#ocado API

[arctechnolabs1]

How ArcTechnolabs Builds Grocery Pricing Datasets in UK & Australia

Introduction

In 2025, real-time grocery price intelligence is mission-critical for FMCG brands, retailers, and grocery tech startups...

ArcTechnolabs specializes in building ready-to-use grocery pricing datasets that enable fast, reliable, and granular price comparisons...

Why Focus on the UK and Australia for Grocery Price Intelligence?

The grocery and FMCG sectors in both regions are undergoing massive digitization...

Key Platforms Tracked by ArcTechnolabs:

How ArcTechnolabs Builds Pre-Scraped Grocery Pricing Datasets

Step 1: Targeted Platform Mapping

UK: Tesco (Superstore), Ocado (Online-only)

AU: Coles (urban + suburban), Woolworths (nationwide chain)

Step 2: SKU Categorization

Dairy

Snacks & Beverages

Staples (Rice, Wheat, Flour)

Household & Personal Care

Fresh Produce (location-based)

Step 3: Smart Scraping Engines

Rotating proxies

Headless browsers

Captcha solvers

Throttling logic

Step 4: Data Normalization & Enrichment

Product names, pack sizes, units, currency

Price history, stock status, delivery time

Sample Dataset: UK Grocery (Tesco vs Sainsbury’s)

ProductTesco PriceSainsbury’s PriceDiscount TescoStock1L Semi-Skimmed Milk£1.15£1.10NoneIn StockHovis Wholemeal Bread£1.35£1.25£0.10In StockCoca-Cola 2L£2.00£1.857.5%In Stock

Sample Dataset: Australian Grocery (Coles vs Woolworths)

Product Comparison – Coles vs Woolworths

Vegemite 380g

--------------------

Coles: AUD 5.20 | Woolworths: AUD 4.99

Difference: AUD 0.21

Discount: No

Dairy Farmers Milk 2L

---------------------------------

Coles: AUD 4.50 | Woolworths: AUD 4.20

Difference: AUD 0.30

Discount: Yes

Uncle Tobys Oats

------------------------------

Coles: AUD 3.95 | Woolworths: AUD 4.10

Difference: -AUD 0.15 (cheaper at Coles)

Discount: No

What’s Included in ArcTechnolabs’ Datasets?

Attribute Overview for Grocery Product Data:

Product Name: Full title with brand and variant

Category/Subcategory: Structured food/non-food grouping

Retailer Name: Tesco, Sainsbury’s, etc.

Original Price: Base MRP

Offer Price: Discounted/sale price

Discount %: Auto-calculated

Stock Status: In stock, limited, etc.

Unit of Measure: kg, liter, etc.

Scrape Timestamp: Last updated time

Region/City: London, Sydney, etc.

Use Cases for FMCG Brands & Retailers

Competitor Price Monitoring – Compare real-time prices across platforms.

Retailer Negotiation – Use data insights in B2B talks.

Promotion Effectiveness – Check if discounts drive sales.

Price Comparison Apps – Build tools for end consumers.

Trend Forecasting – Analyze seasonal price patterns.

Delivery & Formats

Formats: CSV, Excel, API JSON

Frequencies: Real-time, Daily, Weekly

Custom Options: Region, brand, platform-specific, etc.

Book a discovery call today at ArcTechnolabs.com/contact

Conclusion

ArcTechnolabs delivers grocery pricing datasets with unmatched speed, scale, and geographic depth for brands operating in UK and Australia’s dynamic FMCG ecosystem.

Source >> https://www.arctechnolabs.com/arctechnolabs-grocery-pricing-datasets-uk-australia.php

[productdata]

Real-Time FMCG Price Intelligence in USA Using Walmart & Amazon

Introduction

In the fast-moving world of consumer goods, pricing changes by the hour. Whether you're a global CPG brand or a challenger startup, staying competitive on Walmart and Amazon USA requires more than strategy—it requires real-time data. FMCG brands are now turning to web scraping as a powerful tool to gain real-time pricing intelligence and optimize across regions, SKUs, and seasons. By extracting Amazon Grocery product data and web scraping Walmart Grocery product data , businesses can gather insights that help them respond to pricing fluctuations and market trends faster.

This blog explains how companies are leveraging scraped pricing data from Walmart and Amazon to track their competitors, align with retail partners, and dynamically adjust their strategies in the high-demand USA market.

What is Real-Time Price Intelligence?

Real-time FMCG price intelligence means tracking:

Product-level pricing (MRP, selling price, discounts)

Promo structures (BOGO, flat %, loyalty discounts)

Stock availability & delivery time

Seller type (retail vs 3P)

Regional or zip code-based price differences

Price changes over time (hourly, daily, weekly)

Key Use Cases for Scraping Walmart & Amazon

Use CaseBenefitCompetitor Price MatchingStay within pricing corridors to win Buy Box & shelf spacePromo Timing OptimizationAlign your promotions with competitors' flash dealsRegional Price BenchmarkingIdentify price inconsistencies across U.S. citiesSubscription Strategy PlanningAnalyze Amazon's Subscribe & Save discountsInventory & OOS MonitoringSpot stock-outs in competitors and capture demand

Sample Data Snapshot (May 2025)

ProductPlatformRegionMRP ($)Price ($)DiscountStockPromo TypeTide Pods 42ctAmazonNYC$19.99$16.4918%YesSubscribe & SaveTide Pods 42ctWalmartNYC$19.99$15.9820%YesRollbackLysol Spray 12ozAmazonChicago$6.99$6.497%YesLimited Time DealLysol Spray 12ozWalmartChicago$6.99$6.793%NoRegular

Case Study: Real-Time Pricing Transforms Strategy for CPG Brand

A Fortune 500 CPG brand partnered with Product Data Scrape to track over 5,000 SKUs on Amazon and Walmart.

