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How Red Hat Runs

This past week at Red Hat Summit 2019 (May 7 – 9 2019) has been exhausting. It’s not an overstatement to say that they run analysts ragged at their events, but that’s not why the conference made me tired. It was the sheer energy of the show, the kind of energy that keeps you running with no sleep for three days straight. That energy came from two sources – excitement and fear.

Two announcements, in particular, generated joy amongst the devoted Red Hat fans. The first was the announcement of Red Hat Enterprise Linux version 8, better known as RHEL8. RHEL is the granddaddy of all major Linux distributions for the data center. RHEL8, however, doesn’t seem all that old. As well as all the typical enhancements to the kernel and other parts of the distro, Red Hat has added two killer features to RHEL.

The first, the web console, is a real winner. It provides a secure browser-based system to manage all the features of Linux that one typically needs a command line on the server to perform. Now, using Telnet or SSH to log in to a remote box and do a few adjustments is no big deal when you have a small number of machines, physical or virtual, in a data center. When there are thousands of machines to care for, this is too cumbersome. With web console plus Red Hat Satellite, the same type of system maintenance is much more efficient. It even has a terminal built in if the command line is the only option. I predict that the web console will be an especially useful asset to new sysadmins who have yet to learn the intricacies of the Linux command line (or just don’t want to).

The new image builder is also going to be a big help for DevOps teams. Image builder uses a point and click interface to build images of software stacks, based on RHEL of course, that can be instantiated over and over. Creating consistent environments for developers and testing is a major pain for DevOps teams. The ability to quickly and easily create and deploy images will take away a major impediment to smooth DevOps pipelines.

The second announcement that gained a lot of attention was the impending GA of OpenShift 4 represents a major change in the Red Hat container platform. It incorporates all the container automation goodness that Red Hat acquired from CoreOS, especially the operator framework. Operators are key to automating container clusters, something that is desperately needed for large scale production clusters. While Kubernetes has added a lot of features to help with some automation tasks, such as autoscaling, that’s not nearly enough for managing clusters at hyperscale or across hybrid clouds. Operators are a step in that direction, especially as Red Hat makes it easier to use Operators.

Speaking of OpenShift, Satya Nadella, CEO of Microsoft appeared on the mainstage to help announce Azure Red Hat OpenShift. This would have been considered a mortal sin at pre-Nadella Microsoft and highlights the acceptance of Linux and open source at the Windows farm. Azure Red Hat OpenShift is an implementation of OpenShift as a native Azure service. This matters a lot to those serious about multi-cloud deployments. Software that is not a native service for a cloud service provider do not have the integrations for billing, management, and especially set up that native services do. That makes them second class citizens in the cloud ecosystem. Azure Red Hat OpenShift elevates the platform to first-class status in the Azure environment.

Now for the fear. Although Red Hat went to considerable lengths to address the “blue elephant in the room”, to the point of bringing Ginny Rometty, IBM CEO on stage, the unease around the acquisition by IBM was palpable amongst Red Hat customers. Many that I spoke to were clearly afraid that IBM would ruin Red Hat. Rometty, of course, insisted that was not the case, going so far as to say that she “didn’t spend $34B on Red Hat to destroy them.”

That was cold comfort to Red Hat partners and customers who have seen tech mergers start with the best intentions and end in disaster. Many attendees I spoke drew parallels with the Oracle acquisition of Sun. Sun was, in fact, the Red Hat of its time – innovative, nimble, and with fierce loyalists amongst the technical staff. While products created by Sun still exist today, especially Java and MySQL, the essence of Sun was ruined in the acquisition. That is a giant cloud hanging over the IBM-Red Hat deal. For all the advantages that this deal brings to both companies and the open source community, the potential for a train wreck exists and that is a source of angst in the Red Hat and open source world.

In 2019, Red Hat is looking good and may have a great future. Or it is on the brink of disaster. The path they will take now depends on IBM. If IBM leaves them alone, it may turn out to be an amazing deal and the capstone of Rometty and Jim Whitehurst’s careers. If IBM allows internal bureaucracy and politics to change the current plan for Red Hat, it will be Sun version 2. Otherwise, it is expected that Red Hat will continue to make open source enterprise-friendly and drive open source communities. That would be very nice indeed.

