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John Teltsch (IBM)credit score: IBM
IBM has published a string of recent channel initiatives and investments because it seeks to build key accomplice skills and alternatives in the year ahead.
Unveiled utter the artery through PartnerWorld at suppose in San Francisco, magnificient Blue rolled out novel initiatives including its Watson-based collaboration engine known as IBM enterprise accomplice connect; My PartnerWorld; novel application deal registration and IBM energy techniques, LinuxONE and Z ‘In It to Win It’ incentives.
On properly of this, the dealer likewise detailed the artery it become extra investing in ally competencies and enablement, in addition to driving growth alternatives in market segments reminiscent of protection and hybrid cloud.
in particular, IBM traffic companion connect focuses on assisting companions tap into the global ecosystem to collaborate on customer alternatives, develop novel company and convey novel solutions to market.
in the meantime, the Watson-primarily based engine will enable partners with complementary capabilities to locate each other, find to market sooner and promote more collectively, in response to John Teltsch, general manager of associate ecosystem at IBM.
My PartnerWorld, set to launch on 15 February, goals to present a customized on-boarding and enablement journey, giving companions the opening to control PartnerWorld outfit and shortcuts in one region. This contains My revenue pastime (MySA), the PartnerWorld Console, Co-advertising Centre and enterprise associate join platform.
moreover the novel software deal registration will present partners with precedence entry to particular bid pricing in order to carve back combat between know-how suppliers while improving monetary consequences.
Teltsch added this changed into a portion of the supplier's ongoing transformation efforts, which it kicked off final year, to enhance companion journey and profitability, together with initiatives round channel pricing, incentives and streamlined resale authorisation.
In April remaining 12 months, great Blue, restructured its accomplice tryst fashions throughout the channel.
in addition, the IBM energy methods, LinuxONE and Z 'In It to Win It' incentives fill now been made purchasable to companions, which additionally extends the motivation for IBM storage into 2019, along with deploying novel require technology tactics to obligate unique leads.
On precise of these novel initiatives, the vendor is likewise launching an ESA co-advertising providing for partners embedding IBM know-how, and throughout third-celebration marketplaces, apart from bettering its digital APIs amid plans to double the variety of digital offerings.
"We proceed to uphold partners on their adventure to SaaS, with more than four hundred as-a-provider choices, coupled with novel demos, trials and incentives to accelerate boom,” Teltsch noted.
IBM has additionally launched SaaS deal registration and P360, which is a device enabling partners to examine their client’s analytics.
companion capabilities and enablement
Delving deeper, IBM additionally showcased its novel sales enablement answer, Seismic@IBM, a device that features revenue kits, plays, presentations, utilize cases and different assets.
The charge package steer bundle is additionally being multiplied into version 2.0, whereby partners can double their IBM cloud credits; supply entry to a pile sandbox atmosphere; access carve charge vouchers for opt for training movements and additional subscription coupon codes.
as a result, Teltsch observed it become additionally doubling the variety of device channel technical retailers to aid companions within the box.
Going forward, a brand novel managed capabilities security provider application (MSSP) goals to aid partners construct protection practices and carry solutions in either an embedded or reseller model.
A ‘understand Your IBM’ protection income incentive will serve up reloadable IBM Rewards debit cards to partners who promote novel protection licenses and SaaS.
Teltsch talked about it has additionally enabled partners to construct ‘Cloud Paks,’ which are business-grade container software applications with suggestions and step-by-step guidelines for partners to directly build with open specifications a friendly artery to permit hybrid and multi-cloud deployments.
“constructed for Kubernetes on the IBM Cloud and IBM Cloud private, IBM Cloud Paks are designed to accelerate time to construction and enrich readiness at lower can permeate than containers by myself,” he explained.
moreover, a novel Cloud Digital Technical tryst program will deliver companions with entry to technical assets concentrated on product demos and tutorials.
“These belongings deliver a deep understanding of product choices and how they can aid consumers along their journey to Cloud and AI," Teltsch delivered. "Our novel Seismic@IBM will involve a link to this Cloud Digital Technical tryst software so partners can quite simply access this vital assistance whereas preparing for customer engagements."
in accordance with Teltsch, the brand novel initiatives and investments were a response to the "continuous comments" from companions and shoppers.