Goals:

Beat competitor prices by 2–4% in key metro areas

Monitor promo cycles for top categories (cleaning, snacks, beverages)

Prevent over-discounting across retail partners

What They Did:

Scraped prices every hour for high-velocity SKUs

Tracked delivery delays and OOS patterns

Correlated Amazon coupon timings with sales spikes

Results:

+21% Buy Box Wins on Amazon

+15% promo ROI due to optimized timing

12% decrease in price-matching penalties from Walmart

Visual Workflow (Turn into Infographic)

mathematica

CopyEdit

Scrape Walmart & Amazon → Extract Pricing + Promo + Stock Data →

Match SKUs → Analyze Regionally → Push Alerts & Dashboards → Take Action

Benefits of Real-Time Price Intelligence

Faster Reaction Times

Brands can adjust pricing or escalate issues within hours, not weeks.

Better Retailer Relations

Stay in compliance with MAP (Minimum Advertised Pricing) while still staying competitive.

Competitive Promo Strategy

Identify windows where your competitors offer steep discounts and counter them.

Forecasting & Trend Analysis

Track seasonality, Black Friday/Cyber Monday trends, and stock cycles by category.

Country Comparison: Why This Matters Globally

CountryUse CaseUSAWalmart + Amazon dynamic pricing + MAP trackingGermanyREWE, Lidl SKU monitoring & eco-label trackingAustraliaColes & Woolworths – promo alignmentUKTesco & Ocado flash discountsIndiaBlinkit, Zepto, and JioMart hyperlocal pricing

Technical Stack Used

ComponentTech StackScraping EnginePython + PlaywrightProxy SystemRotating Residential IP PoolSchedulerAWS Lambda + CloudWatchStoragePostgreSQL + S3DashboardPower BI / Looker StudioNotificationsSlack, Email Digest, API Webhooks

Legal & Ethical Considerations

Public-facing pages only (no login-protected or private seller data)

Compliant with U.S. data laws and Walmart/Amazon robots.txt boundaries

Throttled & timed scrapers to minimize server load

No scraping of consumer/personal data

Platform Differences: Walmart vs Amazon

FeatureWalmartAmazonDiscount TypeRollback, Clearance, BundleCoupons, Subscribe & Save, Lightning DealsSeller Type1P (First-Party) and 3P (Third-Party)Heavily 3P (Third-Party)Stock VisibilityClearVaries by sellerDelivery EstimationReal-timeSeller-dependentPrice Frequency ChangeModerateHigh

Top FMCG Categories to Scrape

1. Detergents & Cleaners

2. Snacks & Beverages

3. Pet Food

4. Health Supplements

5. Baby Care

6. Beauty & Personal Care

Weekly Dashboard Views Provided by Product Data Scrape

Top Price Drops by City

Amazon vs Walmart Price Gap Charts

Stock-Out Alerts for Competitor SKUs

Category-Wise Promo Heatmaps

Hourly Price Volatility Tracker

Final Takeaway

If you're selling FMCG products in the USA, you're competing on price—by the minute. Web scraping Walmart and Amazon for real-time pricing data isn't just smart—it's now mandatory for brand survival.

[foodspark-scraper]

Extract Ocado grocery delivery data from the internet and websites to aid your pricing policies and boost your business outcomes Know more : https://www.foodspark.io/how-web-scraping-is-used-to-deliver-ocado-grocery-delivery-data.php

[techreviewpage]

Cloud AutoML: Making AI accessible to every business

When we both joined Google Cloud just over a year ago, we embarked on a mission to democratize AI. Our goal was to lower the barrier of entry and make AI available to the largest possible community of developers, researchers and businesses.

Our Google Cloud AI team has been making good progress towards this goal. In 2017, we introduced Google Cloud Machine Learning Engine, to help developers with machine learning expertise easily build ML models that work on any type of data, of any size. We showed how modern machine learning services, i.e., APIs—including Vision, Speech, NLP, Translation and Dialogflow—could be built upon pre-trained models to bring unmatched scale and speed to business applications. Kaggle, our community of data scientists and ML researchers, has grown to more than one million members. And today, more than 10,000 businesses are using Google Cloud AI services, including companies like Box, Rolls Royce Marine, Kewpie and Ocado.

But there’s much more we can do. Currently, only a handful of businesses in the world have access to the talent and budgets needed to fully appreciate the advancements of ML and AI. There’s a very limited number of people that can create advanced machine learning models. And if you’re one of the companies that has access to ML/AI engineers, you still have to manage the time-intensive and complicated process of building your own custom ML model. While Google has offered pre-trained machine learning models via APIs that perform specific tasks, there’s still a long road ahead if we want to bring AI to everyone.

To close this gap, and to make AI accessible to every business, we’re introducing Cloud AutoML. Cloud AutoML helps businesses with limited ML expertise start building their own high-quality custom models by using advanced techniques like learning2learn and transfer learning from Google. We believe Cloud AutoML will make AI experts even more productive, advance new fields in AI and help less-skilled engineers build powerful AI systems they previously only dreamed of.

Our first Cloud AutoML release will be Cloud AutoML Vision, a service that makes it faster and easier to create custom ML models for image recognition. Its drag-and-drop interface lets you easily upload images, train and manage models, and then deploy those trained models directly on Google Cloud. Early results using Cloud AutoML Vision to classify popular public datasets like ImageNet and CIFAR have shown more accurate results with fewer misclassifications than generic ML APIs.

Here’s a little more on what Cloud AutoML Vision has to offer:

Increased accuracy: Cloud AutoML Vision is built on Google’s leading image recognition approaches, including transfer learning and neural architecture search technologies. This means you’ll get a more accurate model even if your business has limited machine learning expertise.

Faster turnaround time to production-ready models: With Cloud AutoML, you can create a simple model in minutes to pilot your AI-enabled application, or build out a full, production-ready model in as little as a day.

Easy to use: AutoML Vision provides a simple graphical user interface that lets you specify data, then turns that data into a high quality model customized for your specific needs.

AutoML

×

“Urban Outfitters is constantly looking for new ways to enhance our customers’ shopping experience,” says Alan Rosenwinkel, Data Scientist at URBN. “Creating and maintaining a comprehensive set of product attributes is critical to providing our customers relevant product recommendations, accurate search results and helpful product filters; however, manually creating product attributes is arduous and time-consuming. To address this, our team has been evaluating Cloud AutoML to automate the product attribution process by recognizing nuanced product characteristics like patterns and neckline styles. Cloud AutoML has great promise to help our customers with better discovery, recommendation and search experiences.”

