language models (LLMs) to meet their operational and business requirements. This platform is not just about AI; it's about transforming your business through automation, streamlining workflows, enhancing security, and driving sustainability goals. 

Key Components of Watson X 

Watsonx.ai: The AI Builder's Playground 

Watsonx.ai is an enterprise development studio where AI builders can train, test, tune, and deploy both traditional machine learning and cutting-edge generative AI capabilities. 

It offers a diverse array of foundation models, training and tuning tools, and cost-effective infrastructure to facilitate the entire data and AI lifecycle. 

Watsonx.data: Fueling AI Initiatives 

Watsonx.data is a specialized data store built on the open lakehouse architecture, tailored for analytics and AI workloads. 

This agile and open data repository empowers enterprises to efficiently manage and access vast amounts of data, driving quick decision-making processes. 

Watsonx.governance: Building Responsible AI 

Watsonx.governance lays the foundation for an AI workforce that operates responsibly and transparently. 

It establishes guidelines for explainable AI, ensuring businesses can understand AI model decisions, fostering trust with clients and partners. 

Benefits of WatsonX 

Unified Data Access: Gain access to information data across both on-premises and cloud environments, streamlining data management. 

Enhanced Governance: Apply robust governance measures, reduce costs, and accelerate model deployment, ensuring high-quality outcomes. 

End-to-End AI Lifecycle: Accelerate the entire AI model lifecycle with comprehensive tools and runtimes for training, validation, tuning, and deployment—all in one location. 

In a world driven by data and AI, Pragma Edge's Watson X Platform empowers enterprises to harness the full potential of these technologies. Whether you're looking to streamline processes, enhance security, or unlock new business opportunities, Watson X is your partner in navigating the future of AI and data. Don't miss out on the transformative possibilities—explore Watson X today at watsonx.ai and embark on your journey towards AI excellence. 

Downer’s Data with IBM

When it comes to city transportation, accuracy is essential. As one of the top providers of integrated services in Australia and New Zealand, Downer sees itself as a protector of the complex transportation network and is always looking for ways to improve its efficiency. Downer has amassed a massive quantity of data with more than 200 trains and numerous sensors.

Even though Downer’s routinely extracts useful insights from its data, its collaboration with IBM Client Engineering sought to go deeper into the dataset’s possibilities, with a particular emphasis on tools and techniques that can simplify their analytical procedures.

Refining data insights with increased cooperation and knowledge

Downer is constantly striving to improve operational effectiveness while reducing maintenance expenses and disruptions to transportation plans. However, Downer’s envisioned a collaboration that would enable them to go through the data more thoroughly and find more insights that could improve operations. They joined forces with IBM specialists to explore the ever-expanding realm of data science. IBM Client Engineering ensured a valuable partnership by implementing Lean Startup, pair programming, and the IBM Value Engineering Method.

IBM cross-team involvement, enablement, and support with customer success managers, IT specialists, and IBM Consulting bolstered this team effort. With two sprints and a series of workshops, Downer and IBM set off on an innovative and exploratory journey. The companies worked together to investigate the prospect of increasing Downer’s capacity with the goal of detecting transportation abnormalities four times faster than they were four weeks earlier.

“This is a step towards potential transformation understanding what other tools and methods can drive deeper insights,” says Downer’s Lead Data Scientist, Yin Wong.

Low-code and no-code approaches for the investigation of time series data

With IBM’s introduction, the field of no-code and low-code options for constructing predictive models to yield quicker insights was opened up. With Watsonx.ai technology (deployed on IBM Cloud), Downer was able to reduce modeling times and increase anomaly detection efficiency.

Using advanced time series forecasting and anomaly prediction techniques along with IBM AutoAI and SPSS Modeler flows, Downer was able to quickly develop predictive models that can be integrated into business operations to improve decision-making processes.

In doing so, Downer’s surpassed their target period of one week to identify defective sensors, a substantial reduction in time. Presenting patterns to improve and refine data analysis while making it accessible to individuals without expert topic expertise was the major goal, rather than revolutionizing the method.

Downer’s was able to improve their data research procedures and experiment with various models thanks to the solutions, highlighting:

Versatility: Downer was able to guarantee continuous data access while integrating a variety of data sources.

Speed and scalability: Downer used tools like the Jupyter Notebook to effectively adapt models to a variety of data signals.

