Michael Azoff
With over 17 years analyst experience, most recently at Ovum/ Informa, Michael Azoff joined Kisaco Research, the company behind the AI Hardware and Edge AI Summit series, in 2020 as Chief Analyst.
Eitan Michael Azoff, PhD, MSc, BEng.
HQ’d in Kisaco Research’s London office, Michael's current focus is launching Kisaco Research vendor product comparison reports with the new Kisaco Leadership Chart (KLC) analyst chart. The first KLC is also the first analyst chart in the AI chip industry, with 16 vendors having participated in the research.
In his career Michael worked at Rutherford Appleton Laboratory building simulators for electron and hole transport in semiconductors for UK national and European community research projects and published papers in learned journals. He then turned to building neural networks and created a startup selling his Prognostica Microsoft Excel add-in for time series forecasting, and wrote a book on the topic for publisher John Wiley & Sons in 1994.
Since 2003 Michael has worked as an IT industry analyst covering software engineering topics, from agile and DevOps, to application lifecycle management and cloud native computing. He started covering machine learning when deep learning emerged as the most recent wave of interest in AI and left his position as Distinguished Analyst at Ovum/Informa to join Kisaco Research and help build an analyst capability within the company.
My analyst coverage areas at KR Analysis
My first research project at KR was to create the first analyst comparison chart for AI chips. We invited AI chip producers to participate and were fortunate to have 16 vendors participate from across the globe: USA, UK, France, and China, and a mix of established players (Nvidia, Imagination, Intel, and Xilinx, to startups.
Our analysis showed that the market naturally fell into three areas of hot activity:
▪ Data centers and high-performance computing environments (HPC): here large boxes are installed and the aim is to achieve maximum performance for training and inferencing AI systems. The buyers are cloud hyperscalars, national research labs and agencies, and some large enterprises with big investments in AI.
▪ Small edge: the opposite end of the spectrum, building the smallest useful chip possible to sell as cheap as possible and embed in edge devices. AI is inferencing here.
▪ Automotive: an active industry in AI but highly regulated creating hurdles and technology adoption cadences that can be challenging for suppliers. AI is mainly inferencing here (for systems installed in vehicles).
We produced four Kisaco Leadership Charts out of this research.
We are also researching the machine learning (ML) software tools space, and our first report here is ML Lifecycle Solutions. The biggest challenge for enterprises is taking the research AI systems developed by their data scientist and deploying these into production at scale. Using a host of open source tools to achieve this is possible but time consuming to build and maintain, as well as prone to breakdown. This is why the ML lifecycle solution space exists.
Finally, in our first batch of KR Analysis reports we produced the KLC on engineering application lifecycle management (ALM) solutions. While ALM has been in existence as a distinct practice since KR Analysis and Michael Azoff introduction © Kisaco Research. All rights reserved. Unauthorized reproduction prohibited. 4 around 2003, it continues to evolve. We found the engineering and highly regulated industries relying on engineering and compliance oriented ALM to help manage risk and complexity.
Daniella Dos Santos
Daniella obtained a BSc (hons) in Molecular Genetics from Kings College London in 2007, before going to study veterinary medicine at the RVC, qualifying in 2012. Since then she has been in first opinion small animal and exotic animal practice and is currently working towards her CertAVP zoological medicine. She became a member of the BVA Ethics and Welfare Advisory Panel in 2015, and became Chair in 2017. She also became a Trustee of the Animal Welfare Foundation in 2016.
Chris Belnap
Ricky Ghoshal
Anton Xavier
Victoria Johnson
Paul Koffman
Georgia Wraight
Zaid Kahn
Zaid is currently a VP in Microsoft’s Silicon, Cloud Hardware, and Infrastructure Engineering organization where he leads systems engineering and hardware development for Azure including AI systems and infrastructure. Zaid is part of the technical leadership team across Microsoft that sets AI hardware strategy for training and inference. Zaid's teams are also responsible for software and hardware engineering efforts developing specialized compute systems, FPGA network products and ASIC hardware accelerators.
Prior to Microsoft Zaid was head of infrastructure at LinkedIn where he was responsible for all aspects of architecture and engineering for Datacenters, Networking, Compute, Storage and Hardware. Zaid also led several software development teams focusing on building and managing infrastructure as code. This included zero touch provisioning, software-defined networking, network operating systems (SONiC, OpenSwitch), self-healing networks, backbone controller, software defined storage and distributed host-based firewalls. The network teams Zaid led built the global network for LinkedIn, including POP's, peering for edge services, IPv6 implementation, DWDM infrastructure and datacenter network fabric. The hardware and datacenter engineering teams Zaid led were responsible for water cooling to the racks, optical fiber infrastructure and open hardware development which was contributed to the Open Compute Project Foundation (OCP).
Zaid holds several patents in networking and is a sought-after keynote speaker at top tier conferences and events. Zaid is currently the chairperson for the OCP Foundation Board. He is also currently on the EECS External Advisory Board (EAB) at UC Berkeley and a board member of Internet Ecosystem Innovation Committee (IEIC), a global internet think tank promoting internet diversity. Zaid has a Bachelor of Science in Computer Science and Physics from the University of the South Pacific.