Mike White, CTO and SVP, for Disney Consumer Products and Interactive Media, says: “Cloud AutoML’s technology is helping us build vision models to annotate our products with Disney characters, product categories and colors. These annotations are being integrated into our search engine to enhance the impact on Guest experience through more relevant search results, expedited discovery and product recommendations on shopDisney.”

And Sophie Maxwell, Conservation Technology Lead at the Zoological Society of London, tells us: “ZSL is an international conservation charity devoted to the worldwide conservation of animals and their habitats. A key requirement to deliver on this mission is to track wildlife populations to learn more about their distribution and better understand the impact humans are having on these species. In order to achieve this, ZSL has deployed a series of camera traps in the wild that take pictures of passing animals when triggered by heat or motion. The millions of images captured by these devices are then manually analysed and annotated with the relevant species, such as elephants, lions and giraffes, etc., which is a labour-intensive and expensive process. ZSL’s dedicated Conservation Technology Unit has been collaborating closely with Google’s Cloud ML team to help shape the development of this exciting technology, which ZSL aims to use to automate the tagging of these images—cutting costs, enabling wider-scale deployments and gaining a deeper understanding of how to conserve the world’s wildlife effectively.”

If you’re interested in trying out AutoML Vision, you can request access via this form.

AutoML Vision is the result of our close collaboration with Google Brain and other Google AI teams, and is the first of several Cloud AutoML products in development. While we’re still at the beginning of our journey to make AI more accessible, we’ve been deeply inspired by what our 10,000+ customers using Cloud AI products have been able to achieve. We hope the release of Cloud AutoML will help even more businesses discover what’s possible through AI.

References

Learning Transferable Architectures for Scalable Image Recognition, Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le. Arxiv, 2017.

Progressive Neural Architecture Search, Chenxi Liu, Barret Zoph, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy, Arxiv, 2017.

Large-Scale Evolution of Image Classifiers, Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Quoc Le, Alex Kurakin. International Conference on Machine Learning, 2017.

Neural Architecture Search with Reinforcement Learning, Barret Zoph, Quoc V. Le. International Conference on Learning Representations, 2017.

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi. AAAI, 2017.

Bayesian Optimization for a Better Dessert, Benjamin Solnik, Daniel Golovin, Greg Kochanski, John Elliot Karro, Subhodeep Moitra, D. Sculley. NIPS, Workshop on Bayesian Optimization, 2017.

Cloud AutoML: Making AI accessible to every business syndicated from http://ift.tt/2whjk0n

[mialiciousness]

SKIPPY® Peanut Butter & Chocolate Crunchy Bars

SKIPPY® Selai Kacang & Coklat Renyah

Ini SKIPPY® Bilah selai kacang adalah suguhan yang mudah dan manis tanpa harus memanggang! Semua orang akan menyukai bar kenyal, manis & asin yang siap ini tidak ada waktu sama sekali.

Waktu persiapan: 5 menit (plus waktu pendinginan)

Waktunya memasak: 10 menit

100 gram Kacang ekstra renyah SKIPPY® mentega

1. Olesi baking 20cm x 30cm baki.

2. Tambahkan mentega, gula, sirup, dan selai kacang ke wajan besar. Lelehkan semua bahan dengan api kecil sampai gula larut. Didihkan dan biarkan mendidih di atas panas rendah selama 5 menit tanpa diaduk.

3. Sekarang tambahkan cornflakes dan aduk hingga rata. Sebarkan campuran ke dalam loyang, tekan dengan kuat dan dinginkan selama 30 menit.

4. Lelehkan cokelat dalam a mangkuk kecil di atas wajan berisi air mendidih (jangan biarkan air menyentuh dasar mangkuk) atau dalam microwave, sampai halus. Oleskan cokelat di atas krisis selai kacang, berdirilah di kamar suhu sampai diatur sebelum memotong. Nikmati.

Selai Kacang Skippy® (RRP £ 2,40 per 340g) tersedia dalam varietas halus dan renyah, yang saat ini tersedia dalam jumlah besar supermarket termasuk Sainsbury, Morrisons, Costco, bagian Internasional dari Tesco dan Ocado. Untuk informasi dan resep lebih lanjut, kunjungi www.peanutbutter.uk.com

Mentega kacang SKIPPY® EXTRA CRUNCHY

Menginginkan rasa kacang yang mengenyangkan? Tidak terlihat lagi dari SKIPPY® EXTRA CRUNCHY® Peanut Butter. Setiap toples dicampur dengan banyak yang asli potongan kacang, sehingga Anda mendapatkan rasa menyenangkan dari SKIPPY® Peanut Butter, plus banyak renyah.

SKIPPY® EXTRA SMOOTH Peanut Butter

Oleskan pada senyum dengan kacang halus, lembut, dan meleleh di mulut kesempurnaan mentega. SKIPPY® Smooth Peanut Butter menambahkan lebih banyak yum dan menyenangkan hanya tentang apapun. Bukan apa-apa selain ngemil dengan klasik krem ​​ini.

Source link

from WordPress https://ift.tt/2kWhbqZ via IFTTT

[android-for-life]

"What a week! 105 announcements from Google Cloud Next '18"

Google Cloud Next ‘18 was incredible! From fantastickeynotes and fireside chats to GO-JEK CTO Ajey Gore appearing on-stage on a scooter to listening to Target CIO Mike McNamara we had an inspiring, educational and entertaining week at our flagship conference. We were joined by over 23,000 leaders, developers and partners from our Google Cloud community, listened to more than 290 customer speakers share their stories of business transformation in the cloud and took part in hundreds of breakout sessions. The theme of the conference was Made Here Together, and we’re so grateful to everyone who attended and contributed to help build the cloud for everyone.  

But the week of Next wouldn’t be complete without a comprehensive list of what happened. So without further ado, here are 105 product and solution launches, customer stories and announcements from Next ‘18.

Customers

eBay—The world’s largest global marketplace is leveraging Google Cloud in many different ways, including experimenting with conversational commerce with Google Assistant, building ML models with Cloud TPUs for image classification, and applying AI to help buyers quickly find what they’re looking for.

GO-JEK—This ride-hailing and logistics startup in Jakarta uses Google Cloud to support its hundreds of thousands of concurrent transactions, Maps for predicting traffic and BigQuery to get data insights.

