Databricks today unveiled a new cloud-based machine learning offering that’s designed to give engineer everything they need to build, train, deploy, and manage ML models.

The new offering is designed to bridge the gap in existing machine learning products that arises by focusing too much on data engineering, ML model creation, or the deployment aspects of the machine learning cycle, Databricks says.

“Many ML platforms fall short because they ignore a key challenge in machine learning: they assume that data are available at high quality and ready for training,” Databricks says in its announcement. “That requires data teams to stitch together solutions that are good at data but not AI, with others that are good at AI but not data.”

To address this gap, Databricks lets users switch between user “experiences” that it exposes, including data science/engineering, SQL analytics, and machine learning experiences, to access tools and features relevant to their everyday workflow.

The new Databricks offering includes two major components: AutoML and Feature Store.

Databricks AutoML, like other AutoML solutions, automates many of the steps that data scientists must manually go through in terms of experimenting and testing different machine learning models. But instead of working like a “black box,” like other AutoML offerings, the new Databricks offering works like a “glass box,” according to Databricks vice president of marketing Joel Minnick.

AutoML will give data scientists the ability to peer into the inner workings of the model to see how things are working, either through the Databricks UI, through an API call, or through the notebook interface, he says.

“We will do everything that you expect an AutoML tool to do, like analyzing the data, figuring out the features, training and tuning the model,” he says. “But what we give you on the end of that process is all the experiment we ran, all of the notebooks we auto-generated as a result of those [runs] and let you compare those models and decide, perhaps I’d be willing to give up a point of accuracy if I can get inference 200 milliseconds faster with this model versus perhaps the most accurate model.”

If edge cases arise and the model is acting up, Databricks AutoML lets the data scientist “dive into the notebook code and have full control over that notebook code, so that that if I do need to spend some time accounting for edge cases, doing some additional tuning, I can do that,” Minnick says. “I can also understand how this model works, so if I have to explain to regulatory or to compliance authorities exactly how I’m making these decisions, I’m able to do that and be sure that that transparency is there.”

Feature Store, meanwhile, automatically tracks the machine learning features that are core to the functioning of the machine learning models. This helps to ensure the performance of the model doesn’t drift over time. The Feature Store is integrated with Databricsk’ Delta Lake platform and uses Delta Lake APIs. It’s also integrated with MLflow, which simplifies ongoing management of the model.

The features are embedded directly into the models when packaged with MLFlow, which simplifies the process of changing the features at a later time, Minnick says.

“So I never have to engage the application engineering team to make changes to the client application just because I’m evolving the features,” he tells Datanami. “This is a way for us to help customer get models to production and iterate those models faster and easier than before.”

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A mobile data handling concept developed by Germany-based specialist ViGEM should, according to the company, make 24/7 utilization of test fleets for ADAS and AD validation driving feasible.

The system is based on the fast, uncomplicated exchange and shipment of robust removable data storage devices independent from the vehicle under test. As ViGEM’s CEO, Markus Trauth, explained, “Thanks to the robustness of our removable data storages and the mobile data handling concept, our customers are able to reduce the downtime of their vehicles to a minimum compared to other data transfer methods. Test fleets can thus be utilized almost 24/7, which holds significant potential for cost savings for ViGEM customers.”

In practice this means that large amounts of test data can be made quickly available to manufacturers’ development departments while testing is still ongoing. Working in cooperation with OEMs, ViGEM says it has developed a suite of integrated hardware and software components (Car Communication Analyzer – CCA) for the efficient validation of advanced driving functions. By ensuring seamless interaction between elements such as dataloggers, robust storage media and fast copy stations, featuring transfer rates of up to 50Gb/s, maximum data reliability and security are ensured.

For example, the SSDs installed in its CCA 9010 solution, with up to 64TB storage capacity and data rates of up to 25Gb/s, can continuously record raw data for approximately eight hours. Their shock- and vibration-tested metal housings can withstand even the highest mechanical stresses and allow operating temperatures from -20°C to +65°C, meaning they can be removed from vehicles and transported safe in the knowledge the data they contain is secure.

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DataRobot’s AI platform makes it easy to build predictive models to identify infection hotspots

A new data-driven project is launching with the ambitious aim of resolving the COVID-19 pandemic in 60 days. 

ContagionNET has been designed by DataRobot, a global enterprise AI company that enables easy building and deployment of predictive models. Aimed at solving the data sharing problem that has plagued the COVID-19 pandemic response, the not-for-profit initiative will help determine when a patient is at their most contagious.

ContagionNET combines affordable antigen checks performed at home, anonymous data collection, and an AI platform. With these tools DataRobot are aiming to identify the most contagious areas in a community and encourage people to take preventative action. The goal is to identify people earlier than traditional testing (i.e., when they are most infectious) to disrupt the chain of transmission. 

ContagionNET can also predict how many people need to participate on a county-by-county basis by measuring the impact of different participation levels and testing frequency. This localised approach allows those with the highest viral load to self-isolate earlier, preventing further spread of the virus. 