User-friendly exploration: Even for individuals without a strong background in data science, Downer found the model recommendations to be clear and beneficial in producing insights.

Employees at Downer’s express great satisfaction with the collaboration. Numerous analytics possibilities are available with these instruments. Yin Wong explains, “It’s about leveraging what best fits our team’s needs and skill set.

Additionally, Kes Gooding, RTS GM of Technology at Downer, acknowledged the possibility for additional operational improvements and hinted at a more thorough examination of the recommendations.

Together, navigating the data landscape

With this partnership, Downer and IBM Client Engineering demonstrated the possible results that can arise when two massive enterprise companies work together to achieve common objectives and improve data insights. They revisited the significance of ongoing exploration in the current digital era by delving into the vast lands of data-driven decision making over the course of four weeks.

Many firms use structured and unstructured synthetic data to tackle their largest data challenges thanks to breakthroughs in machine learning models and artificial intelligence including generative AI, generative adversarial networks, computer vision, and transformers. Qualitative data comprises text, images, and video, while quantitative synthetic data is tabular. Business leaders and data scientists across industries prioritize creative data synthesis to address data gaps, protect sensitive data, and speed to market. Finding and exploring synthetic data use cases like:

Edge cases and sample size increase with synthetic tabular data. When paired with real datasets, this data improves AI model training and prediction.

Synthetic test data speeds application and feature testing, optimization, and validation.

Synthetic data from agent-based simulations for “what-if” or new business events.

Protecting sensitive machine learning data with created data.

Sharing and selling a high-quality, privacy-protected synthetic copy to internal stakeholders or partners.

Synthesising data provides improved data value and guards against data privacy and anonymization strategies like masking. Business leaders lack trust. To build confidence and adoption, synthetic data creation tool manufacturers must answer two questions corporate leaders ask: Does synthetic data enhance my company’s data privacy risks? How well does synthetic data match mine?

Best practices help organizations answer these challenges and build trust in synthetic data to compete in today’s shifting marketplaces. Check it out.

Keeping bogus data private

Artificial data, which is computer-generated rather than real occurrences like customer transactions, internet logins, or patient diagnoses, can reveal PII when used as AI model training data. If a company prioritizes precision in synthetic data, the output may include too many personally identifiable traits, accidentally increasing privacy risk. Companies and vendors must work hard to minimize inadvertent linkages that could reveal a person’s identity and expose them to third-party assaults as data science modeling tools like deep learning and predictive and generative models evolve.

Companies interested in synthetic data reduce privacy risk:

Data should stay

Many companies are moving their software programs to the cloud for cost savings, performance, and scalability, but privacy and security require on-premises deployments. This includes fake data. Low-risk public cloud deployment of synthetic data without private or PII or model training data. When synthetic data requires sensitive data, organizations should implement on-premises. Your privacy team may prohibit sending and storing sensitive PII client data in third-party cloud providers, notwithstanding their outstanding security and privacy measures.

Be in charge and protected

Some synthetic data uses require privacy. Executives in security, compliance, and risk should govern their intended privacy risk during synthetic data generation. “Differential privacy” enables data scientists and risk teams choose their privacy level (1–10, with 1 being the most private). This method hides each person’s participation, making it impossible to tell if their information was used.

It automatically finds sensitive data and hides it with “noise”. The “cost” of differential privacy is reduced output accuracy, although introducing noise does not reduce usefulness or data quality compared to data masking. So, a differentially private synthetic dataset resembles your real dataset statistically. Data transparency, effective data security against privacy threats, and verifiable privacy guarantees regarding cumulative risk from subsequent data releases are also provided by differential privacy strategies.

Understand privacy metrics

If differentiated privacy isn’t achievable, business users should monitor privacy metrics to determine their privacy exposure. Although incomplete, these two metrics give a good foundation:

Leakage score: Percentage of false dataset rows that match original. A synthetic dataset may be accurate, but too much original data may compromise privacy. When original data contains target information but is inaccessible to the AI model for prediction or analysis, data leaks.

Distance between original and generated data generates closeness score. Short distances make synthetic tabular data rows easier to extract, increasing privacy risk.

