IBM Watson Studio is a comprehensive data science and AI development platform that enables users to build, train, and deploy machine learning models and AI applications. It offers a suite of tools for data preparation, model development, and collaboration, making it ideal for data scientists, analysts, and developers. Watson Studio supports a wide range of machine learning and deep learning algorithms, and it integrates with IBM Cloud services for scalable computing. Use cases include data cleaning and transformation, building and training models for tasks like classification and regression, developing AI-powered applications such as chatbots, automating machine learning with AutoAI, and deploying models for real-time predictions. Its collaborative features make it well-suited for team-based projects across industries like healthcare, finance, and retail.

What is IBM Watson Studio?

IBM Watson Studio is an integrated development environment designed to facilitate data science, machine learning, and AI model development. It offers a collaborative platform for data scientists, analysts, and business professionals to work together on data preparation, model building, and deployment. IBM Watson Studio integrates various tools and technologies, including open-source frameworks, Jupyter Notebooks, SPSS Modeler, and a range of Watson APIs, making it a comprehensive solution for the AI lifecycle.

As a cloud-based service, IBM Watson Studio streamlines the process of exploring data, training machine learning models, and deploying them into production environments. It provides an environment where users can easily scale their AI workflows, access powerful computational resources, and integrate with other IBM Cloud services.

Top 10 Use Cases of IBM Watson Studio

1. Predictive Maintenance
   • Analyze sensor data to predict equipment failures and schedule maintenance before downtime occurs.
2. Fraud Detection
   • Leverage machine learning models to identify patterns of fraudulent activity in financial transactions or insurance claims.
3. Customer Segmentation
   • Use clustering and classification techniques to group customers based on their behavior and preferences.
4. Supply Chain Optimization
   • Optimize inventory levels, forecast demand, and improve logistics by analyzing historical and real-time data.
5. Healthcare Insights
   • Build models to predict patient outcomes, identify at-risk individuals, and improve treatment recommendations.
6. Natural Language Processing (NLP)
   • Create applications that extract insights from unstructured text data, such as customer feedback or legal documents.
7. Churn Prediction
   • Identify customers at risk of leaving and implement targeted retention strategies.
8. Image Recognition and Analysis
   • Train deep learning models to classify images, detect objects, and analyze visual data for various industries.
9. Energy Consumption Forecasting
   • Analyze historical energy usage data to predict future consumption and optimize energy distribution.
10. Marketing Campaign Optimization
    • Leverage data to segment audiences, predict campaign performance, and allocate resources more effectively.

Features of IBM Watson Studio

• Collaboration Tools: Enables teams to work together on datasets, models, and notebooks in a unified environment.
• Flexible Deployment: Supports multiple deployment options, including cloud, on-premises, and hybrid setups.
• Integration with Watson APIs: Connects easily to Watson services for NLP, speech-to-text, image recognition, and more.
• Open-Source Compatibility: Integrates with popular open-source frameworks and libraries like TensorFlow, PyTorch, and scikit-learn.
• AutoAI: Automates key steps of the AI workflow, from data preparation to model selection and hyperparameter tuning.
• Data Preparation and Refinement: Offers tools for cleaning, transforming, and enriching datasets.
• Scalable Infrastructure: Provides access to IBM’s powerful cloud resources for large-scale training and deployment.

How IBM Watson Studio Works and Architecture

IBM Watson Studio is designed as a modular platform, allowing users to select the components that best fit their workflow. Its core architecture includes:

1. Data Access and Preparation: Connect to various data sources, including databases, cloud storage, and on-premises systems. Use built-in tools to clean, normalize, and transform data.
2. Development Environments: Work with Jupyter Notebooks, RStudio, SPSS Modeler, or the AutoAI graphical interface.
3. Machine Learning and Deep Learning: Build, train, and evaluate models using integrated machine learning libraries and frameworks.
4. Model Management and Deployment: Store models in a centralized repository, track version history, and deploy models as APIs or batch jobs.
5. Integration with IBM Cloud Services: Leverage additional Watson services, data storage solutions, and security features to enhance workflows.

By combining these components, IBM Watson Studio supports the entire AI lifecycle, from data exploration to production deployment.

How to Install IBM Watson Studio

IBM Watson Studio is a cloud-based platform, and it does not require installation in the traditional sense. However, if you’re looking to use it programmatically (e.g., through APIs, SDKs, or from a Python environment), you can interact with Watson Studio using the IBM Cloud SDK or directly through APIs.

1. Create an IBM Cloud Account

Before proceeding, ensure you have an IBM Cloud account. You can create one for free on the IBM Cloud website.