Lahey Health—Lahey’s journey to the cloud included migrating from four legacy email systems to G Suite in 91 days.

LATAM Airlines—South America’s largest airline uses G Suite to connect teams, and GCP for data analytics and creating 3D digital elevation models.

LG CNS—LG is looking to Google Cloud AI, Cloud IoT Edge and Edge TPU to build its Intelligent Vision inspection tool for better quality and efficiency in some of its factories.

HSBC—One of the world’s leading banking institutions shares how they’re using data analytics on Google Cloud to extract meaningful insights from its 100PB of data and billions of transactions.

The New York Times—The newest way the New York Times is using Google Cloud is to scan, encode, and preserve its entire historical photo archive  and evolve the way the newsroom tells stories by putting new tools for visual storytelling in the hands of journalists.

Nielsen—To support its nearly 45,000 employees in 100 countries with real-time collaboration and cost-effective video conferencing, Nielsen turned to G Suite.

Ocado—This online-only supermarket uses Google Cloud’s AI capabilities to power its machine learning model for responding to customer requests and detecting fraud much faster.

PayPal—PayPal discusses the hows and whys of their journey to the public cloud.

Scotiabank—This Canadian banking institution shares its views on modernizing and using the cloud to solve inherent problems inside an organization.

Sky—The UK media company uses Google Cloud to identify and disconnect pirate streaming sites during live sporting events.

Target—Moving to Google Cloud has helped Target address challenges like scaling up for Cyber Monday without disruptions, and building new, cutting-edge experiences for their guests.

20th Century Fox—The renowned movie studio shares how it’s using BigQuery ML to understand audience preferences.

Twitter—Twitter moved large-scale Hadoop clusters to GCP for ad hoc analysis and cold storage, with a total of about 300 PB of data migrated.

Veolia—This environmental solution provider moved its 250 systems to G Suite for their anytime, anywhere, any-device cloud project.

Weight Watchers—How Weight Watchers evolved its business, including creating mobile app and an online community to support its customers’ lifestyles.

Partners

2017 Partner Awards—Congratulations to the winners! These awards recognize partners who dedicated themselves to creating industry-leading solutions and strong customer experiences with Google Cloud.

SAP and Deloitte collaboration—Customers can run SAP apps on GCP with Deloitte’s comprehensive tools.

Updates to our Cisco partnership—Includes integrations between our new Call Center AI solution and Cisco Customer Journey solutions, integrations with Webex and G Suite, and a new developer challenge for hybrid solutions.

Digital Asset and BlockApps—These launch partners are helping users try Distributed Ledger Technology (DLT) frameworks on GCP, with open-source integrations coming later this year.

Intel and Appsbroker—We’ve created a cloud center of excellence to make high-performance cloud migration a lot easier.

NetApp—New capabilities help customers access shared file systems that apps need to move to cloud, plus Cloud Volumes are now available to more GCP customers.

VMware vRealize Orchestrator—A new plug-in makes it easy to use GCP alongside on-prem VMware deployments for efficient resource provisioning.

New partner specializations—We’ve recently welcomed 19 partners in five new specialization areas (bringing the total areas to nine) so customers can get even more industry-specific help moving to cloud.

SaaS-specific initiative—A new set of programs to help our partners bring SaaS applications to their customers.

Accenture Google Cloud Business Group, or AGBG—This newly formed group brings together experts who’ll work with enterprise clients to build tailored cloud solutions.

Partnership with NIH—We’re joining with the National Institutes of Health (NIH) to make more research datasets available, integrate researcher authentication and authorization mechanisms with Google Cloud credentials, and support industry standards for data access, discovery, and cloud computation.

Partnership with Iron Mountain—This new partnership helps enterprises extract hard-to-find information from inside their stored documents.

Chrome, Devices and Mobility

Cloud-based browser management—From a single view, admins can manage Chrome Browser running on Windows, Mac, Chrome OS and Linux.

Password Alert Policy—Admins can set rules to prevent corporate password use on sites outside of the company’s control.

Managed Google Play (out of beta)—Admins can curate applications by user groups as well as customize a broad range of policies and functions like application blacklisting and remote uninstall.

Google Cloud Platform | AI and machine learning

Cloud AutoML Vision, AutoML Natural Language, and AutoML Translation (all three in beta)—Powerful ML models that can be extended to suit specific needs, without requiring any specialized knowledge in machine learning or coding.

Cloud Vision API (GA)—Cloud Vision API now recognizes handwriting, supports additional file types (PDF and TIFF), and can identify where an object is located within an image.

Cloud Text-to-Speech (beta)—Improvements to Cloud Text-to-Speech offer multilingual access to voices generated by DeepMind WaveNet technology and the ability to optimize for the type of speaker you plan to use.

Cloud Speech-to-Text—Updates to this API help you identify what language is being spoken, plus provide word-level confidence scores and multi-channel (multi-participant) recognition.

Training and online prediction through scikit-learn and XGBoost in Cloud ML Engine (GA) —While Cloud ML Engine has long supported TensorFlow, we’re releasing XGBoost and scikit-learn as alternative libraries for training and classification.

Kubeflow v0.2—Building on the previous version, Kubeflow v0.2 makes it easier for you to use machine learning software stacks on Kubernetes. Kubeflow v0.2 has an improved user interface and several enhancements to monitoring and reporting.

Cloud TPU v3 (alpha)—Announced at this year’s I/O, our third-generation TPUs are now available for Google Cloud customers to accelerate training and inference workloads.

Cloud TPU Pod (alpha)—Second-generation Cloud TPUs are now available to customers in scalable clusters. Support for Cloud TPUs in Kubernetes Engine is also available in beta.

Phone Gateway in Dialogflow Enterprise Edition (beta)—Now you can assign a working phone number to a virtual agent—all without infrastructure. Speech recognition, speech synthesis, natural language understanding and orchestration are all managed for you.

Knowledge Connectors in Dialogflow Enterprise Edition (beta)—These connectors understand unstructured documents like FAQs or knowledge base articles and complement your pre-built intents with automated responses sourced from internal document collections.

Automatic Spelling Correction in Dialogflow Enterprise Edition (beta)—Natural language understanding can sometimes be challenged by spelling and grammar errors in a text-based conversation. Dialogflow can now automatically correct spelling mistakes using technology similar to what’s used in Google Search and other products.