Teltsch revealed that utter over the past year, partners had doubled their variety of specialist and knowledgeable lore across its know-how stack.
“We’ve likewise considered gigantic momentum in the variety of partners pile their skills and promoting options in strategic areas fancy IBM security, Cloud, IoT and Watson AI, with their second Watson build challenge producing more than 500 AI options and prototypes that run on the IBM Cloud,” Teltsch observed.
all the artery through its fourth quarter outcomes, massive Blue mentioned that its cloud company, grew 12 per cent to US$19.2 billion in 2018.
in addition, the business's cognitive application company, including its synthetic intelligence platform Watson, analytics and cyber protection functions and is its 2d biggest, suggested earnings of US$5.46 billion, in comparison with analysts' expectation of US$5.25 billion.
Julia Talevski attended PartnerWorld at feel in San Francisco as a guest of IBMbe a portion of the newsletter!
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In September 2018, IBM introduced a brand novel product, IBM Db2 AI for z/OS. This simulated intelligence engine monitors records entry patterns from executing SQL statements, uses computer gaining lore of algorithms to settle upon most efficacious patterns and passes this tips to the Db2 query optimizer for utilize with the aid of subsequent statements.desktop discovering on the IBM z Platform
In may additionally of 2018, IBM announced version 1.2 of its desktop researching for z/OS (MLz) product. here's a hybrid zServer and cloud application suite that ingests performance data, analyzes and builds fashions that symbolize the fitness repute of a number of indicators, screens them over time and offers actual-time scoring capabilities.
a few features of this product offering are aimed at assisting a group of model builders and executives. as an instance:
This computing device discovering suite become originally aimed toward zServer-primarily based analytics purposes. one of the vital first glaring decisions became zSystem efficiency monitoring and tuning. device management Facility (SMF) data that are automatically generated by the working system give the raw information for device resource consumption akin to vital processor utilization, I/O processing, reminiscence paging etc. IBM MLz can assemble and reclaim these statistics over time, and construct and instruct fashions of system habits, rating these behaviors, establish patterns not comfortably foreseen via people, augment key efficiency warning signs (KPIs) and then feed the mannequin results returned into the device to fill an sequel on device configuration changes that may enhance performance.
The subsequent step became to implement this suite to investigate Db2 performance statistics. One solution, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the laptop studying technology to Db2 operational information to capitalize an figuring out of Db2 subsystem health. it may well dynamically build baselines for key performance indications, supply a dashboard of these KPIs and provides operational cadaver of workers real-time perception into Db2 operations.
while widespread Db2 subsystem efficiency is an principal element in general application fitness and efficiency, IBM estimates that the DBA steer cadaver of workers spends 25% or more of its time, " ... combating access direction issues which trigger performance degradation and service impact.". (See Reference 1).AI comes to Db2
believe the plight of simultaneous DBAs in a Db2 environment. In modern day IT world they fill to steer one or greater great data applications, cloud application and database features, utility setting up and configuration, Db2 subsystem and software performance tuning, database definition and administration, catastrophe recuperation planning, and extra. query tuning has been in existence given that the origins of the database, and DBAs are usually tasked with this as well.
The heart of question path analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to entry the records, experiences the areas of the objects to live accessed and develops a listing of candidate data access paths. These entry paths can consist of indexes, table scans, quite a few table join strategies and others. within the facts warehouse and great records environments there are constantly extra selections accessible. One of those is the existence of abstract tables (every now and then called materialized question tables) that involve pre-summarized or aggregated statistics, as a consequence enabling Db2 to wait away from re-aggregation processing. a further option is the starjoin entry direction, commonplace within the data warehouse, the state the order of desk joins is changed for performance factors.
The Optimizer then stories the candidate access paths and chooses the entry direction, "with the lowest cost." can permeate in this context potential a weighted summation of resource utilization together with CPU, I/O, reminiscence and different components. finally, the Optimizer takes the bottom permeate access route, stores it in reminiscence (and, optionally, within the Db2 directory) and starts access course execution.
massive facts and statistics warehouse operations now consist of application suites that allow the enterprise analyst to utilize a graphical interface to construct and maneuver a miniature statistics mannequin of the statistics they need to analyze. The programs then generate SQL statements in line with the users’ requests.