2. Install IBM Cloud CLI

To manage your IBM Cloud services from the command line, install the IBM Cloud CLI (Command Line Interface):

• Go to IBM Cloud CLI Installation and follow the instructions for your operating system.
• Once installed, open your terminal or command prompt and log in to IBM Cloud using: ibmcloud login

3. Install IBM Watson SDK for Python (Optional)

If you want to interact with IBM Watson services programmatically in Python, you can install the Watson SDK for Python. For example, to interact with Watson Studio, you will likely need the ibm-watson Python package for accessing various Watson services.

To install the IBM Watson SDK, use pip:

pip install ibm-watson

You may also want the ibm-cloud-sdk-core package for authentication and more advanced SDK features:

pip install ibm-cloud-sdk-core

4. Interact with IBM Watson Studio via APIs (Using Python SDK)

You can now interact with Watson Studio using the IBM Watson APIs. Below is an example code to interact with Watson Studio services programmatically.

First, set up your credentials (such as your API key and service URL). Then, use the Watson SDK to interact with Watson Studio.

Example (Python code):

from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
from ibm_watson import VisualRecognitionV3

# Set up your IBM Watson credentials
api_key = 'YOUR_API_KEY'
url = 'YOUR_SERVICE_URL'

# Set up the authenticator and service
authenticator = IAMAuthenticator(api_key)
visual_recognition = VisualRecognitionV3(version='2018-03-19', authenticator=authenticator)
visual_recognition.set_service_url(url)

# Example of analyzing an image
with open('example_image.jpg', 'rb') as image_file:
    result = visual_recognition.classify(images_file=image_file).get_result()

print(result)

Replace 'YOUR_API_KEY' and 'YOUR_SERVICE_URL' with the actual credentials from your IBM Cloud Watson service.

5. Access Watson Studio

To access Watson Studio via the API, you typically work with different Watson services (such as Watson Machine Learning, Watson Visual Recognition, and Watson Natural Language Understanding). You will use the corresponding Python SDKs to integrate these services with your Watson Studio workflows.

Basic Tutorials of IBM Watson Studio: Getting Started

To get started with IBM Watson Studio, here are some initial steps:

1. Create a Project:
   • Open Watson Studio, go to the Projects page, and click “Create Project.”
   • Choose “Standard” or “Enterprise” and provide a name for your project.
2. Add Data Assets:
   • Upload a CSV file or connect to a data source.
   • Use the Data Refinery tool to clean and transform your data.
3. Launch a Notebook:
   • Open the Notebooks tab and create a new notebook.
   • Choose a runtime environment, such as Python or R.
4. Build a Simple Model:
   • Use the AutoAI feature to automate model building.
   • Explore different algorithms, compare their performance, and select the best one.
5. Deploy Your Model:
   • After training, save your model to the Model Repository.
   • Deploy it as a REST API and test it with sample inputs.

The post What is IBM Watson Studio and Its Use Cases? appeared first on Artificial Intelligence.

Facebook seeks to placate lawmakers over Libra but Mnuchin raises “serious concerns”
Facebook has made known that it won’t launch its digital currency Libra until it receives regulatory approval especially after US Treasury Secretary Steve Mnuchin expresses very serious concerns that Libra could be misused by terrorists. Apart from being supervised by the Swiss Financial Markets Supervisory Authority it will also register in the US with FinCEN. Libra does not intend to compete with any sovereign currencies or intruding the monetary policy arena. It is designed to act like cash only to create economic empowerment.

Google’s new ‘data echoing’ technique speeds up AI training
Search engine giant, Google’s researchers think that the best-performing data echoing algorithms can match the baseline’s predictive performance using less upstream processing. The approach used by these researchers in data echoing inserts a stage in the pipeline that repeats the output data of the previous stage, recovering idle compute capacity. Data echoing is an alternative to optimizing the training pipeline or adding additional workers to perform upstream data processing which may not be possible.

Salesforce named a leader in data management platforms by independent research firm
CRM solutions provider, Salesforce has been recognised a leader by Forrester Research in its report namely ‘The Forrester Wave: Data Management Platforms,’ Q2 2019. Salesforce’s Data Management Platform (DMP), Audience Studio enables marketers to know, personalise and engage their customers across all verticals. It also provides a platform to capture customer data, segment audiences with Einstein AI for engagement. Salesforce Marketing Cloud helps marketers customise marketing with Einstein and analyse the impact to improve their campaign performance.