Sentiment Analysis in Dialogflow Enterprise Edition (beta)—Relies on the Cloud Natural Language API to optionally inspect a request and score a user's attitude as positive, negative or neutral.

Text-to-Speech in Dialogflow Enterprise Edition (beta)—We’re adding native audio response to Dialogflow to complement existing Speech-to-Text capability.

Contact Center AI (alpha)—A new solution which includes new Dialogflow features alongside other tools to perform analytics and assist live agents.

Agent Assist in Contact Center AI (alpha)—Supports a live agent during a conversation and provides the agent with relevant information, like suggested articles, in real-time.

Conversational Topic Modeler in Contact Center AI (alpha)—Uses Google AI to analyze historical audio and chat logs to uncover insights about topics and trends in customer interactions.

Google Cloud Platform | Infrastructure services

Managed Istio (alpha)—A fully-managed service on GCP for Istio, an open-source project that creates a service mesh to manage and control microservices.

Istio 1.0—Speaking of open-source Istio, the project is imminently moving up to version 1.0.

Apigee API Management for Istio (GA)—Soon you can use your existing Apigee Edge API management platform to wrangle microservices running on the Istio service mesh.

Stackdriver Service Monitoring (early access)—A new view for our Stackdriver monitoring suite that shows operators how their end users are experiencing their systems. This way, they can manage against SRE-inspired SLOs.

GKE On-Prem with multi-cluster management (coming soon to alpha)—A Google-configured version of Kubernetes that includes multi-cluster management and can be deployed on-premise or in other clouds, laying the foundation for true hybrid computing.

GKE Policy Management (coming soon to alpha)—Lets you take control of your Kubernetes environment by applying centralized policies across all enrolled clusters.

Resource-based pricing for Compute Engine (rolling out this fall)—A new way we’re calculating sustained use discounts on Compute Engine machines, aggregating all your vCPUs and memory resources to maximize your savings.

Google Cloud Platform | Application development

GKE serverless add-on (coming soon to alpha)—Runs serverless workloads that scale up and down automatically, or respond to events, on top of Kubernetes Engine.

Knative—The same technologies included in the GKE serverless add-on are now available in this open-source project.

Cloud Build (GA)—Our fully managed continuous integration and continuous delivery (CI/CD) platform lets you build container and non-container artifacts and integrates with a wide variety of tools from across the developer ecosystem.

GitHub partnership—GitHub is a popular source code repository, and now you can use it with Cloud Build.

New App Engine runtimes—We’re adding support for the popular Python 3.7 and PHP 7.2 runtimes to App Engine standard environment.

Cloud Functions (GA)—Our event-driven serverless compute service is now generally available, and includes support for additional languages, plus performance, networking and security features.

Serverless containers on Cloud Functions (early preview)—Packages a function within a container, to better support custom runtimes, binaries and frameworks.  

Google Cloud Platform | Data analytics

BigQuery ML (beta)—A new capability that allows data analysts and data scientists to easily build machine learning models directly from BigQuery with simple SQL commands, making machine learning more accessible to all.

BigQuery Clustering (beta)—Creates clustered tables in BigQuery as an added layer of data optimization to accelerate query performance.

BigQuery GIS (public alpha)—New functions and data types in BigQuery that follow the SQL/MM Spatial standard. Handy for PostGIS users and anyone already doing geospatial analysis in SQL.

Sheets Data Connector for BigQuery (beta)—A new way to directly access and refresh data in BigQuery from Google Sheets.

Data Studio Explorer (beta)—Deeper integration between BigQuery and Google Data Studio to help users visualize query results quickly.

Cloud Composer (GA)—Based on the open source Apache Airflow project, Cloud Composer distributes workloads across multiple clouds.

Customer Managed Encryption Keys for Dataproc—Customer-managed encryption keys that let customers create, use and revoke key encryption for BigQuery, Compute Engine and Cloud Storage. Generally available for BigQuery; beta for Compute Engine and Cloud Storage.

Streaming analytics updates, including Python Streaming and Dataflow Streaming Engine (both in beta)—Provides streaming customers more responsive autoscaling on fewer resources, by separating compute and state storage.

Dataproc Autoscaling and Dataproc Custom Packages (alpha)—Gives users Hadoop and Spark clusters that scale automatically based on the resource requirements of submitted jobs, delivering a serverless experience.

Google Cloud Platform | Databases

Oracle workloads on GCP—We’re partnering with managed service providers (MSPs) so you can run Oracle workloads on GCP using dedicated hardware.

Compute Engine VMs powered by Intel Optane DC Persistent Memory—Lets you run SAP HANA workloads for more capacity at lower cost.

Cloud Firestore (beta)—Helps you store, sync and query data for cloud-native apps. Support for Datastore Mode is also coming soon.

Updates to Cloud Bigtable—Regional replication across zones and Key Visualizer, in beta, to help debug performance issues.

Updates to Cloud Spanner—Lets users import and export data using Cloud Dataflow. A preview of Cloud Spanner’s data manipulation language (DML) is now available.

Resource-based pricing model for Compute Engine—A new billing model gives customers more savings and a simpler bill.

Google Cloud Platform | IoT

Edge TPU (early access)—Google’s purpose-built ASIC chip that’s designed to run TensorFlow Lite ML so you can accelerate ML training in the cloud and utilize fast ML inference at the edge.

Cloud IoT Edge (alpha)—Extends data processing and machine learning capabilities to gateways, cameras and end devices, helping make IoT devices and deployments smart, secure and reliable.

Google Cloud Platform | Security

Context-aware access—Capabilities to help organizations define and enforce granular access to GCP APIs, resources, G Suite, and third-party SaaS apps based on a user’s identity, location and the context of their request.

Titan Security Key—A FIDO security key that includes firmware developed by Google to verify its integrity.

Shielded VMs (beta)—A new way to leverage advanced platform security capabilities to help ensure your VMs haven’t been tampered with or compromised.

Binary Authorization (alpha)—Lets you enforce signature validation when deploying container images.

Container Registry Vulnerability Scanning (alpha)—Automatically performs vulnerability scanning for Ubuntu, Debian and Alpine images to help ensure they are safe to deploy and don’t contain vulnerable packages.