The issue for the DBA
so as to enact decent analytics to your numerous facts stores you need an excellent figuring out of the statistics necessities, an knowing of the analytical capabilities and algorithms accessible and a high-efficiency facts infrastructure. regrettably, the number and site of data sources is increasing (each in size and in geography), information sizes are becoming, and functions proceed to proliferate in quantity and complexity. How should IT managers aid this ambiance, peculiarly with probably the most experienced and age personnel nearing retirement?
take into account additionally that a magnificient portion of decreasing the full can permeate of possession of those systems is to find Db2 purposes to run quicker and extra successfully. This continually translates into the usage of fewer CPU cycles, doing fewer I/Os and transporting less records throughout the community. for the judgement that it is often involved to even establish which purposes might advantage from efficiency tuning, one fashion is to automate the detection and correction of tuning concerns. here's where computer studying and simulated intelligence will likewise live used to fabulous effect.Db2 12 for z/OS and synthetic Intelligence
Db2 version 12 on z/OS uses the machine studying facilities mentioned above to accumulate and store SQL question textual content and entry route details, in addition to genuine efficiency-linked historical counsel comparable to CPU time used, elapsed instances and outcomes set sizes. This offering, described as Db2 AI for z/OS, analyzes and stores the statistics in computing device discovering models, with the mannequin evaluation outcomes then being scored and made available to the Db2 Optimizer. The next time a scored SQL statement is encountered, the Optimizer can then utilize the model scoring statistics as enter to its entry direction alternative algorithm.
The sequel may still live a reduction in CPU consumption because the Optimizer uses model scoring enter to select more suitable entry paths. This then lowers CPU fees and speeds application response instances. a significant talents is that the usage of AI software does not require the DBA to fill facts science abilities or deep insights into query tuning methodologies. The Optimizer now chooses the most efficient access paths based mostly not best on SQL query syntax and information distribution information but on modelled and scored ancient performance.
This will likewise live specially crucial in case you store records in dissimilar areas. for example, many analytical queries towards great facts require concurrent access to inescapable information warehouse tables. These tables are commonly known as dimension tables, and that they involve the facts elements usually used to manage subsetting and aggregation. for example, in a retail atmosphere believe a desk known as StoreLocation that enumerates each store and its region code. Queries against reclaim revenue records can likewise need to mixture or summarize income by location; therefore, the StoreLocation desk might live used by using some great records queries. in this ambiance it is regular to remove the dimension tables and copy them always to the massive records application. within the IBM world this vicinity is the IBM Db2 Analytics Accelerator (IDAA).
Now reckon about SQL queries from both operational purposes, statistics warehouse users and magnificient facts enterprise analysts. From Db2's point of view, utter these queries are equal, and are forwarded to the Optimizer. besides the fact that children, in the case of operational queries and warehouse queries they should undoubtedly live directed to entry the StoreLocation desk in the warehouse. even so, the query from the enterprise analyst in opposition t massive statistics tables should still doubtless access the reproduction of the desk there. This consequences in a proliferations of skills entry paths, and more toil for the Optimizer. thankfully, Db2 AI for z/OS can provide the Optimizer the suggestions it needs to defer smart entry path selections.the artery it Works
The sequence of hobbies in Db2 AI for z/OS (See Reference 2) is frequently here:
There are likewise numerous user interfaces that supply the administrator visibility to the fame of the accrued SQL remark efficiency data and mannequin scoring.abstract
IBM's desktop learning for zOS (MLz) providing is being used to notable sequel in Db2 edition 12 to improve the efficiency of analytical queries in addition to operational queries and their associated functions. This requires administration attention, as you fill to investigate that your traffic is ready to consume these ML and AI conclusions. How will you measure the costs and advantages of the usage of laptop gaining lore of? Which IT aid group of workers fill to live tasked to reviewing the sequel of mannequin scoring, and maybe approving (or overriding) the consequences? How will you overview and justify the assumptions that the application makes about access direction selections?
In different words, how neatly enact you know your information, its distribution, its integrity and your existing and proposed entry paths? this may investigate the state the DBAs disburse their time in supporting analytics and operational application performance.