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Red Hat Hybrid Cloud Management Gets Financial with Cloud Cost Management

Key Stakeholders: CIO, CFO, Accounting Directors and Managers, Procurement Directors and Managers, Telecom Expense Personnel, IT Asset Management Personnel, Cloud Service Managers, Enterprise Architects

Why It Matters: As enterprise cloud infrastructure continues to grown 30-40% per year and containerization becomes a top enterprise concern, IT must have tools and a strategy for managing the cost of storage and compute associated with both hybrid cloud and container spend. With Cloud Cost Management, Red Hat provides an option for its considerable customer base.

Key Takeaways: Red Hat OpenShift customers seeking to managing the computing costs associated with hybrid cloud and containers should starting trialing Cloud Cost Management when it becomes available in 2019. Effective cost management strategies and tools should be considered table stakes for all enterprise-grade technologies.

Amalgam Insights is a top analyst firm in the analysis of IT subscription cost management, as can be seen in our:

In this context, Red Hat’s intended development of multi-cloud cost management integrated with CloudForms is an exciting announcement for the cloud market. This product, scheduled to come out in early 2019, will allow enterprises supporting multiple cloud vendors to support workload-specific cost management, which Amalgam Insights considers to be a significant advancement in the cloud cost management market.

And this product comes at a time when cloud infrastructure cost management has seen significant investment including VMware’s $500 million purchase of Boston-based CloudHealth Technologies, the 2017 $50 million “Series A” investment in CloudCheckr, investments in this area by leading Telecom and Technology Expense Management vendors such as Tangoe and Calero, and recent acquisitions and launches in this area from the likes of Apptio, BMC, Microsoft, HPE, and Nutanix.

However, the vast majority of these tools are currently lacking in the granular management of cloud workloads that can be tracked at a service level and then appropriately cross-charged to a project, department, or location. This capability will be increasingly important as application workloads become increasingly nuanced and revenue-driven accounting of IT becomes increasingly important. Amalgam Insights believes that, despite the significant activity in cloud cost management, that this market is just starting to reach a basic level of maturity as enterprises continue to increase their cloud infrastructure spend by 40% per year or more and start using multiple cloud vendors to deal with a variety of storage, computing, machine learning, application, service, integration, and hybrid infrastructure needs.

Red Hat Screenshot of Hybrid Cloud Cost Management

As can be seen from the screenshot, Red Hat’s intended Hybrid Cloud Cost Management offering reflects both modern design and support for both cloud spend and container spend. Given the enterprise demand for third-party and hybrid cloud cost management solutions, it makes sense to have an OpenShift-focused cost management solution.

Amalgam Insights has constantly promoted the importance of formalized technology cost management initiatives and their ability in reducing IT cost categories by 30% or more. We believe that Red Hat’s foray into Hybrid Cloud Cost Management has an opportunity to compete with a crowded field of competitors in managing multi-cloud and hybrid cloud spend. Despite the competitive landscape already in play, Red Hat’s focus on the OpenShift platform as a starting point for cost management will be valuable for understanding cloud spend at container, workload, and microservices levels that are currently poorly understood by IT executives.

My colleague Tom Petrocelli has noted that “I would expect to see more and more development shift to open source until it is the dominant way to develop large scale infrastructure software.” As this shift takes place, the need to manage the financial and operational accounting of these large-scale projects will become a significant IT challenge. Red Hat is demonstrating its awareness of this challenge and has created a solution that should be considered by enterprises that are embracing both Open Source and the cloud as the foundations for their future IT development.

Recommendations

Companies already using OpenShift should look forward to trialling Cloud Cost Management when it comes out in early 2019. This product provides an opportunity to effectively track the storage and compute costs of OpenShift workloads across all relevant infrastructure. As hybrid and multi-cloud management becomes increasingly common, IT organizations will need a centralized capability to track their increasingly complex usage associated with the OpenShift Container Platform.

Cloud Service Management and Technology Expense Management solutions focused on tracking Infrastructure as a Service spend should consider integration with Red Hat’s Cloud Cost Management solution. Rather than rebuild the wheel, these vendors can take advantage of the work already done by RedHat to track container spend.

And for Red Hat, Amalgam Insights provides the suggestion that Cloud Cost Management become more integrated with CloudForms over time. The most effective expense management practices for complex IT spend categories always include a combination of contracts, inventory, invoices, usage, service orders, service commitments, vendor comparisons, and technology category comparisons. To gain this holistic view that optmizes infrastructure expenses, cloud procurement and expense specialists will increasingly demand this complete view across the entire lifecycle of services.