“The reality is that those most likely to spread COVID-19 aren’t usually aware they are contagious” said Sally Embrey, DataRobot’s VP of Public Health and Health Technologies. 

“With the proliferation of frequent, easy-to-use at home antigen checks, ContagionNET is poised to inform the most contagious of their risk and help them make lifestyle modifications that will significantly reduce spread of the virus.”

DataRobot has been involved in pandemic response since early 2020. Working with the US government, DataRobot’s forecasting helped vaccine manufacturers select the right participants and prioritise enrollment in the highest risk locations over each vaccine trial period. 

The organisation has also used modeling to improve testing distribution, equity, and diagnostic reporting, all of which have informed its approach to ContagionNET. 

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Dell Technologies has announced Dell EMC PowerScale, a new family of storage systems engineered with storage software and server hardware to capture and capitalise on unstructured data, such as documents, images, videos and social media content.

“The amount of unstructured data enterprises store as file or object storage is expected to triple by 2024, and there are no signs of it slowing,” says Dell Technologies storage president and general manager Dan Inbar. 

“In this data era, businesses need a simple, seamless and cost-effective way to store and use unstructured data to innovate, create differentiation and bring products to market faster. The Dell EMC PowerScale family provides the foundation companies need to unlock the potential of their data, no matter where it resides, and use it to drive meaningful business impact.”

Dell EMC PowerScale runs on OneFS, the operating system that powers EMC Isilon. 

The PowerScale family features new 1U PowerEdge-based PowerScale all-flash and NVMe nodes and existing Isilon all-flash, hybrid and archive nodes running the OneFS 9.0 operating system.

It delivers up to 15.8 million input-output operations per second (IOPS) per cluster, offering performance to handle demanding AI, analytics, IOT, digital media, healthcare and life sciences workloads.

New all-flash PowerScale F200 nodes bring up to five times more performance than its predecessor. Enhanced inline data reduction makes the platform up to six times more efficient.

PowerScale can start small and grow to massive, petabyte-scale.

• Scale without disruption: PowerScale clusters can scale from 11TB raw capacity to 60PB and millions of file operations without disruption or downtime. Nodes can be added to either PowerScale or an existing Isilon cluster in 60 seconds.
   
• Intelligent automation: With smart scale-out capabilities, PowerScale distributes resources effectively so that customers can get the most performance out of a cluster. 
   
• Resilient and efficient: Through flexible failover policies, PowerScale delivers up to 85% storage utilisation across a cluster and can sustain multi-node failures.
   
• Programmable infrastructure: With support for a number of leading management and container orchestration frameworks, such as Kubernetes, and Ansible, customers can streamline application development and reduce deployment timeframes.

Dell Technologies makes it easy for customers to understand their data and storage infrastructure health through software included with PowerScale.

• Put data to work: DataIQ software helps companies extract business value from unstructured data, typically uncategorised and found in siloes throughout businesses. DataIQ breaks down data silos by delivering a single view of file and object data across Dell EMC, third-party and public cloud storage. Users can gain better control over their data, ensure the right teams have access to it, and make the most of their investment by ensuring data is stored on the right tier within their storage environment. 
   
• Proactive health monitoring: CloudIQ infrastructure monitoring and analytics software combines machine learning and human intelligence to provide real-time performance and capacity analysis as well as historical tracking for a single view of Dell EMC infrastructure.

PowerScale supports a wide variety of file protocols and customers can easily deploy it to meet their infrastructure needs.

• Any application: PowerScale OneFS 9.0 features broad multiprotocol support, including new S3 support for modern apps relying on object storage. Additional support for protocols including NFS, SMB and HDFS, allow customers to run a large number of traditional and modern applications without compatibility concerns.
   
• Deployment flexibility: PowerScale is easily deployed in core data centres, in edge locations or as part of a multi-cloud strategy. The compact 1U footprint and minimum cluster size of new all-flash F200 and NVMe F600 PowerScale nodes make it an ideal option for edge deployments.
   
• Multi-cloud support: PowerScale for Multi-cloud can directly connect to all major public clouds as a managed service – ideal for customers looking to move or deploy demanding apps in the cloud. Google Cloud customers looking to save time and management complexity can choose Dell Technologies Cloud PowerScale for Google Cloud, a native cloud service that combines the performance, scale, and consistent experience of PowerScale with the economics and simplicity of Google Cloud.

With Dell Technologies On Demand, PowerScale customers can respond to workload spikes and new service requests with elastic capacity and cloud economics. 

Several flexible pay-per-use choices with short-and-long term commitment options are available, including a one year term for flexible consumption.

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Amdocs this week announced that KT Corporation, the largest quad-play service provider in South Korea, has started to upgrade and migrate their existing product catalog to Amdocs CatalogONE. 

This solution enables operators to create, deploy, test and launch new services at a much faster pace and quickly take advantage of new 5G use cases and revenue opportunities.