Geo-based access control in Cloud Armor (beta)—Lets you control access to your services based on the geographic location of the client trying to connect to your application.

Cloud HSM (alpha)—A fully managed cloud-hosted hardware security module (HSM) service that allows you to host encryption keys and perform cryptographic operations in FIPS 140-2 Level 3 certified HSMs.  

Access Transparency (coming soon to GA)—Provides an audit trail of actions taken by Google Support and Engineering in the rare instances that they interact with your data and system configurations on Google Cloud.

G Suite | Enterprise collaboration and productivity

New investigation tool in the Security Center (Early Adopter Program)—A new tool in the security center for G Suite that helps admins identify which users are potentially infected, see if anything’s been shared externally and remove access to Drive files or delete malicious emails.

Data Regions for G Suite (available now for G Suite Business and Enterprise customers)—Lets you choose where to store primary data for select G Suite apps—globally, distributed, U.S. or Europe.

Smart Reply in Hangouts Chat—Coming soon to G Suite, Smart Reply uses artificial intelligence to recognize which emails need responses and proposes reply options.

Smart Compose in Gmail—Coming soon to G Suite, Smart Compose intelligently autocompletes emails for you by filling in greetings, common phrases and more.

Grammar Suggestions in Google Docs (Early Adopter Program)—Uses a unique machine translation-based approach to recognize grammatical errors (simple and complex) and suggest corrections.

Voice Commands for Hangouts Meet hardware (coming to select Hangouts Meet hardware customers later this year)—Brings some of the same magic of the Google Assistant to the conference room so that teams can connect to video meetings quickly.

The new Gmail (GA)—Features like redesigned security warnings, snooze and offline access are now generally available to G Suite users.

New functionality in Cloud Search—Helps organizations intelligently and securely index third-party data beyond G Suite (whether the data is stored in the cloud or on-prem).

Google Voice to G Suite (Early Adopter Program)—An enterprise version of Google Voice that lets admins manage users, provision and port phone numbers, access detailed reports and more.

Standalone offering of Drive Enterprise (GA)—New offering with usage-based pricing to help companies easily transition data from legacy enterprise content management (ECM) systems.

G Suite Enterprise for Education—Expanding to 16 new countries.

Jamboard Mobile App—Added features for Jamboard mobile devices, including new drawing tools and a new way to claim jams using near-field communication (NFC).

Salesforce Add-on in Google Sheets—A new add-on that lets you import data and reports from Salesforce into Sheets and then push updates made in Sheets back to Salesforce.

Social Impact

Data Solutions for Change—A program that empowers nonprofits with advanced data analytics to drive social and environmental impact. Benefits include role-based support and Qwiklabs.

Visualize 2030—In collaboration with the World Bank, the United Nations Foundation, and the Global Partnership for Sustainable Development Data, we’re hosting a data storytelling contest for college or graduate students.

Harambee Youth Employment Accelerator—We’re helping Harambee connect more unemployed youth with entry-level positions in Johannesburg by analyzing large datasets with BigQuery and machine learning on Cloud Dataflow.

Foundation for Precision Medicine—We’re aiding the Foundation for Precision Medicine to find a cure for Alzheimer’s disease by scaling their patient database to millions of anonymized electronic medical record (EMR) data points, creating custom modeling, and helping them visualize data.

Whew! That was 104. Thanks to all our customers, partners, and Googlers for making this our best week of the year.

But wait, there’s more! Here’s the 105th announcement: Next 2019 will be April 9-11 at the newly renovated Moscone in San Francisco. Please save the date!  

Source : The Official Google Blog via Source information

[opticien2-0]

How Ocado is putting machine-learning to use in combatting fraud

Ocado [IRDX ROCA] is putting cloud and machine learning technologies to use in detecting fraud.

The retailer, a Top50 retailer in IRUK Top500 research, is using technologies developed through its in-house Ocado Technology business and says machine learning has made its fraud detection rate 15 times more precise. It believes this is the first example of a retailer using machine-learning to combat fraud.

Previously, its fraud agents, equipped with a rules-based detection system, might spot trends in transactions that later prove to be fraudulent. There might be a correlation between baskets containing large alcohol orders and confirmed instances of fraud, and they might look for this trend in future. But those carrying out the transactions could quickly shift to another area, making this a cat and mouse game that the agents must work hard to catch up with, says Ocado.

Now, say Ocado, the new technology will make the job of those agents easier: its machine-learning model, which has been developed and put into use over the last six months, will predict results in real-time and present the likelihood of a transaction being fraudulent. The agent must then decide simply whether, based on that probability, the order should be cancelled.

Roland Plaszowski, head of retail systems at Ocado Technology, said: “Removing fraud altogether is the final goal but it will be a long road. It’s worth remembering fraud is one part of this but it’s quite rare – it’s one in every thousand orders, so one part is to eliminate fraud which brings a lot of costs to the company but the other part is to make the user experience better.” The other part, he says, is in moving away from systems that flag up legitimate transactions, disappointing customers if their order is cancelled. “There’s a balance between removing fraud and doing so positively,” he said.

The graphic below shows how the machine-learning system works.

Customer order information is stored and analysed using BigQuery, before being processed using Dataflow. There the data is normalised into a format required for machine-learning algorithms such as the Deep Neural networks that use TensorFlow. Dataflow is used to transfer data to Google Cloud storage and Datastore. Cloud machine learning then uses data from cloud storage to produce models as APIs. The Ocado fraud detection model, powered by TensorFlow, then reads the data from Datastore and uses cloud machine learning APIs to make real-time predictions.

This latest example of machine-learning in retail follows Ocado’s previous use of the technology to improve its handling of customer queries, and in its fulfillment and logistics operations. It also uses the technology for tasks such as product recommendations, enabling it to avoid showing meat to vegetarians or gluten-containing products to coeliacs.