Synthio introduces Contact Data Maintenance for Salesforce
Synthio has revealed immediate availability of its Contact Data Management for Salesforce (CDM-SF) platform. This platform compares a customer’s Salesforce database against more than 160 million data records and automatically updates it with complete and correct information. Companies implementing CDM-SF are certain about the data they receive being correct and complete in order to build Account Based Marketing and demand generation programs, customer billing, and partner management.

IBM builds AI ladder with partnerships
Multinational information technology company, IBM has joined forces with data preparation technology provider Trifacta to improve the data preparation technology it provides in its data and artificial intelligence stack. This partnership has resulted in InfoSphere Advanced Data Preparation to enable organisations in formatting, structuring and enriching datasets while working with their existing environments including data lakes. Trifacta’s clients include GSK, the Centers for Disease Control, Google, and Pepsico.

IBM announces AutoAI For Watson Studio
IBM has announced AutoAI, a new set of capabilities for Watson Studio to automate complicated tasks associated with designing, optimising and governing AI in the enterprise. Watson Studio’s AI capabilities work efficiently with Watson Machine Learning (ML) to solve the challenges by automating and speeding steps in the AI lifecycle. The AutoAI capabilities are available on the IBM Cloud and they automate data prep and pre-processing including model development and feature engineering. They also contain a suite of powerful model types for enterprise data science and aim to scale ML deployment quickly.
Read More >>

IBM offers employees ‘master’s programs’ in AI and data science
IBM is now offering its employees the opportunities to take courses in data science and artificial intelligence with Simplilearn. The courses will be web-based including live virtual classrooms access to teaching assistants, self-paced video instruction courses and assessments. Upon completion, employees will be granted certificates from Simplilearn and IBM.

Oracle bets big on ‘autonomous offering’ for security as cyber criminals embrace AI, ML
Cloud services provider, Oracle aims to automate its database by creating a tool, Web Application Firewall, that eliminates manual errors in database tuning, security, backups, and updates. The cloud service provider believes that security should be embedded by default. This tool makes use of AI and ML to secure itself and has features that can identify good bots from bad bots by furthering whitelisting good bots letting more traffic in and blacklisting bad bots.

Amazon partners with the NHS to help UK patients access health advice
Amazon has partnered with UK’s NHS to help elderly people access their health advice online through Amazon’s AI-powered voice assistant Alexa. The Department of Health is looking forward to this partnership to empower patients take control of their health. The UK government has set up a new unit called NHSX to boost the use of digital technologies in the UK health service. Secretary of State for Health and Social Care, Matt Hancock, said: “We want to empower every patient to take better control of their healthcare and technology like this is a great example of how people can access reliable, world-leading NHS advice from the comfort of their home, reducing the pressure on our hardworking GPs and pharmacists.”

FCA to explore the ‘explainability’ of AI in consumer finance
The Financial Conduct Authority (FCA) is joining forces with the Alan Turing Institute to test the transparency and explainability of AI in the financial sector. The watchdog wants a practical understanding of machine learning and its functions in comparison to just debates. Christopher Woolard, executive director of strategy and competition at the FCA said: “AI and its application in financial services is causing us to ask big questions – and the answers we arrive at have the potential to fundamentally alter society and the established order.”

Samsung Pay integrates loan and credit card applications in India
Samsung Pay has tapped Paisabazaar to let people apply for things such as credit cards and loans through Samsung Pay. This service calls Paisabazaar’s ‘Chance of Approval’ feature that makes use of predictive algorithms to connect consumers with suitable lenders with a possibility of approving a loan. Sanjay Razdan, senior director, services, management, Samsung India, says: “Our partnership with Paisabazaar.com has helped us create a holistic platform that gives them the comfort of opting for various financial products besides using it as a payment platform.”

Lloyds partners with Blue Motor Finance to develop instant payments API
Lloyds Bank has partnered with Blue Motor Finance to provide motor dealerships with realtime loan payments through a commercial direct debit API collaboration. Co-developed by Lloyds Bank Commercial Banking API Lab, the programme taps the Faster Payment rails and aims to deliver instant loans to motor trader clients of Blue. Steve Everett, MD, payments and cash Management, Lloyds Bank Commercial Banking said: “We’re excited to partner with Blue Motor Finance to bring this innovative instant payment service to its motor trader customers and create real value for businesses in the car financing industry through new API-enabled solutions.”

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International Business Machines Corporation IBM recently updated Watson Studio with AutoAI capabilities.

As the name suggests, AutoAI enables enterprises to automate complex arduous processes, comprising optimizing, designing and other related tasks by leveraging robust AI capabilities.