# # #
John Campbell, IBM Db2 exclusive EngineerFrom "IBM Db2 AI for z/OS: raise IBM Db2 software efficiency with desktop studying"https://www.worldofdb2.com/activities/ibm-db2-ai-for-z-os-raise-ibm-db2-software-efficiency-with-ma
Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
Feb 11, 2019Joyce Wells
Catalina, a issuer of consumer intelligence and personalised digital media, has adopted the IBM integrated Analytics system (IIAS) as a key pillar of its expanding analytics software to assist provide its consumers greater principal consumer insights.
Catalina companions with greater than 10,000 of the realm's main buyer packaged goods (CPG) manufacturers throughout each main category to convey significant value for valued clientele each time, any place. Its personalized digital media networks and client intelligence insights cause more constructive client engagements.
Having helped CPG manufacturers and agents rotate consumers into buyers with focused advertising solutions pushed by artery of records analytics in view that 1983, the enterprise is embarking on finished digital transformation to enable sellers and types to seamlessly connect with patrons regardless of their vicinity, live it in-shop, online or via their cellular instruments—anytime and any place.
As marketers and brands continue to build money into insights-driven marketing courses—to uphold them enact every diminutive thing from engage the perquisite purchasers and augment income raise and loyalty, to power each visits and "basket" sizes—data science is on the core of Catalina.
A key piece of its transformation is the IBM IIAS, a unified data outfit designed to supply users access to advanced statistics science capabilities, as well as the skill to toil with facts throughout hybrid multiclouds, which can live a mixture of public and personal cloud environments, and traditional on-premise IT. The device comes with quite a lot of information science equipment developed-in, and because it is according to the IBM common SQL engine, valued clientele can utilize the system to bound workloads from on-premise to cloud environments to start automating their companies with laptop gaining lore of.
moreover, IIAS comes with records virtualization, a developed-in capability that makes it viable for individuals to write down analytics queries that may access facts any state throughout the business. The system likewise facets IBM snappily information circulation, which makes it viable for companies to easily rush information between Hadoop clusters and the IIAS, extending and scaling their processing environments.
"Catalina sits squarely on the intersection of marketing, technology and analytics," mentioned Wes Chaar, chief information and analytics officer at Catalina. "As a portion of their broader facts science strategic initiative, Catalina is leveraging IBM's know-how to energy greater significant consequences from Catalina's analytics options, from personalization and customer preference modeling, to profits management and multi contact attribution—all to operate at most preempt stages. The solutions they are pile are principal for their customers because they optimize media and promotional choices for every single consumer, and enable seamless, integrated person experiences that power results."
For greater information, depart to www.catalina.com and www.ibm.com/analytics.
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LONDON--(BUSINESS WIRE)--Feb 14, 2019--A global data analytics and advisory firm, Quantzig, that delivers actionable analytics solutions to resolve involved traffic problems has announced the completion of their latest market basket analysis tryst for a food retailer. During the course of this engagement, the market basket analysis experts at Quantzig adopted a comprehensive approach to aid the client tackle their challenges and develop a highly scalable system.
This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20190214005612/en/
Market Basket Analysis tryst for a Food Retailer (Graphic: traffic Wire)
The retail industry is a highly fragmented and involved one. The rapid technological advancements and the ongoing digital shifts pose several problems for players in this sector. This is one of the main reasons why retailers are now looking at leveraging the utilize of analytic models and techniques to cater to the dynamic shifts in customer preferences. One such utensil used by them is – Market Basket Analysis. Market basket analysis empowers retailers to identify product groups that a customer is more likely to buy based on their previous purchase history.
However, only a fraction of the retail companies today defer decisions based on their customers' purchase behaviors. This turns out to live a major roadblock for an industry whose sole purpose is to fulfill the needs of its customers. By leveraging market basket analysis retailers can develop preempt strategies to compass their traffic goals.
The traffic Problem: The client is a leading organic food retailer with traffic units spread across Southern and Western Germany. The client' major challenge revolved around improving sales. The client witnessed a depreciating profit margin for three consecutive years due to which they wanted to leverage market basket analysis to develop novel cross-selling strategies and identify novel sales opportunities.
Our market basket analysis expertise will aid you gain pellucid insights into the purchase behaviors of various customer groups. find in finger with their experts to know how market basket analysis can aid you improve sales.