The post How Ocado is putting machine-learning to use in combatting fraud appeared first on InternetRetailing.

from InternetRetailing http://ift.tt/2FunEBF via IFTTT

[spivertw-blog]

企業的顧客服務也能透過「機器學習」改善！Google先前在舊金山召開全球雲端用戶大會《2017 Google Cloud Next》，舉辦了一場工作坊指導如何運用Google雲端平台的「機器學習」工具改進客戶服務。 自然語言處理或情緒分析等「機器學習」工具能從歷史數據裡擷取特定資訊，協助企業更快速、精準地為顧客解決問題。當一個公司把每名用戶的交流歷史整合到客服中，這些數據就可以讓顧客體驗到定製化的個人服務。 在Google Next活動中，Google 雲端團隊的產品經理Apoorv Saxena 主持了一場講座，聚焦在機器學習工具如何為客服加分。Saxena 認為企業能採取兩種做法：第一種方式是透過已經經過訓練的API處理企業問題，例如Google已經提供的API如 Cloud Vision API、Cloud Speech API、Cloud Jobs API、Cloud Translation API、Cloud Video Intelligence API、Cloud Natural Language API。 「其中和客服最相關的為 Cloud Speech API和Natural Language API」Saxena補充表示。「Cloud Speech 技術可以逐字紀錄80種語言的電話語音內容，配合其他的機器學習API可以進一步分析這些文字內容，而Natural Language API可以從客戶回應中判斷他們的情緒。」 若企業想要為組織量身打造解決方案，Google建議他們運用 TensorFlow 和 Cloud Machine Learning Engine 打造並訓練自己的系統。 網路超市Ocado 引進機器學習，將每天客服中心收到的2,000封電子信件透過 Natural Language API為每封信貼上「Feedback (回饋)」或「Positive (正面)」等標籤。再運用TensorFlow 和 Cloud Machine Learning […]

[nicaurybenitezcortorreal-blog]

How to use machine learning to improve customer service

Image: iStockphoto/Jirsak

One area of business that stands to benefit from machine learning is customer service. Technologies like natural language processing and sentiment analysis can help companies better understand how to respond to customer feedback and questions.

At the 2017 Google Cloud Next conference in San Francisco, Apoorv Saxena, a product manager on the Google Cloud team, hosted a breakout session explaining how organizations can use Google products to improve their customer service. He began by outlining the two different approaches that companies can take to machine learning.

The first approach, Saxena said, is to use pre-trained models in the form of APIs to handle the machine learning needs of the company. For example, Google offers APIs such as its Cloud Vision API, Cloud Speech API, Cloud Jobs API, Cloud Translation API, Cloud Video Intelligence API, and the Cloud Natural Language API.

The two most relevant APIs for customer service are the Cloud Speech API and the Natural Language API.

Cloud Language can transcribe text in over 80 languages and detect inappropriate content. Users can then analyze the text output with other machine learning APIs from Google. In customer service, one would use this to transcribe audio from customer service calls or voicemails.

The Natural Language API lets you extract entities from text, such as a person, place, or thing. It then gives extra metadata, pointing the user to a Wikipedia page for that entity, if one exists. It can also analyze sentiment (from customer feedback, for example), or analyze syntax so the user knows how the words depend on each other.

However, if a company needs a more unique solution, they would have to build a custom system using TensorFlow and the Cloud Machine Learning engine. They would start by training the system with examples of content, including classifiers. Then, they would be able to serve it in production.

As a case study, Saxena brought up Dan Nelson, the head of data for online supermarket Ocado, to explain how his company has implemented machine learning. Nelson said that Ocado gets roughly 2,000 emails into its contact centers a day, ranging from refund requests, to general feedback, to website trouble, and more.

Starting with the Natural Language API, Nelson said they were able to label message with tags such as "Feedback" or "Positive." However, they then built a custom solution with Cloud Machine Learning and TensorFlow to get more detailed filtering. It sits outside of their storage layer and allows the company to more effectively triage their customer service requests, Nelson said.

As a result, Nelson said that Ocado was able to respond to urgent emails four times faster, and saved money on headcount in the contact center. Nelson recommended that companies investigating machine learning would define their success criteria early and be careful to set control groups and perform a lot of testing.

The 3 big takeaways for TechRepublic readers

Google has been increasingly investing in machine learning, opening up new tools for customers to use to improve efforts in customer service.

Google customers can approach machine learning with pre-built APIs, or use TensorFlow and the Cloud Machine Learning platform to build a custom solution.

Customers should define their criteria early and test often when working with machine learning.

Also see

Conner Forrest has nothing to disclose. He doesn't hold investments in the technology companies he covers.

Conner Forrest is News Editor for TechRepublic. He covers enterprise technology and is interested in the convergence of tech and culture.

Source

http://www.techrepublic.com/article/how-to-use-machine-learning-to-improve-customer-service/

[omgpasadena3blog-blog]

pasadena website design

Digital marketing is the newest trend of marketing of this era. It is that type of marketing which allow promotion of goods in digital technologies through smart phones. It has changed the way of adverting products in the market. Google’s launching of the new video API machine is the biggest achievement and will definitely change the scenario of webside design pasadena It will prove to be a big tool in enhancing the advertisement of the business, product, objects or their investment in their organizations. There are a number of ways where digital marketing has changed the prospective human mind setup and as the technology is getting better day by day it is making it so easy for customers in choosing their products and organization with a simple search engine button that is ‘Google’.

By using web design pasadena platforms, businesses can create competitive advantage through various means. Firms use social media as their main tool to create a channel of information, to reach the maximum potential of digital marketing. Through this a business can create a system in which they are able to pinpoint behavioural patterns of clients and feedback on their needs. This means, of content, has shown to have a larger impingement on those having a long standing relationship with the firm and also with the consumers who are frequently active social media users. Relative to this, creating a social media page will not only increase the quality of the relationship between new consumers and the existing ones but also consistent brand reinforcement. Therefore, improving brand awareness resulting in a possible rise for consumers up the Brands awareness Pyramid.

Why need Artificial Intelligence in Digital Marketing?

The answer is very simple. It is just to make life easier. It will make the work of markers very easy to work with and reduce various efforts of managing and complicated software tools. It will give marketers to grow and flourish in their organizations. Two important points are:

·         Artificial intelligence is enhancing an increased essential role in the internet business. There are many businesses who are trying hard to compete with the flourishing business in the market. One of the most important goals is to make machine learning a transformational tool for organizations of any size, sophistication or industry.