Although this Cloud Cost Management capability has room to grow, Amalgam Insights expects this tool to quickly become a mainstay, either as a standalone tool or as integrated inputs within an enterprise’s technology expense or cloud service management solution. As with all things Red Hat, Amalgam Insights expects rapid initial adoption within the Red Hat community in 2019-2020 which will drive down enterprise infrastructure total cost of ownership and increase visibility for enterprise architects, financial controllers, and accounting managers responsible for responsible IT cost management.

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Observations on the Future of Red Hat from Red Hat Analyst Day

On November 8th, 2018, Amalgam Insights analysts Tom Petrocelli and Hyoun Park attended the Red Hat Analyst Day in Boston, MA. We had the opportunity to visit Red Hat’s Boston office in the rapidly-growing Innovation District, which has become a key tech center for enterprise technology companies. In attending this event, my goal was to learn more about the Red Hat culture that is being acquired as well as to see how Red Hat was taking on the challenges of multi-cloud management.

Throughout Red Hat’s presentations throughout the day, there was a constant theme of effective cross-selling, growing deal sizes including a record 73 deals of over $1 million in the last quarter, over 600 accounts with over $1 million in business in the last year, and increased wallet share year-over-year for top clients with 24 out of 25 of the largest clients increasing spend by an average of 15%. The current health of Red Hat is undeniable, regardless of the foibles of the public market. And the consistency of Red Hat’s focus on Open Source was undeniable across infrastructure, integration, application development, IT automation, IT optimization, and partner solutions, which demonstrated how synchronized and focused the entire Red Hat executive team presenters were, including Continue reading Observations on the Future of Red Hat from Red Hat Analyst Day

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Is IBM’s Acquisition of Red Hat the Biggest Acquihire of All Time?

Estimated Reading Time: 11 minutes

Internally, Amalgam Insights has been discussing why IBM chose to acquire Red Hat for $34 billion dollars fairly intensely. Our key questions included:

  • Why would IBM purchase Red Hat when they’re already partners?
  • Why purchase Red Hat when the code is Open Source?
  • Why did IBM offer a whopping $34 billion, $20 billion more than IBM currently has on hand?

As a starting point, we posit that IBM’s biggest challenge is not an inability to understand its business challenges, but a fundamental consulting mindset that starts with the top on down. By this, we mean that IBM is great at identifying and finding solutions on a project-specific basis. For instance, SoftLayer, Weather Company, Bluewolf, and Promontory Financial are all relatively recent acquisitions that made sense and were mostly applauded at the time. But even as IBM makes smart investments, IBM has either forgotten or not learned the modern rules for how to launch, develop, and maintain software businesses. At a time when software is eating everything, this is a fundamental problem that IBM needs to solve.

The real question for IBM is whether IBM can manage itself as a modern software company.

Continue reading Is IBM’s Acquisition of Red Hat the Biggest Acquihire of All Time?

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Tom Petrocelli Provides Context for IBM’s Acquisition of Red Hat

Tom Petrocelli, Amalgam Insights Research Fellow

In light of yesterday’s announcement that IBM is planning to acquire Red Hat for $34 billion, we’d like to share with you some of our recent coverage and mentions of Red Hat to provide context for this gargantuan acquisition.

To learn more about the state of Enterprise Free Open Source Software and the state of DevOps, make sure you continue to follow Tom Petrocelli on this website and his Twitter account.

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Market Milestone: Red Hat Acquires CoreOS Changing the Container Landscape

Red Hat Acquires CoreOS

We have just published a new document from Tom Petrocelli analyzing Red Hat’s $250 million acquisition of CoreOS and why it matters for DevOps and Systems Architecture managers.

This report is recommended for CIOs, System Architects, IT Managers, System Administrators, and Operations Managers who are evaluating CoreOS and Red Hat as container solutions to support their private and hybrid cloud solutions. In this document, Tom provides both the upside and concerns that your organization needs to consider in evaluating CoreOS.

This document includes:
A summary of Red Hat’s Acquisition of CoreOS
Why It Matters
Top Takeaways
Contextualizing CoreOS within Red Hat’s private and hybrid cloud portfolio
Alternatives to Red Hat CoreOS
Positive and negative aspects fcr current Red Hat and CoreOS customers

To download this report, please go to our Research section.