Amdocs CatalogONE is one of the building blocks of CES20, Amdocs’ new cloud-native and microservices-based customer experience suite. It enables end customers to benefit from more frequent service innovation and updated plans and bundles, as well as more market-driven promotions, such as offerings around special events and new 5G features, tailored for specific customer segments and sales channels.

With a centralized view of all products and services and advanced user interface, collaboration platform and approvals and notification management capabilities, the simple-to-use Amdocs CatalogONE allows business and marketing users to manage the offering lifecycle and create frequent configuration updates. To be deployed on the cloud, it will enable KT and Amdocs teams to efficiently collaborate and handle multiple business requests in parallel. 

Having previously deployed Amdocs’ real-time convergent charging solution, KT will be able to further accelerate its ability to introduce and monetize new 5G consumer and enterprise offerings. 

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Foster City-based Aible has launched Aible Advanced, its new solution for data scientists and developers that automates the machine learning process from data-to-impact.

Aible Advanced empowers data scientists with a platform that advances beyond legacy semi-AutoML solutions such as DataRobot to enable data scientists to achieve business impact quickly and efficiently. Aible Advanced automates all the repetitive and boring parts of the end-to-end data science and model deployment process so data scientists and developers can focus on the parts where they add the most value.

The Only AI Platform Purpose-Built to Understand Business Objectives

In March, Aible launched the only Artificial Intelligence/Machine Learning solution for business people that understands business objectives and constraints, then crafts a custom prediction model that maximizes business impact. Forrester called it the “best choice for pure businesspeople” in The Forrester New Wave™: Automation-Focused Machine Learning Solutions Q2 2019 – The Nine Providers That Matter Most And How They Stack Up by Kjell Carlsson, Ph.D. and Mike Gualtieri et al. published 28 May, 2019.

Aible Advanced is the new complementary solution for data scientists and developers that takes what makes Aible unique and helps organizations adopt it at scale.

“Business users have the business domain knowledge; data scientists have the modeling expertise,” said Arijit Sengupta, Aible’s Founder and CEO. “Aible Advanced, in conjunction with Aible, lets each type of user seamlessly contribute their unique skills and knowledge to generate the best predictive model for their unique business reality.”

The Ten Steps of True AutoML

• Requirement Gathering whereAutoML solution learns the business objectives and business realities of the use case
   
• Blueprints where it helps users get started by showing them examples of good predictors for their use case
   
• Data Recipes where it helps gather the training data from existing enterprise applications and data repositories
   
• Data Enhancement where it cleans the data, prepares it for machine learning and even creates derived variables that can improve the quality of the predictive model
   
• Model Customization where it ensure the model is trained to maximize the actual business objectives of the use case while respecting the business constraints
   
• Hyperparameter Tuning where it trains various types of models and tries out many settings for each model type to create the best model
   
• Model Selection where it recommends the model that best optimizes the business objectives and conducts evaluations like sensitivity, what-if and scenario analysis to ensure the best model is chosen
   
• Model Deployment where it starts running the model in the customer’s environment 
   
• Prediction Writeback where it writes predictions back to the enterprise applications the business users use
   
• Monitoring where it observes the actual business outcomes and compares it to the predicted outcomes to determine how well the predictive model is doing and whether it needs to be retrained

AutoML solutions today barely cover half of these ten steps, while Aible Advanced automatically conducts all of them. That is why Aible calls Aible Advanced the first true AutoML solution. In fact, all other AutoML solutions miss the most important steps, Requirement Gathering and Model Customization,  which enable Aible Advanced to focus on delivering the unique needs of each use case, rather than focusing on maximizing simplistic measures like accuracy.

If, for instance, the benefit of successfully pursuing a sales opportunity is $100 and the cost of pursuing it and failing is $1, then you might be willing to pursue 98 sales opportunities and fail at each as long as you successfully sold one deal. Such an approach would be highly inaccurate based on simplistic accuracy measures like Log Loss, but it would create significant business benefits. Forrester independently stated in their New Wave report that , “Unique among AutoML vendors, Aible gets that a model that maximizes accuracy almost never maximizes business impact.”

Aible is offering a 30-minute AutoML challenge: in less than 30 minutes, Aible will create a predictive model based on users’ proprietary data in their own AWS account and deploy the model also in their own AWS account without ever moving the data beyond the security of the user’s account. If Aible fails to  do that, users can keep the model free of charge. No other AutoML vendor offers such a challenge because they actually use expensive consulting to do several of the 10 steps of a true AutoML offering.

The combination of Aible Advanced for data scientists and Aible for business people allows experts to scale themselves by enforcing best practices while easily soliciting business user input to maximize business impact. Aible Advanced users can solicit feedback from Aible users or even delegate specific steps of the end-to-end process to specific business people using Aible. Business users can ensure the solution meets their business objectives and respects their business constraints. Data scientists can ensure the rigor of the end-to-end process because they can see every aspect of the model training process in Python code in notebooks.

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