 ·         Google Cloud Platform is seeing customers in making it part of a wider data analytics strategy, with early adopters like Airbus Disney and Ocado serving as inspirational use cases. Google Cloud 2017 announces new products research and education programs to ensure machine learning are accessible to all businesses, data scientists and developers. Google Cloud introduces Kaggle in the system. It is the home to the world's largest community of data scientists and machine teaching enthusiasts, Kaggle is initiated by more than 800,000 data experts to explore, analyze and interpret the latest updates in machine learning and data analytics.

To know more about it please visit http://networkingbizz.com

[michellesteel1-blog]

Pasadena Website Design

Digital marketing is the newest trend of marketing of this era. It is that type of marketing which allow promotion of goods in digital technologies through smart phones. It has changed the way of adverting products in the market. Google’s launching of the new video API machine is the biggest achievement and will definitely change the scenario of Pasadena Website Design it will prove to be a big tool in enhancing the advertisement of the business, product, objects or their investment in their organizations. There are a number of ways where digital marketing has changed the prospective human mind setup and as the technology is getting better day by day it is making it so easy for customers in choosing their products and organization with a simple search engine button that is ‘Google’.

By using Website Design Pasadena platforms, businesses can create competitive advantage through various means. Firms use social media as their main tool to create a channel of information, to reach the maximum potential of digital marketing. Through this a business can create a system in which they are able to pinpoint behavioural patterns of clients and feedback on their needs. This means, of content, has shown to have a larger impingement on those having a long standing relationship with the firm and also with the consumers who are frequently active social media users. Relative to this, creating a social media page will not only increase the quality of the relationship between new consumers and the existing ones but also consistent brand reinforcement. Therefore, improving brand awareness resulting in a possible rise for consumers up the Brands awareness Pyramid.

Why need Artificial Intelligence in Digital Marketing?

The answer is very simple. It is just to make life easier. It will make the work of markers very easy to work with and reduce various efforts of managing and complicated software tools. It will give marketers to grow and flourish in their organizations. Two important points are:  

·         Artificial intelligence is enhancing an increased essential role in the internet business. There are many businesses who are trying hard to compete with the flourishing business in the market. One of the most important goals is to make machine learning a transformational tool for organizations of any size, sophistication or industry.

 ·         Google Cloud Platform is seeing customers in making it part of a wider data analytics strategy, with early adopters like Airbus Disney and Ocado serving as inspirational use cases. Google Cloud 2017 announces new products research and education programs to ensure machine learning are accessible to all businesses, data scientists and developers. Google Cloud introduces Kaggle in the system. It is the home to the world's largest community of data scientists and machine teaching enthusiasts, Kaggle is initiated by more than 800,000 data experts to explore, analyze and interpret the latest updates in machine learning and data analytics.

To know more about it please visit http://networkingbizz.com

[bitesbitesbites]

Announcing Google Cloud Video Intelligence API, and more Cloud Machine Learning updates

Artificial intelligence is playing an increasingly essential role in the enterprise, however, more and more businesses find themselves struggling to keep up. One of our most important goals is to make machine learning a transformational tool for organizations of any size, industry or sophistication.

We’re seeing customers making it part of their wider data analytics strategy, with early adopters like Airbnb, Airbus, Disney and Ocado serving as inspirational use cases.Today at Google Cloud Next ‘17 we’re excited to announce new products, research and education programs to ensure machine learning is accessible to all businesses, data scientists and developers. We're also thrilled to welcome Kaggle to Google Cloud. Home to the world's largest community of data scientists and machine learning enthusiasts, Kaggle is used by more than 800,000 data experts to explore, analyze and understand the latest updates in machine learning and data analytics

https://cloud.google.com/blog/big-data/2017/03/announcing-google-cloud-video-intelligence-api-and-more-cloud-machine-learning-updates

[opticien2-0]

Tesco, Topps Tiles, Dunelm, Ocado: how Top500 retailers are tackling multichannel, plus ideas from InternetRetailing Summit

In today’s InternetRetailing newsletter we’re still pondering some of the information and ideas shared at this week’s InternetRetailing Summit. We’ve rounded up some of those ideas in our coverage of the event. There’s lots to think about here – and we’ll be carrying the issues raised and opinions shared forward into our future coverage of the sector.

Today we’re also reporting in a week that leading IRUK Top500 retailers have given updates on their approach to multichannel retailing. We have analysis of Ocado’s approach to selling both groceries and its technology platform, and Dunelm as it reassesses its position and plans future improvements to its service. Topps Tiles considers the way it inspires customers as they make the crosschannel shopping journeys that are now the most typical in its business, while Tesco is making multichannel improvements to the way its Clubcard loyalty scheme works that are aimed at improving service from its mobile app to the store.

Today we cover research from Foundit! that looks at how most mobile visitors now arrive on websites via Google Shopping – and what they can do to improve the reception they get.

Our guest comment today is a timely one: it comes from Guy Murphy of Mulesoft who argues that APIs are key to omnichannel retailing.

The Tamebay Ecommerce Cup The Tamebay Ecommerce Cup 2017 will be held on September 7 and five-a-side teams are now being invited to sign up. The tournament, now in its fifth year, is moving to a new venue in Shepherd’s Bush and promises FA refs and a comfy new players’ lounge complete with screens to keep tabs on competitors’ matches.

“We had a total of 26 teams enter last year, and it was great to have both suppliers and retailers competing, with Deliveroo taking the top spot and runners up Uber narrowly missing out,” says Mark Pigou, founder of InternetRetailing Media. “This year promises to be just as competitive.” To enter a team of up to 10 people (five players and five subs), sign up here.

Sponsorship opportunities are limited to three companies: email Joey Evans (joe.evans@tamebay.com) for more.

Webinars Find out more about upcoming InternetRetailing webinars and register for free on the InternetRetailing webinar page. You can also catch up with past webinars on the page: recent sessions have come from IBM Watson on using AI to improve the customer experience, and from SmartFocus and The Entertainer on using social to reach digital customers.

The post Tesco, Topps Tiles, Dunelm, Ocado: how Top500 retailers are tackling multichannel, plus ideas from InternetRetailing Summit appeared first on InternetRetailing.

from InternetRetailing http://ift.tt/2swQJTn via IFTTT