Markedly, innovation in machine learning (ML) and improvement in data set quality drives development of AI tools. IBM rolled out AutoAI with an aim to free up time for data engineers to design sturdy ML models to expedite AI deployment.

AutoAI capabilities function in sync with Watson Machine Learning to accelerate AI processes considerably. AutoAI is available in “Watson Studio on the IBM Cloud.”

IBM is leaving no stone unturned to strategically infuse AI capabilities across its offerings, which hold promise. AutoAI is anticipated to boost adoption of IBM Watson Studio services, consequently bolstering the company’s top line in the days ahead.

Noteworthy Features

With AutoAI, data scientists can utilize “hyperparameter optimization capabilities” which facilitates development of AI models. Moreover, AutoAI is loaded with robust enterprise data science models, including gradient boosted trees which allow users to upscale ML deployment processes.

In a bid to empower users with deep learning (DL) capabilities, AutoAI allows access to IBM Neural Networks Synthesis (NeuNetS). Notably, NeuNetS utilizes AI to aid development of DL model and automates data synthesis across custom-designed neural networks. Further, NeuNetS provides users with the flexibility to prioritize accuracy or speed of the data model.

Increasing Spend on AI Favors Growth Prospects

Huge volume of data is being generated through a wide range of sources. In a bid to harness this data constructively, the companies intend to leverage AI to provide specific solutions and enhance solutions’ capabilities. However, deployment of AI tools is not trouble free.

For instance, per an IBM study, Shifting Toward Enterprise-Grade AI , conducted last year, 63% of surveyed individuals cited “lack of proper technical skills was a prime challenge to AI implementations.”

Moreover, according to a Forrester study, 60% of surveyed candidates claimed management of data quality to be one of the headwinds in delivering AI applications.

Markedly, IBM’s new Watson AutoAI capabilities provide companies with robust data models, consequently accelerating their AI processes.

We must note that the number of companies utilizing enterprise AI is increasing, which favors the prospects of AutoAI tool in Watson Studio. Per a report by IBM, customer satisfaction is the primary reason behind around 77% of organizations focusing on AI investments.

Further, IDC data projects worldwide spending on AI systems to hit $79.2 billion by 2022 at a CAGR of 38% from 2018 to 2022.

The move is expected to strengthen the company’s position in machine learning as a service (MLaaS) market, which is envisioned to hit $8.31 billion by 2023 per a MarketReportsWorld report, as revealed by MarketWatch.

The aforementioned secular trends reinforce IBM’s growth prospects in the longer haul.

Competition a Woe

Rising competition from Amazon Web Services (AWS) and Microsoft Azure, which dominate the cloud infrastructure services market, is a headwind. Notably, the cloud players are strengthening their IT infrastructure with advanced ML capabilities. For instance, Microsoft recently rolled out additional functionalities to Azure Machine Learning.

According to latest first quarter data from Synergy Research, IBM Cloud is classified as “strong niche player” with comparatively “lower growth rates.”

Zacks Rank & Key Picks

IBM carries a Zacks Rank #3 (Hold).

Some better-ranked stocks worth considering in the broader sector are eGain Corporation EGAN , Rosetta Stone Inc. RST and j2 Global, Inc. JCOM , each sporting Zacks Rank #1 (Strong Buy). You can see the complete list of today’s Zacks #1 Rank stocks here .

Long-term earnings growth rate for eGain, Rosetta Stone and j2 Global is pegged at 30%, 12.5% and 8%, respectively.

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The post IBM Watson Studio’s Latest AutoAI Tool to Automate Processes appeared first on Artificial Intelligence.

For years, the sheer messiness of data slowed efforts to launch artificial intelligence (A.I.) and machine learning projects. Companies weren’t willing to wait a year or two while data analysts cleaned up a massive dataset, and executives sometimes had a hard time trusting the outputs of a platform or tool built on messy data.

Data pre-processing is a well-established art, and there are many tech pros out there who specialize in tweaking datasets for maximum validity, accuracy, and completeness. It’s a tough job, and someone has to do it (usually with the assistance of tools, as well as specialized libraries such as Pandas). But now IBM is trying to apply A.I. to this issue, via new data prep tools within AutoAI, itself a tool within the cloud-based Watson Studio.

“We have seen that complexity of data infrastructures can be daunting to the most sophisticated companies, but it can be overwhelming for those with little to no technical resources,” Rob Thomas, General Manager of IBM Data and AI, wrote in a statement.“The automation capabilities we’re putting Watson Studio are designed to smooth the process and help clients start building ML models and experiments faster.”