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February 25: From BI to AI (Aporia, cnvrg.io, Decodable, Equalum, Grata, Hasura, Mage, nRoad, Redpanda, SeMI Technologies, thatDot)

If you would like your announcement to be included in Amalgam Insights’ weekly data and analytics roundups, please email lynne@amalgaminsights.com.

Funding

Aporia Raises $25 Million to Grow its Machine Learning Observability Platform

On February 22, Aporia, a machine learning observability platform, announced that it had raised $25M in a Series A funding round. Tiger Global Management led the round, with participation from existing investors TLV Partners and Vertex Ventures, and new investors Samsung NEXT and Tal Ventures. The funding will go towards hiring and global expansion.

Decodable Raises $20M Series A Funding Round For Its Realtime Data Platform

Decodable, a realtime data engineering platform, raised a $20M A round this week. Bain Capital Ventures and Venrock led the funding round, with additional participation from individual investors including former US Chief Data Scientist DJ Patil, DataDog CEO Olivier Pomel, Cockroach Labs CEO Spencer Kimball, and Redis CRO and President Jason Forget. Decodable also debuted the Decodable Real-Time Data Platform, which supports functions like event-driven micro services, data mesh deployment, realtime data integration and ML/AI pipelines, and data governance and regulatory compliance.

Grata Closes $25 Million A Round For Its Data Intelligence Engine

Grata, a data intelligence engine, announced February 22 that it had raised $25M in a Series A funding round led by Craft Ventures. Existing investors Accomplice, Bling, and Touchdown Ventures also participated, along with new investors Altai Ventures, Eigen Ventures, and Teamworthy Ventures. The funding will go towards further product development. Grata uses proprietary machine learning and natural language processing models to process unstructured data from websites into insights on private companies, made available in a search-based interface.

GraphQL Engine Provider Hasura Announces $100M in Series C Funding

Hasura, a GraphQL engine provider, has raised a $100M Series C funding round. Greenoaks led the round, with participation from existing investors Lightspeed Venture Partners, Nexus Venture Partners, and Vertex Ventures. Hasura will use the funding for R+D and global expansion of their go-to-market strategy.

Streaming Data Platform Redpanda Raises $50M Series B

Redpanda, a data streaming platform, announced February 23 that they had raised a $50M Series B Funding Round led by GV. Haystack VC also participated, as did Lightspeed Venture Partners (busy week for Lightspeed, also participating in the Hasura C round!). The funding will go towards hiring for their engineering and go-to-market teams.

SeMI Technologies Raises $16M Series A Round For AI-Based Search Database

SeMI Technologies, providers of open source vector search engine Weaviate, announced a $16M Series A funding round February 22. Cortical Ventures and New Enterprise Associates co-led the round. The funding will go towards hiring, community development, and product improvement including increasing potential use cases and creating and improving the ML models Weaviate is based on.

Launches and Updates

cnvrg.io Announces AI Blueprints, Customizable ML Pipelines

On February 22, Cnvrg.io, an AI/ML platform provider, debuted cnvrg.io AI Blueprints. AI Blueprints is a curated open-source library of machine learning model APIs and customizable pipelines, allowing companies to quickly piece together models to analyze their data. Availability of cnvrg.io AI Blueprints is planned for the first half of 2022.

Equalum Releases v3.0 of their Continuous Data Integration Platform 3.0

Equalum released version 3.0 of their “continuous” data integration platform this week. New features include expanded support for cloud targets across AWS, Azure, and GCP; enhanced binary parsers for Oracle logs and SQL replication; improvements to replication groups to allow for extensive data migrations and cross-platform data warehousing; and no-code data integration capabilities for streaming ETL and ELT data, as well as batch ETL and change data capture.

Mage Debuts Low Code AI Ranking Model Tool for Product Developers

On February 24, Mage announced the general availability of its low code AI tool. Mage is targeted towards product developers needing to build AI ranking models to increase user engagement and retention.

nRoad Launches Unstructured Data Processing Platform Convus

nRoad, an NLP startup, introduced its Convus platform February 23. Convus provides machine learning models for financial services to extract insights from unstructured data. This allows FinTech businesses to avoid manual data extraction and entry while incorporating information in documents into business processes.

thatDot Releases Complex Event Processing Engine Quine Streaming Graph

thatDot, complex event processing software providers, debuted Quine Streaming Graph, an open source event processing engine based on streaming graph data. Developers can use Quine to quickly build complex event processing workflows to apply to streaming graph data using “recipes.” Recipes currently available include blockchain realtime tag propagation, CDN cache efficiency analysis, and Apache server log observability.