Synthetic data quality assessment

Data scientists and business leaders must trust synthetic data output to use it enterprise-wide. In particular, they must immediately assess how well synthetic data matches their data model’s statistical properties. Lower-fidelity synthetic data is needed for realistic commercial demos, internal training assets, and AI model training situations than healthcare patient data. A healthcare company may use synthetic output to identify new patient insights that inform downstream decision-making, thus business leaders must ensure that the data matches their business realities.

Considering fidelity and other quality metrics:

Fidelity

A critical metric is “fidelity”. It assesses synthetic data based on its data model and real data similarity. Companies should know column distributions and univariate and multivariate column linkages. Understanding complex and huge data tables is crucial (most are). The latest neural networks and generative AI models capture these intricate relationships in database tables and time-series data. Bar graphs and correlation tables show lengthy but informative fidelity measurements. Open-source Python modules like SD metrics can help you learn fidelity analytics.

Utility

AI model training requires real datasets, which takes time. Machine learning model training is faster with synthetic data. Understanding synthetic data’s application in AI model training is essential before sharing it with the proper teams. The expected accuracy of a machine learning model trained on real data is compared to synthetic data.

Fairness

Enterprise datasets may be biased, making “fairness” important. A biased dataset will skew synthetic data. Understanding prejudice’s scope helps businesses address it. Identification of bias can help businesses make informed judgments, but it’s less common in synthetic data solutions and less significant than privacy, fidelity, and utility.

Watsonx.ai synthetic data usage

IBM watsonx.ai allows AI builders and data scientists input data from a database, upload a file, or construct a custom data schema to create synthetic tabular data. This statistics-based method creates edge situations and larger sample sets to improve AI training model forecasts. With this data, client demos and employee training may be more realistic.

Foundation models power Watsonx.ai, an enterprise-ready machine learning and generative AI studio. Watsonx.ai lets data scientists, application developers, and business analysts train, validate, adapt, and deploy classical and generative AI. Watsonx.ai aids hybrid cloud AI application development collaboration and scaling.

In a rapidly evolving digital landscape, enterprises are constantly seeking innovative solutions to harness the power of artificial intelligence (AI) and data to gain a competitive edge. Enter Pragma Edge's Watson X, a groundbreaking AI and Data Platform designed to empower enterprises with scalability and accelerate the impact of AI capabilities using trusted data. This comprehensive platform offers a holistic solution, encompassing data storage, hardware, and foundational models for AI and Machine Learning (ML). 

The All-in-One Solution for AI Advancement 

At the heart of Watson X is its commitment to providing an open ecosystem, allowing enterprises to design and fine-tune large language models (LLMs) to meet their operational and business requirements. This platform is not just about AI; it's about transforming your business through automation, streamlining workflows, enhancing security, and driving sustainability goals. 

Key Components of Watson X 

Watsonx.ai: The AI Builder's Playground 

Watsonx.ai is an enterprise development studio where AI builders can train, test, tune, and deploy both traditional machine learning and cutting-edge generative AI capabilities. 

It offers a diverse array of foundation models, training and tuning tools, and cost-effective infrastructure to facilitate the entire data and AI lifecycle. 

Watsonx.data: Fueling AI Initiatives 

Watsonx.data is a specialized data store built on the open lakehouse architecture, tailored for analytics and AI workloads. 

This agile and open data repository empowers enterprises to efficiently manage and access vast amounts of data, driving quick decision-making processes. 

Watsonx.governance: Building Responsible AI 

Watsonx.governance lays the foundation for an AI workforce that operates responsibly and transparently. 

It establishes guidelines for explainable AI, ensuring businesses can understand AI model decisions, fostering trust with clients and partners. 

Benefits of WatsonX 

Unified Data Access: Gain access to information data across both on-premises and cloud environments, streamlining data management. 

Enhanced Governance: Apply robust governance measures, reduce costs, and accelerate model deployment, ensuring high-quality outcomes. 

End-to-End AI Lifecycle: Accelerate the entire AI model lifecycle with comprehensive tools and runtimes for training, validation, tuning, and deployment—all in one location. 

In a world driven by data and AI, Pragma Edge's Watson X Platform empowers enterprises to harness the full potential of these technologies. Whether you're looking to streamline processes, enhance security, or unlock new business opportunities, Watson X is your partner in navigating the future of AI and data. Don't miss out on the transformative possibilities—explore Watson X today at watsonx.ai and embark on your journey towards AI excellence.