Kisaco Leadership Chart on Enterprise ML Lifecycle Solutions 2020-21 | Kisaco Research

About the Author

Author:

Michael Azoff

Chief Analyst
Kisaco Research

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 when KR Analysis and Michael Azoff introduction © Kisaco Research. All rights reserved. Unauthorized reproduction prohibited. 3 backpropagation was invented 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.

  • Motivation

    For enterprises looking to build and deploy ML applications there are multiple hurdles to overcome: team skills, team collaboration, maturity of deployment process, industrialization of ML application development lifecycle, monitoring production and managing change and updates. While many of these steps and concepts are familiar to software engineers they are novel to data science teams and line of business (LOB) teams, and the parallel between ML application development and software application development only goes so far and the former has quite distinct requirements. To address these needs the AI market has seen the rise of ML lifecycle solutions and we compare six players with solutions focused on the enterprise in this space, as well as analyze the components of the ML lifecycle.

  • What you will learn

    • The makeup of the machine learning (ML) software solution space, how the market segments itself into available products.
    • The makeup ML lifecycle solutions: what these solutions encompass and provide.
    • The Kisaco Leadership Chart (KLC) on the participating vendors: positions the solutions in the chart. We also provide an overview of the solution characteristics in terms of aggregated performance scores in a heapmap.
    • Deep profiles of six of our participating vendors competing in ML lifecycle solutions, including strengths and weaknesses.
    • Analysis of the roles required to use ML lifecycle solutions and the levels of expertise required.
  • Contents

    Kisaco Research View. 2

    Motivation. 2

    Note: How we define AI 2

    Key findings. 2

    Solution Analysis: ML lifecycle. 3

    Essential concepts for managing the ML lifecycle. 3

    The ML disciplines. 3

    The ML team roles. 5

    The ML lifecycle and market solutions. 5

    The broader competitive landscape. 8

    Solution analysis: vendor comparisons. 8

    Kisaco Leadership Chart on ML Lifecycle Solutions 2020-21. 8

    ML lifecycle solution vendor comparisons. 8

    The KLC chart for ML lifecycle solutions. 9

    Vendor analysis. 11

    Algorithmia, Kisaco evaluation: Leader 11

    Kisaco Assessment 13

    DataKitchen, Kisaco evaluation: Emerging Player 13

    Kisaco Assessment 15

    dotData, Kisaco evaluation: Emerging Player 16

    Kisaco Assessment 18

    Iguazio, Kisaco evaluation: Leader 19

    Kisaco Assessment 21

    Spell, Kisaco evaluation: Emerging Player 22

    Kisaco Assessment 24

    Splice Machine, Kisaco evaluation: Innovator 24

    Kisaco Assessment 26

    Appendix. 27

    Vendor solution selection. 27

    Inclusion criteria. 27

    Exclusion criteria. 28

    Methodology. 28

    Definition of the KLC. 28

    Kisaco Research ratings. 28

    Further reading. 29

    Acknowledgements. 29

    Author 29

    Kisaco Research Analysis Network. 29

    Copyright notice and disclaimer 29

  • Figures

    Figure 1: The space of ML spans AI, computing, and data science.

    Figure 2: ML application code is a small part of the infrastructure required to build and run ML.

    Figure 3: ML lifecycle in the ML tool eco-system (covering DataOps, AutoML, MLOps).

    Figure 4: Components of a ML lifecycle solution.

    Figure 5: Heat map analysis of participating vendor solution features.

    Figure 6: Kisaco Leadership Chart on ML lifecycle solutions 2020-21.

    Figure 7: Kisaco Leadership Chart on ML lifecycle solutions 2020-21: ranking of vendors.

    Figure 8: Algorithmia Enterprise architecture.

    Figure 9: DataKitchen DataOps Platform: orchestrate data to customer value.

    Figure 10: Feature engineering is a key set of activities for ML development.

    Figure 11: Three dotData deployment methods.

    Figure 12: Iguazio Data Science as a Platform architecture.

    Figure 13: The Spell MLOps solution.

    Figure 14: Splice Machine architecture: product offerings in blue.

  • About the Author

    Author:

    Michael Azoff

    Chief Analyst
    Kisaco Research

    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 when KR Analysis and Michael Azoff introduction © Kisaco Research. All rights reserved. Unauthorized reproduction prohibited. 3 backpropagation was invented 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.

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