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5 Predictions That Will Transform Corporate Training in 2018

At Amalgam Insights, we have been focused on the key 2018 trends that will change our ability to manage technology at scale. Tom Petrocelli provided his key Developer Operations and enterprise collaboration predictions for 2018 in mid-December. To continue that trend, Todd Maddox provides 5 key predictions that will shape enterprise learning in 2018 as markets reach new heights, corporate training embraces new scientific principles, and retention replaces compliance as a key training driver.

  1. VR/AR Enterprise Application Budget to Surpass $1 Billion in 2018
  2. eLearning (Computer-Based Training) Market to Approach $180 billion in 2018
  3. Commercial Training Sector to Embrace Neuroscience of Optimized Learning
  4. Continued Exponential Growth of Artificial Intelligence (AI) as a Driving Force Behind the User Interface (UI)
  5. Training for Retention: The Rule, Not the Exception in 2018

Continue reading 5 Predictions That Will Transform Corporate Training in 2018

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What To Watch Out For As GPT Leads a New Technological Revolution

2022 was a banner year for artificial intelligence technologies that reached the mainstream. After years of being frustrated with the likes of Alexa, Cortana, and Siri and the inability to understand the value of machine learning other than as a vague backend technology for the likes of Facebook and Google, 2022 brought us AI-based tools that was understandable at a consumer level. From our perspective, the most meaningful of these were two products created by OpenAI: DALL-E and ChatGPT, which expanded the concept of consumer AI from a simple search or networking capability to a more comprehensive and creative approach for translating sentiments and thoughts into outputs.

DALL-E (and its successor DALL-E 2) is a system that can create visual images based on text descriptions. The models behind DALL-E look at relationships between existing images and the text metadata that has been used to describe those images. Based on these titles and descriptions, DALL-E uses diffusion models to start with random pixels that lead to generated images based on these descriptions. This area of research is by no means unique to OpenAI, but it is novel to open up a creative tool such as DALL-E to the public. Although the outputs are often both interesting and surprisingly different from what one might have imagined, they are not without their issues. For instance, the discussion around the legal ownership of DALL-E created graphics is not clear, since Open AI claims to own the images used, but the images themselves are often based on other copyrighted images. One can imagine that, over time, an artistic sampling model may start to appear similar to the music industry where licensing contracts are used to manage the usage of copyrighted material. But this will require greater visibility regarding the lineage of AI-based content creation and the data used to support graphic diffusion. Until this significant legal question is solved, Amalgam Insights believes that the commercial usage of DALL-E will be challenging to manage. This is somewhat reminiscent of the challenges that Napster faced at the end of the 20th century as a technology that both transformed the music industry and had to deal with the challenges of a new digital frontier.

But the technology that has taken over the zeitgeist of technology users is ChatGPT and related use cases associated with the GPT (Generative Pre-Trained Transformer) autoregressive language model trained on 500 billion words across the web, Wikipedia, and books. And it has become the favorite plaything of many a technologist. What makes ChatGPT attractive is its ability to take requests from users asking questions with some level of subject matter specificity or formatting and to create responses in real-time. Here are a couple of examples from both a subject matter and creative perspective.

Example 1: Please provide a blueprint for bootstrapping a software startup.

This is a bit generic and lacks some important details on how to find funding or sell the product, but it is in line with what one might expect to see in a standard web article regarding how to build a software product. The ending of this answer shows how the autogenerative text is likely referring to prior web-based content built for search engine optimization and seeking to provide a polite conclusion based on junior high school lessons in writing the standard five-paragraph essay rather than a meaningful conclusion that provides insight. In short, it is basically a status quo average article with helpful information that should not be overlooked, but is not either comprehensive or particularly insightful for anyone who has ever actually started a business.

A second example of ChatGPT is in providing creative structural formats for relatively obscure topics. As you know, I’m an expert in technology expense management with over two decades of experience and one of the big issues I see is, of course, the lack of poetry associated with this amazing topic. So, again, let’s go to ChatGPT.

Example 2: Write a sonnet on the importance of reducing telecom expenses

As a poem, this is pretty good for something written in two seconds. But it’s not a sonnet, as sonnets are 14 lines, written in iambic pentameter (10 syllable lines split int 5 iambs, or a unstressed syllable followed by a stressed syllable) and split into three sections of four lines followed by a two-line section with a rhyme scheme of ABAB, CDCD, EFEF, GG. So, there’s a lot missing there.