"As market leaders continue to capitalize on analytic techniques, a novel predictive model of market basket analysis is paving its artery across industries in an application to identify consecutive purchase pattern,"says a market basket analysis expert from Quantzig.
The Solution Offered: The market basket analysis experts at Quantzig adopted a comprehensive approach to tackle the client's challenges. As a result, the client was well positioned to identify profitable marketing campaigns and products with a lofty affinity. The talent to better umpire the overall effectiveness of these campaigns resulted in a 15% subside in the company's overall marketing budget. Also, by keeping track of key metrics like- tolerable disburse per basket and tolerable profit per basket the client was able to track product affinity for their top-selling product categories.
To accurately foretell consumer require is not an effortless task. Request a free proposal to know how their market basket analysis solutions can assist you in increasing sales.
Quantzig's market basket analysis solutions helped the client to:
Quantzig's market basket analysis solutions offered predictive insights on:
Quantzig is a global analytics and advisory solid with offices in the US, UK, Canada, China, and India. For more than 15 years, they fill assisted their clients across the globe with end-to-end data modeling capabilities to leverage analytics for prudent determination making. Today, their solid consists of 120+ clients, including 45 Fortune 500 companies. For more information on their tryst policies and pricing plans, visit:https://www.quantzig.com/request-for-proposal
View source version on businesswire.com:https://www.businesswire.com/news/home/20190214005612/en/
US: +1 630 538 7144
UK: +44 208 629 1455
INDUSTRY KEYWORD: RETAIL FOOD/BEVERAGE OTHER RETAIL
Copyright traffic Wire 2019.
PUB: 02/14/2019 10:48 AM/DISC: 02/14/2019 10:48 AM
In September 2018, IBM announced a novel product, IBM Db2 AI for z/OS. This simulated intelligence engine monitors data access patterns from executing SQL statements, uses machine learning algorithms to settle on optimal patterns and passes this information to the Db2 query optimizer for utilize by subsequent statements.Machine Learning on the IBM z Platform
In May of 2018, IBM announced version 1.2 of its Machine Learning for z/OS (MLz) product. This is a hybrid zServer and cloud software suite that ingests performance data, analyzes and builds models that depict the health status of various indicators, monitors them over time and provides real-time scoring capabilities.
Several features of this product offering are aimed at supporting a community of model developers and managers. For example:
This machine learning suite was initially aimed at zServer-based analytics applications. One of the first obvious choices was zSystem performance monitoring and tuning. System Management Facility (SMF) records that are automatically generated by the operating system provide the raw data for system resource consumption such as central processor usage, I/O processing, reminiscence paging and the like. IBM MLz can collect and store these data over time, and build and train models of system behavior, score those behaviors, identify patterns not readily foreseen by humans, develop key performance indicators (KPIs) and then feed the model results back into the system to affect system configuration changes that can improve performance.
The next step was to implement this suite to resolve Db2 performance data. One solution, called the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the machine learning technology to Db2 operational data to gain an understanding of Db2 subsystem health. It can dynamically build baselines for key performance indicators, provide a dashboard of these KPIs and give operational staff real-time insight into Db2 operations.
While general Db2 subsystem performance is an principal factor in overall application health and performance, IBM estimates that the DBA uphold staff spends 25% or more of its time, " ... fighting access path problems which cause performance degradation and service impact.". (See Reference 1).AI Comes to Db2
Consider the plight of modern DBAs in a Db2 environment. In today's IT world they must uphold one or more magnificient data applications, cloud application and database services, software installation and configuration, Db2 subsystem and application performance tuning, database definition and management, catastrophe recovery planning, and more. Query tuning has been in existence since the origins of the database, and DBAs are usually tasked with this as well.
The heart of query path analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the data, reviews the locations of the objects to live accessed and develops a list of candidate data access paths. These access paths can involve indexes, table scans, various table join methods and others. In the data warehouse and magnificient data environments there are usually additional choices available. One of these is the existence of summary tables (sometimes called materialized query tables) that contain pre-summarized or aggregated data, thus allowing Db2 to avoid re-aggregation processing. Another option is the starjoin access path, common in the data warehouse, where the order of table joins is changed for performance reasons.