In addition to data cleanup, AutoAI includes a number of other tools for building A.I. and ML algorithms, including ones that set optimal hyperparameters (which are the parameters with values set before the machine’s learning begins). There’s also IBM Neural Networks Synthesis, or NeuNetS, which creates customized neural networks (users are asked to optimize for either speed or accuracy).

IBM is competing fiercely with Google (which is plunging into the ML-automation game with AutoML Video and AutoML Tables, with other tools surely on the way) and Microsoft (which has automation and recommendation tools built into its Azure Machine Learningplatform) to claim the attention of companies interested in the A.I./ML market. If that wasn’t enough of a crowded landscape, Amazon is plunging heavily into the enterprise-automation game with Amazon Personalize, which streamlines everything from mobile-app development to email marketing.

Of course, the rise of A.I./ML automation could lead to a new host of problems. Sure, having tech professionals build bespoke algorithms and tools in-house is a painstaking process with a fair amount of risk (if you fail, you’ve burned tons of time and resources), but there’s the reasonable expectation that you’ll have something tailored to your needs, based on reliable data and math. When you begin to automate these processes, you risk obfuscating at least a portion of the data and logic behind dashboards—which might lead some to question the output of the work.

Then again, many firms can’t afford to even begin an internal, customized A.I./ML program; in that context, these automated solutions are the best (and perhaps only) bet if they want to get into this particular game.

For tech professionals, these new tools are yet another sign that the A.I./ML market is maturing. Those professionals who understand how these tools work—as well as the underlying logic and theories—will have their pick of positions, as companies desperate for A.I./ML talent are willing to pay enormous salaries and benefits. Although these technologies might seem daunting, there are a number of resources designed to give you a solid education; check them out.

The post IBM’s AutoAI and the Race to Automate ML and A.I. appeared first on Artificial Intelligence.

Deploying AI-imbued apps and services isn’t as challenging as it used to be, thanks to offerings like IBM’s Watson Studio (previously Data Science Experience). Watson Studio, which debuted in 2017 after a 12-month beta period, provides an environment and tools that help to analyze, visualize, cleanse, and shape data; to ingest streaming data; and to train and optimize machine learning models in real time. And today, it’s becoming even more capable with the launch of AutoAI, a set of features designed to automate tasks associated with orchestrating AI in enterprise environments.

“IBM has been working closely with clients as they chart their paths to AI, and one of the first challenges many face is data prep — a foundational step in AI,” said general manager of IBM Data and AI Rob Thomas in a statement. “We have seen that complexity of data infrastructures can be daunting to the most sophisticated companies, but it can be overwhelming for those with little to no technical resources. The automation capabilities we’re putting Watson Studio are designed to smooth the process and help clients start building machine learning models and experiments faster.”

AutoAI, which is also available in IBM Cloud, automates data prep and preprocessing steps including feature engineering, or the process of using domain knowledge of data to create elements core to AI algorithms. It handles hyperparameter optimization (i.e., choosing a set of optimal hyperparameters for a learning algorithm, where “hyperparameter” refers to the value set before the learning process begins), and it boasts a growing suite of powerful pretrained model types such as gradient boosted trees.

Also on tap in Watson Studio AutoAI is IBM’s Neural Networks Synthesis, or NeuNetS, a platform intended to expedite deep learning model development by leveraging AI to synthesize custom neural networks automatically, and by allowing users to choose to prioritize speed or accuracy. It runs on IBM Cloud with Kubernetes, and it doesn’t require knowledge of code or experience with deep learning frameworks, IBM says.

NeuNetS launched in preview last fall and is available in beta within Watson Studio projects.

Watson Studio AutoAI’s unveiling comes months after IBM brought Watson Studio, Watson Assistant, and AI OpenScale to private clouds and public platforms like Google Cloud Platform, AWS, and Microsoft’s Azure through an integration with IBM Cloud Private for Data. Coinciding with that news, IBM launched AI Digital Automation, a service that collects and analyzes patterns in data to identify tasks that can be automated.

IBM competes with Google, Microsoft, Amazon, and others in the machine learning as a service (MLaaS) market, which is anticipated to reach $5.5 billion by 2023. Microsoft in early March announced enhancements to Azure Machine Learning, its service that enables users to architect predictive models, classifiers, and recommender systems for cloud-hosted and on-premises apps. In April during its I/O 2019 developer conference, Google introduced AutoML Video and AutoML Tables for structured data, two new classes for Google’s suite of services that automate the creation of AI systems. And just this week, Amazon announced the general availability of Amazon Personalize, an AWS service that facilitates the development of websites, mobile apps, and content management and email marketing systems that suggest products, provide tailored search results, and customize funnels on the fly.

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