So, based on these examples, how should ChatGPT be used? First, let’s look at what this content reflects. The content here represents the average web and text content that is associated with the topic. With 500 billion words in the GPT-3 corpus, there is a lot of context to show what should come next for a wide variety of topics. Initial concerns of GPT-3 have started with the challenges of answering questions for extremely specific topics that are outside of its training data. But let’s consider a topic I worked on in some detail back in my college days while using appropriate academic language in asking a version of Gayatri Spivak’s famous (in academic circles) question “Can the subaltern speak?”

Example 3: Is the subaltern allowed to fully articulate a semiotic voice?

Considering that the language and topic here is fairly specialized, the introductory assumptions are descriptive but not incisive. The answer struggles with the “semiotic voice” aspect of the question in discussing the ability and agency to use symbols from a cultural and societal perspective. Again, the text provides a feeling of context that is necessary, but not sufficient, to answer the question. The focus here is on providing a short summary that provides an introduction to the issue before taking the easy way out telling us what is “important to recognize” without really taking a stand. And, again, the conclusion sounds like something out of an antiseptic human resources manual in asking for the reader to consider “different experiences and abilities” rather than the actual question regarding the ability to use symbols, signs, and assumptions. This is probably enough of an analysis at a superficial level as the goal here isn’t to deeply explore postmodern semiotic theory but to test ChatGPT’s response in a specialized topic.

Based on these three examples, one should be careful in counting on ChatGPT to provide a comprehensive or definitive answer to a question. Realistically, we can expect ChatGPT will provide representative content for a topic based on what is on the web. The completeness and accuracy of a ChatGPT topic is going to be dependent on how often the topic has been covered online. The more complete an answer is, the more likely it is that this topic has already been covered in detail.

ChatGPT will provide a starting point for a topic and typically provide information that should be included to introduce the topic. Interestingly, this means that ChatGPT is significantly influenced by the preferences that have built online web text over the past decade of content explosion. The quality of ChatGPT outputs seems to be most impressive to those who treat writing as a factual exercise or content creation channel while those who look at writing as a channel to explore ideas may find it lacking for now based on its generalized model.

From a topical perspective, ChatGPT will probably have some basic context for whatever text is used in a query. It would be interesting to see the GPT-3 model augmented with specific subject matter texts that could prioritize up-to-date research, coding, policy, financial analysis, or other timely new content either as a product or training capability.

In addition, don’t expect ChatGPT to provide strong recommendations or guidance. The auto-completion that ChatGPT does is designed to show how everyone else has followed up on this topic. And, in general, people do not tend to take strong stances on web-based content or introductory articles.

Fundamentally, ChatGPT will do two things. First, it will make mediocre content ubiquitous. There is no need to hire people to write an “average” post for your website anymore as ChatGPT and other technologies either designed to compete with or augment it will be able to do this easily. If your skillset is to write grammatically sound articles with little to no subject matter experience or practical guidance, that skill is now obsolete as status quo and often-repeated content can now be created on command. This also means that there is a huge opportunity to combine ChatGPT with common queries and use cases to create new content on demand. However, in doing so, users will have to be very careful not to plagiarize content unknowingly. This is an area where, just like with DALL-E, OpenAI will have to work on figuring out data lineage, trademark and copyright infringement, and appropriation of credit to support commercial use cases.  

Second, this tool now provides a much stronger starting point for writers seeking to say something new or different. If your point of view is something that ChatGPT can provide in two seconds, it is neither interesting or new. To stand out, you need to provide greater insight, better perspective, or stronger directional guidance. This is an opportunity to improve your skills or to determine where your professional skills lie. ChatGPT still struggles with timely analysis, directional guidance, practical recommendations beyond surface-level perspectives, and combining mathematical and textual analysis (i.e. doing word problems or math-related case studies or code review) so there is still an immense amount of opportunity for people to write better.

Ultimately, ChatGPT is a reflection of the history of written text creation, both analog and digital. Like all AI, ChatGPT provides a view of how questions were answered in the past and provides an aggregate composite based on auto-completion. For topics with a basic consensus, such as how to build a product, this tool will be an incredible time saver. For topics that may have multiple conflicting opinions, ChatGPT will try to play either both sides or all sides in a neutral manner. And for niche topics, ChatGPT will try to fake an answer at what is approximately a high school student’s understanding of the topic. Amalgam Insights recommends that all knowledge workers experiment with ChatGPT in their realm of expertise as this tool and the market of products that will be built based on the autogenerated text will play an important role in supporting the next generation of tech.