The Optimizer then reviews the candidate access paths and chooses the access path, "with the lowest cost." Cost in this context means a weighted summation of resource usage including CPU, I/O, reminiscence and other resources. Finally, the Optimizer takes the lowest cost access path, stores it in reminiscence (and, optionally, in the Db2 Directory) and begins access path execution.
Big data and data warehouse operations now involve software suites that allow the traffic analyst to utilize a graphical interface to build and maneuver a miniature data model of the data they wish to analyze. The packages then generate SQL statements based on the users’ requests.
The Problem for the DBA
In order to enact friendly analytics on your multiple data stores you need a friendly understanding of the data requirements, an understanding of the analytical functions and algorithms available and a high-performance data infrastructure. Regrettably, the number and location of data sources is expanding (both in size and in geography), data sizes are growing, and applications continue to proliferate in number and complexity. How should IT managers uphold this environment, especially with the most experienced and age staff nearing retirement?
Understand likewise that a great portion of reducing the total cost of ownership of these systems is to find Db2 applications to run faster and more efficiently. This usually translates into using fewer CPU cycles, doing fewer I/Os and transporting less data across the network. Since it is often difficult to even identify which applications could capitalize from performance tuning, one approach is to automate the detection and correction of tuning issues. This is where machine learning and simulated intelligence can live used to Great effect.Db2 12 for z/OS and simulated Intelligence
Db2 version 12 on z/OS uses the machine learning facilities mentioned above to assemble and store SQL query text and access path details, as well as actual performance-related historical information such as CPU time used, elapsed times and result set sizes. This offering, described as Db2 AI for z/OS, analyzes and stores the data in machine learning models, with the model analysis results then being scored and made available to the Db2 Optimizer. The next time a scored SQL statement is encountered, the Optimizer can then utilize the model scoring data as input to its access path preference algorithm.
The result should live a reduction in CPU consumption as the Optimizer uses model scoring input to pick better access paths. This then lowers CPU costs and speeds application response times. A significant advantage is that using AI software does not require the DBA to fill data science skills or deep insights into query tuning methodologies. The Optimizer now chooses the best access paths based not only on SQL query syntax and data distribution statistics but on modelled and scored historical performance.
This can live particularly principal if you store data in multiple places. For example, many analytical queries against magnificient data require concurrent access to inescapable data warehouse tables. These tables are commonly called dimension tables, and they contain the data elements usually used to control subsetting and aggregation. For example, in a retail environment reckon a table called StoreLocation that enumerates every store and its location code. Queries against store sales data may wish to aggregate or summarize sales by Location; hence, the StoreLocation table will live used by some magnificient data queries. In this environment it is common to remove the dimension tables and copy them regularly to the magnificient data application. In the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).
Now reflect about SQL queries from both operational applications, data warehouse users and magnificient data traffic analysts. From Db2's perspective, utter these queries are equal, and are forwarded to the Optimizer. However, in the case of operational queries and warehouse queries they should most likely live directed to access the StoreLocation table in the warehouse. On the other hand, the query from the traffic analyst against magnificient data tables should probably access the copy of the table there. This results in a proliferations of potential access paths, and more toil for the Optimizer. Luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to defer smart access path selections.How It Works
The sequence of events in Db2 AI for z/OS (See Reference 2) is generally the following:
There are likewise various user interfaces that give the administrator visibility to the status of the accumulated SQL statement performance data and model scoring.Summary
IBM's machine learning for zOS (MLz) offering is being used to Great sequel in Db2 version 12 to improve the performance of analytical queries as well as operational queries and their associated applications. This requires management attention, as you must verify that your traffic is prepared to consume these ML and AI conclusions. How will you measure the costs and benefits of using machine learning? Which IT uphold staff must live tasked to reviewing the result of model scoring, and perhaps approving (or overriding) the results? How will you review and justify the assumptions that the software makes about access path choices?
In other words, how well enact you know your data, its distribution, its integrity and your current and proposed access paths? This will determine where the DBAs disburse their time in supporting analytics and operational application performance.
# # #
John Campbell, IBM Db2 Distinguished EngineerFrom "IBM Db2 AI for z/OS: Boost IBM Db2 application performance with machine learning"https://www.worldofdb2.com/events/ibm-db2-ai-for-z-os-boost-ibm-db2-application-performance-with-ma
Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
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