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May 13: From BI to AI (AWS, Databricks, DataRobot, Dremio, Google, Hugging Face, Mindtech, Predibase, Privacera, Pyramid Analytics, Snowflake, Starburst, ThoughtSpot)

If you would like your announcement to be included in Amalgam Insights’ weekly data and analytics roundups, please email lynne@amalgaminsights.com.

Funding

Hugging Face Raises $100M C Round

Machine learning platform Hugging Face announced May 9 that they had raised $100M in Series C funding. Lux Capital led the round, with participation from Addition, AIX Ventures, a_capital, Betaworks, Coatue, Sequoia, SV Angel, and individual angel investors. Hugging Face will use the funding on R+D, product development, and education.

Predibase Announces $16.25M in Seed and A Round Funding, Emerges From Stealth

Predibase, a low-code machine learning platform, came out of stealth this week and announced $16.25M in Series A and seed funding. Greylock led both rounds, with participation from The Factory and angel investors. The funding will go towards hiring additional engineering and ML talent, as well as the go-to-market strategy and bringing Predibase into general availability.

Pyramid Analytics Closes $120M E Round

Pyramid Analytics, a decision intelligence and augmented analytics platform, announced on May 9 that they had closed $120M in Series E financing. HIG Growth Partners led the round, with participation from existing investors Clal Insurance Enterprises Holdings, General Oriental Investments, and Kingfisher Capital, and new investors JVP, Maor Investments, Sequoia Capital, and Viola Growth. Pyramid will use the money on product development, continued R+D, expanding partnerships globally, and hiring to support all of these efforts.

Launches and Updates

Databricks Strengthens AWS Partnership, Announces PAYGO Lakehouse Offering

On May 10, Databricks announced a pay-as-you-go lakehouse offering on AWS, available now. Customers will be able to launch a lakehouse from the AWS Marketplace whether or not they’ve used Databricks before; they can set up a Databricks account from within AWS, and even consolidate their Databricks usage bills into their AWS billing.

Google Serves Up LaMDA 2 Demos in its AI Test Kitchen

At Google I/O, Google unveiled its new Language Model for Dialog Applications (LaMDA) 2 conversational AI model, along with AI Test Kitchen, an app to demonstrate use cases for LaMDA 2. LaMDA is a generative text model, aiming to produce relevant textual responses based on patterns it recognizes from linguistic input. While no date has been announced for general availability, Google plans to open up LaMDA access to small groups of people.

Mindtech Chameleon Now Generates Diverse “Actors” to Address Bias

Mindtech Global, a synthetic data creation platform, has announced updates to its Chameleon platform. Chameleon 22.1 lets users automatically generate millions of “actors” in virtual worlds, creating privacy-compliant synthetic visual data for training computer vision systems. To address known bias issues, the Chameleon actors now have a range of configuration options, including height, build, age, skin tone, and clothing and hairstyle options.

Privacera Announces Release of Platform 6.3 and PrivaceraCloud 4.3

Privacera, a data access governance company, announced the release of the latest version of Privacera Platform 6.3 and PrivaceraCloud 4.3. New features include extending Attribute Based Access Control across all supported data and analytical sources, supplementing existing role-based and tag-based access control mechanisms; along with enhanced support for Google BigQuery, Starburst Enterprise, Databricks, and Snowflake.

ThoughtSpot Expands the Modern Analytics Cloud to Help Companies Dominate the Decade of Data

At their Beyond 2022 event this week, ThoughtSpot announced numerous new capabilities for their cloud analytics platform. Key updates include integrations and connectors for Amazon Redshift Serverless, Snowflake Data Marketplace, Databricks Partner Connect, Dremio, and Starburst Galaxy; templates, code samples, and ThoughtSpot Blocks to accelerate the development process; and automation capabilities to trigger actions based on analytics.

Events

DataRobot AIX 22 Celebrates AI Innovation, June 7-8, 2022

On June 7 and 8, DataRobot will host DataRobot AIX 22, a free virtual event to explore innovation in AI, analytics, and data science. Featured speakers include DataRobot executives Dan Wright, Debanjan Saha, Nenshad Bardoliwalla, and Michael Schmidt. Register for the event at the DataRobot AIX 22 website.